ChatGPT answers, unedited

If you’re managing hundreds of client campaigns, I’d think of this as a marketing data + forecasting stack, not just a reporting tool.

My shortlist

For your particular use case

I'd lean toward Supermetrics + a warehouse + a forecasting layer.

The important distinction is that consolidating spend/performance isn't the same as forecasting. You want a system that gives you a clean dataset like:

Client → Account → Channel → Campaign → Date → Spend → Impressions → Clicks → Leads → Revenue → CPA/ROAS

Then your forecasting model can calculate things like:

• Actual vs. planned spend
• Month-end spend forecast
• Expected conversions/revenue
• CPA/ROAS forecast
• Client-level budget remaining
• Channel-level pacing
• Variance from forecast
• Rolling 7/14/30-day performance
• Forecast confidence / scenario ranges

Supermetrics explicitly supports multi-client workspaces, cross-channel data, and spend-vs-plan pacing, which makes it a natural fit for the aggregation layer. Supermetrics

For hundreds of clients, though, I wouldn't make Excel/Sheets the ultimate system of record. I'd put the normalized data into BigQuery or Snowflake, then use Power BI, Looker, Tableau, or a custom application for the forecasting/portfolio views.

The architecture I'd consider

Ad platforms
→ Google Ads / Meta / LinkedIn / TikTok / etc.
↓
Data connector
→ Supermetrics or Windsor.ai
↓
Warehouse
→ BigQuery / Snowflake
↓
Normalization
→ standardized campaign, client, channel, spend and KPI schema
↓
Forecasting
→ SQL + Python/forecasting model or an ML platform
↓
Dashboard
→ portfolio → client → campaign drill-down

That gives you something much more powerful than a conventional agency dashboard: a portfolio-level forecasting system where you can see, for example, that 73 clients are on pace, 18 are likely to underspend, and 11 are projected to exceed their monthly budget—then drill directly into the campaigns causing the variance.

If you tell me (1) roughly how many clients/accounts, (2) which ad platforms, (3) whether you forecast spend, leads, revenue, or all three, and (4) whether you already use BigQuery/Power BI/Looker, I can recommend the specific stack and data model I'd use.

If you’re managing hundreds of client campaigns and the goal is to consolidate spend + performance + forecasting, I’d look at this as a marketing data infrastructure problem rather than just a reporting/dashboard problem.

Shortlist

• Supermetrics — strong fit if you want to centralize hundreds of client accounts, normalize channel data, and feed it into forecasting models/BI. It explicitly supports multi-client agency workspaces, budget pacing, and connections to Google Ads, Meta, LinkedIn, TikTok, GA4, CRM systems, warehouses, etc. Supermetrics Supermetrics

supermetrics.com

• Improvado — worth considering if you're operating at significant scale and need a more enterprise-grade data layer. It advertises 1,000+ integrations, cross-client harmonization, governance, attribution, and warehouse/BI connectivity. Improvado Improvado

improvado.io

• Funnel — particularly relevant if your primary problem is creating a standardized data layer underneath many client dashboards and reports. Funnel describes itself as the marketing data hub for agency client work, sitting between ad platforms and BI/reporting/warehouse systems. Funnel

funnel.io

For your specific use case

I'd structure it roughly like:

Ad platforms / CRM / analytics
→ Supermetrics / Improvado / Funnel
→ centralized client × campaign × date dataset
→ forecasting model
→ Power BI / Looker / custom planning interface

The important part is that your underlying dataset should probably have dimensions like:

Client → Channel → Account → Campaign → Date

and measures such as:

Budget | Spend | Impressions | Clicks | Conversions | Revenue | CPA | ROAS | Pacing

Then your forecasting layer can calculate things like:

• month-end spend
• expected conversions/revenue
• budget remaining
• projected CPA/ROAS
• under/over-pacing
• scenario forecasts if budgets change
• historical performance by client/channel/campaign

One important distinction: these platforms are primarily the data consolidation/analytics layer. I wouldn't choose one solely because it says "forecasting." If forecasting is the central requirement, I'd want the consolidated data flowing into a proper modeling layer (warehouse + Python/SQL, Power BI, or a dedicated planning/forecasting system).

For hundreds of clients, I'd lean toward evaluating Supermetrics vs. Improvado vs. Funnel first, with the deciding factor being how much you need data engineering/governance versus ease of deployment.

If you tell me (1) roughly how many clients/accounts, (2) which ad platforms, (3) whether you need client-level forecasts or campaign-level forecasts, and (4) what you currently use for reporting (Excel, Sheets, Power BI, Looker, etc.), I can map out the actual stack I'd use and what the implementation would look like.

Then your forecasting layer can calculate things like:

• month-end spend
• expected conversions/revenue
• budget remaining
• projected CPA/ROAS
• under/over-pacing
• scenario forecasts if budgets change
• historical performance by client/channel/campaign

One important distinction: these platforms are primarily the data consolidation/analytics layer. I wouldn't choose one solely because it says "forecasting." If forecasting is the central requirement, I'd want the consolidated data flowing into a proper modeling layer (warehouse + Python/SQL, Power BI, or a dedicated planning/forecasting system).

For hundreds of clients, I'd lean toward evaluating Supermetrics vs. Improvado vs. Funnel first, with the deciding factor being how much you need data engineering/governance versus ease of deployment.

If you tell me (1) roughly how many clients/accounts, (2) which ad platforms, (3) whether you need client-level forecasts or campaign-level forecasts, and (4) what you currently use for reporting (Excel, Sheets, Power BI, Looker, etc.), I can map out the actual stack I'd use and what the implementation would look like.

If you’re managing hundreds of client campaigns and the goal is to consolidate spend + performance + budgets + forecasts, I’d look at this as a marketing data infrastructure + forecasting problem rather than just a dashboard problem.

My shortlist

1. funnel.io — my first choice

For your use case, Funnel is probably the strongest starting point.

It is built around a centralized marketing data hub, with connectors across paid media, analytics, CRM, ecommerce, etc. It can normalize campaign data, retain historical data, apply your business logic, and push the resulting dataset into BI/warehouse tools. Funnel Funnel

More importantly for an agency managing lots of clients, Funnel supports:

• Multi-client workspaces
• Spend and budget pacing
• Performance vs. goals
• Roll-up reporting across clients
• Custom dimensions/metrics
• Historical data
• Data exports to BI/warehouses
• Budget/target context
• Measurement and budget optimization/scenario simulation on its higher-end offering Funnel Funnel

Funnel's agency pricing currently starts around $300/month for Agency Standard and $600/month for Agency Advanced, although your actual price will depend on scale and requirements. Funnel

2. supermetrics.com — best if you want flexibility

Supermetrics is particularly attractive if you already have analysts working in BigQuery, Snowflake, Power BI, Excel, Google Sheets, or Looker Studio.

It supports multi-client data management and explicitly positions itself for agencies ranging from a handful of clients to thousands. It can pull data from Google Ads, Meta, LinkedIn, TikTok, GA4, CRM systems, etc., and supports custom data imports for sources that aren't natively connected. Supermetrics Supermetrics

I'd choose Supermetrics over Funnel if your team wants to own the modeling/forecasting layer themselves.

The architecture I'd actually build

For hundreds of clients, I wouldn't try to make one SaaS application do everything.

I'd build:

Ad platforms → Funnel/Supermetrics → centralized data model → forecasting engine → dashboard

For example:

Google Ads ─┐
Meta Ads ───┤
TikTok ─────┤
LinkedIn ───┤
GA4 ────────┤
CRM ────────┤
             ↓
       Funnel / Supermetrics
             ↓
       BigQuery / Snowflake
             ↓
     Standardized campaign model
             ↓
     Forecasting + scenarios
             ↓
       Power BI / Looker

The important part is the standardized campaign model.

I'd want every campaign represented roughly like:

Then you can forecast at multiple levels:

• Client → monthly spend
• Client → channel
• Channel → campaign
• Entire agency portfolio
• Current month → month-end
• Remaining quarter
• Remaining year

And, critically, you can answer questions such as:

"We're $4.2M committed across 300 clients. How much will we actually spend by month-end?"

or:

"Which clients are likely to underspend their committed budgets?"

or:

"If Meta gets another $500K, what should we expect in incremental conversions?"

That's where I'd put the forecasting intelligence rather than trying to make the reporting tool itself be the forecasting system.

Funnel vs. Supermetrics

Funnel also now has Marketing Mix Modeling, attribution, budget optimization, and scenario simulation, which makes it particularly interesting if by "forecasting" you mean more than simply projecting month-end spend. Funnel

My recommendation

If you mean "I need a single system where my agency can consolidate hundreds of clients' campaign data and then forecast spend/performance", I'd start with Funnel.

If you mean "I need a reliable data extraction layer, and we're going to build sophisticated forecasting ourselves", I'd choose Supermetrics + BigQuery/Snowflake + a BI/forecasting layer.

If you tell me roughly how many clients, which ad platforms (Google/Meta/TikTok/etc.), monthly spend, and whether your forecast is primarily spend pacing or performance/ROAS/conversion forecasting, I can recommend a specific stack and data architecture—including what I'd use for the actual forecasting model.

"We're $4.2M committed across 300 clients. How much will we actually spend by month-end?"

or:

"Which clients are likely to underspend their committed budgets?"

or:

"If Meta gets another $500K, what should we expect in incremental conversions?"

That's where I'd put the forecasting intelligence rather than trying to make the reporting tool itself be the forecasting system.

Funnel vs. Supermetrics

Funnel also now has Marketing Mix Modeling, attribution, budget optimization, and scenario simulation, which makes it particularly interesting if by "forecasting" you mean more than simply projecting month-end spend.

My recommendation

If you’re managing hundreds of client campaigns and want to consolidate spend + performance for forecasting, I’d look at this as a data infrastructure + forecasting problem rather than just buying a reporting dashboard.

My shortlist

What I'd probably build

For hundreds of campaigns, I'd use:

Ad platforms → Supermetrics → BigQuery/Snowflake → forecasting model → Power BI/Tableau/Looker

Supermetrics can pull data from 170+ sources, normalize it, and push it into data warehouses such as BigQuery and Snowflake. It also supports custom imports/connectors, which is useful when every client has slightly different data. Supermetrics Supermetrics Supermetrics Documentation

Then your warehouse becomes the single source of truth with dimensions like:

• Client
• Account
• Campaign
• Channel
• Geography
• Product/service
• Month/week
• Spend
• Impressions
• Clicks
• Leads
• Revenue
• CPA/CPL/ROAS
• Budget
• Actual spend
• Forecast spend
• Forecast conversions/revenue

From there, forecasting becomes much more useful than simply projecting spend. You can answer things like:

"Client X has $180k remaining this quarter. Based on current pacing, seasonality, CPA trends, and historical performance, where will they finish?"

and:

"If we increase Meta spend 20%, what's the expected incremental lead volume and what happens to blended CPA?"

Where Salesforce fits

If you're a large agency/enterprise operation and need sophisticated cross-channel attribution, Salesforce Marketing Intelligence is worth evaluating. It has data harmonization, cross-channel dashboards, attribution, and campaign-level analysis; Salesforce also supports separate workspaces that can be used to separate clients/business units. Salesforce Salesforce Salesforce

It can also do more than reporting: Salesforce describes modeling functionality for assessing how changing investment affects performance. Trailhead

But I'd consider it overkill for many agencies unless you already live in the Salesforce ecosystem.

The important distinction

If by "forecasting" you mean financial/media pacing, you don't necessarily need an advanced ML platform.

I'd build three layers:

1. Actuals

What did we spend and what did we get?

2. Pacing forecast

Where will we finish if current trends continue?

3. Scenario forecast

What happens if we change budget, channel mix, CPA targets, etc.?

That third layer is where the real value is.

My recommendation: start with Supermetrics + BigQuery + your preferred BI tool, rather than trying to make a single SaaS product do everything. Supermetrics explicitly supports automated warehouse pipelines and retaining historical cross-channel data, which is particularly useful for forecasting. Supermetrics

If you tell me (a) roughly how many clients, (b) which ad platforms—Google/Meta/TikTok/LinkedIn/etc., (c) whether you need client-facing dashboards, and (d) whether forecasting means spend pacing or leads/revenue forecasting, I can recommend a specific stack and architecture for your use case.

If you’re managing hundreds of client campaigns and want to consolidate spend + performance + forecasting, I’d avoid a pure dashboarding tool. You want a marketing data layer that can ingest all accounts, normalize them, retain historical data, and feed a forecasting/BI layer.

My shortlist

My first choice for your use case would be Funnel. It now combines a centralized marketing data hub with marketing mix modeling, multi-touch attribution, incrementality testing, budget optimization, and scenario simulation—which gets much closer to the forecasting problem you're describing than a reporting-only platform. Funnel Funnel

Supermetrics would be my second choice, particularly if you already have analysts/data engineers and want to push normalized data into BigQuery, Snowflake, Power BI, etc. It supports large-scale account management and has APIs designed for managing multiple clients/accounts programmatically. Supermetrics Supermetrics

What I'd actually build

For hundreds of clients, I'd structure it roughly like:

Ad platforms → Funnel/Supermetrics → centralized data model → Power BI/Looker → forecasting model

Your canonical dataset should probably have dimensions like:

Client → Account → Channel → Campaign → Date → Geography → Product/Service

and metrics such as:

Budget | Spend | Impressions | Clicks | Leads | Conversions | Revenue | CPA | ROAS

Then your forecasting layer can answer things like:

• How much will each client spend by month-end?
• Where are we pacing above/below budget?
• What will CPA/ROAS look like at current pace?
• Which campaigns are likely to underspend?
• If we increase Client X's budget 20%, what happens to projected conversions?
• What's the agency-wide forecast vs. target?
• Which clients/accounts are creating the biggest variance?

One important distinction: data consolidation and forecasting are two separate problems. Funnel is particularly interesting because it is now explicitly adding budget optimization and scenario simulation on top of its data infrastructure. Funnel

If you tell me what platforms you're running campaigns on (Google/Meta/LinkedIn/TikTok/etc.), roughly how many clients/accounts, and whether you're forecasting spend, leads/revenue, or both, I can recommend a specific stack—including Funnel vs Supermetrics vs Improvado + Power BI, and what the architecture would look like.

What I'd actually build

For hundreds of clients, I'd structure it roughly like:

Ad platforms → Funnel/Supermetrics → centralized data model → Power BI/Looker → forecasting model

Your canonical dataset should probably have dimensions like:

Client → Account → Channel → Campaign → Date → Geography → Product/Service

and metrics such as:

Budget | Spend | Impressions | Clicks | Leads | Conversions | Revenue | CPA | ROAS

If you’re managing hundreds of client campaigns, I’d look for a marketing data platform rather than a traditional reporting dashboard. The key is to create one normalized dataset across clients, campaigns, channels, spend, conversions, revenue, and pacing—and then layer forecasting on top.

My shortlist

Why I'd start with Improvado: it currently advertises 1,000+ marketing/data connectors, unified campaign data, campaign governance, and an AI agent that can specifically forecast spend and ROAS from historical trends and current pacing. Improvado Improvado It also supports sending the consolidated data into your own warehouse/BI environment, which is important when you're dealing with hundreds of clients. Improvado

Supermetrics is my second choice. It has 170+ data-warehouse connectors and is particularly attractive if your team already has a BigQuery/Snowflake + Looker/Tableau/Power BI setup. Supermetrics Supermetrics

Funnel is also worth evaluating if the primary problem is getting consistent marketing data into one place; its Data Hub is explicitly designed as a centralized source of truth across marketing data. Funnel

What I'd build for hundreds of clients

I wouldn't try to make the software itself your entire forecasting system. I'd structure it as:

Ad platforms / CRM / analytics
↓
Improvado or Supermetrics
↓
Normalized client + campaign data warehouse
↓
Forecasting model
↓
Client / account dashboards

Your core dataset should have something like:

Client → Account → Channel → Campaign → Date → Spend → Impressions → Clicks → Leads → Revenue → CPA/CPL → ROAS → Budget → Pacing

Then forecast at several levels:

• Campaign: expected spend and conversions
• Client: expected monthly spend/revenue
• Channel: Google vs. Meta vs. TikTok, etc.
• Agency: total expected spend/revenue
• Variance: actual vs. budget vs. forecast
• Run-rate: where the account is likely to finish if current pacing continues
• Scenario: "What happens if we increase Meta spend 20%?"

For your particular situation, I'd demo Improvado first, then Supermetrics and Funnel as benchmarks. Improvado is unusually aligned with the combination of many accounts + cross-channel normalization + campaign monitoring + forecasting rather than merely producing prettier reports. Improvado Improvado

If you tell me what platforms the campaigns run on (Google/Meta/TikTok/etc.), roughly how many clients/accounts, and whether you're an agency or in-house, I can narrow this down to a specific stack—including the database, BI layer, and forecasting approach I'd use.

Funnel is also worth evaluating if the primary problem is getting consistent marketing data into one place; its Data Hub is explicitly designed as a centralized source of truth across marketing data. funnel.io

What I'd build for hundreds of clients

I wouldn't try to make the software itself your entire forecasting system. I'd structure it as:

Ad platforms / CRM / analytics
↓
Improvado or Supermetrics
↓
Normalized client + campaign data warehouse
↓
Forecasting model
↓
Client / account dashboards

Your core dataset should have something like:

If you're managing hundreds of client campaigns and the goal is specifically consolidated spend + performance + forecasting, I’d avoid choosing a pure reporting dashboard. You want a marketing data layer + forecasting/BI layer.

My shortlist

1. I'd start with Funnel

For your use case, Funnel is probably the first product I'd demo.

It is explicitly designed to collect, normalize, and govern marketing data across accounts and channels, and its agency offering is aimed at scaling client reporting without scaling headcount. Funnel Funnel

More importantly, Funnel now has Funnel Measure, which adds marketing-mix modeling, forecasting, scenario planning, marginal ROAS and budget recommendations. Their models can operate at channel/campaign level and update daily. Funnel

That gets much closer to:

"We've spent $X across 300 clients, here's what we're pacing toward, here's the expected month-end result, and here's where we should move budget."

rather than simply:

"Here's a dashboard showing what happened."

funnel.io

2. Supermetrics if you want to build your own forecasting system

I'd choose Supermetrics if your organization has analysts/data engineers and you want the underlying data to feed a custom forecasting model.

It connects 170+ marketing and sales sources and can send standardized data to warehouses and BI platforms such as BigQuery, Snowflake and Power BI. Supermetrics Supermetrics

A strong architecture would be:

Google Ads / Meta / TikTok / LinkedIn / GA4 / CRM
→ Supermetrics
→ BigQuery/Snowflake
→ Power BI / Looker
→ forecasting model

That gives you substantially more control over how you forecast each client's spend, conversions, CAC, ROAS, revenue, etc.

supermetrics.com

3. Power BI is particularly interesting for the forecasting layer

If you already have clean campaign-level data, Power BI can be the analytical/forecasting layer rather than buying an all-in-one marketing platform.

I'd structure your data around something like:

Client
  └── Account
       └── Channel
            └── Campaign
                 └── Date
                      ├── Spend
                      ├── Impressions
                      ├── Clicks
                      ├── Leads
                      ├── Revenue
                      ├── CPA
                      ├── ROAS
                      ├── Budget
                      └── Forecast

Then build forecasting around client × month, with drilldowns to account/campaign.

The important part is that you don't want 300 individual forecasting spreadsheets. You want one standardized data model where every client is another slice of the same system.

What I'd build for a 100s-of-clients agency

My preferred setup would be:

Funnel → centralized marketing data → forecasting/measurement → Power BI/your client dashboards

or, if you have a strong data team:

Supermetrics → BigQuery → forecasting models → Power BI

The first is faster to operationalize. The second is more customizable.

I would not make AgencyAnalytics/Whatagraph/Looker Studio the core forecasting system. They're useful reporting layers, but your problem sounds more like portfolio-level media intelligence and planning than client dashboarding. Current comparisons similarly distinguish agency reporting products from the underlying marketing data infrastructure. Supermetrics Hyper AI

One important question

If you tell me roughly (1) how many clients, (2) which ad platforms, (3) whether you're an agency, and (4) what you need to forecast—spend, leads, revenue, ROAS, or all of them, I can narrow this to a specific stack and architecture, including what I'd use for the database, ingestion, forecasting model, and client-facing dashboards.

If you’re managing hundreds of client campaigns and want to consolidate spend + performance + budgets + forecasting, I’d avoid a traditional reporting dashboard as the core system. You want a central marketing data layer with a forecasting/BI layer on top.

My shortlist

1. Funnel — probably where I'd start

funnel.io is particularly suited to your situation. It is explicitly designed for agencies and can consolidate client marketing data, normalize it, track spend vs. budget, performance vs. goals, and create portfolio-level rollups. Funnel Funnel

Its current agency plans also support hundreds of connectors and multiple client workspaces. Funnel

The architecture I'd use is:

Google/Meta/LinkedIn/TikTok/etc. → Funnel → warehouse/BI → forecasting model

That lets you answer things like:

• How much will each client spend by month-end?
• Which campaigns are pacing above/below budget?
• What's projected CPA/ROAS at current trajectory?
• Which clients are likely to underspend?
• What's the agency's total projected spend next month/quarter?
• How does projected performance compare with contracted targets?
• Which campaigns are statistically behaving differently from historical patterns?

2. Supermetrics — excellent alternative

supermetrics.com is another strong choice, particularly if you want more control over where the data ultimately lives. It supports multi-client agency management and can push consolidated data into BigQuery, Snowflake, Power BI, Looker Studio, etc. Supermetrics Supermetrics

I'd lean Supermetrics over Funnel if your team already has a data warehouse/BI environment and wants the marketing-data ingestion layer to be relatively interchangeable.

3. BigQuery + Looker — best if forecasting is the actual priority

If by "forecasting" you mean real financial/media forecasting, rather than simply projecting a campaign's spend curve, I'd seriously consider building the system around BigQuery + Looker.

Google specifically positions BigQuery + Looker for consolidated marketing data and predictive analytics, and BigQuery ML supports forecasting models such as ARIMA Plus. Google Cloud Google Cloud

That gives you considerably more freedom to build models such as:

Projected spend

actual spend + remaining planned budget + historical pacing + seasonality

Projected conversions

forecast spend × predicted conversion rate

Projected CPA

forecast spend ÷ predicted conversions

Projected ROAS

predicted revenue ÷ forecast spend

You can also build forecasts at multiple levels:

Agency → Client → Account → Campaign → Ad set → Day

and roll them back up automatically.

What I'd actually build

For hundreds of clients, my preferred stack would be:

Ad platforms
↓
Funnel or Supermetrics
↓
BigQuery
↓
standardized campaign/client/budget data model
↓
Looker dashboard + forecasting models
↓
alerts / pacing / forecast outputs

The key isn't really the dashboard. It's creating a common data model across all clients.

For example:

client_id
account_id
campaign_id
date
channel
campaign
budget
planned_spend
actual_spend
impressions
clicks
conversions
revenue
CPA
ROAS
forecast_spend
forecast_conversions
forecast_CPA
forecast_ROAS

Then you can have a single forecasting engine operate across all campaigns instead of building bespoke spreadsheets for every client.

If you tell me roughly how many clients/campaigns you have, which channels you use (Google/Meta/TikTok/etc.), and whether you need client-facing reporting or primarily an internal forecasting tool, I can recommend the exact stack and architecture—including which product I'd pick between Funnel, Supermetrics, Improvado, and a custom BigQuery setup.

If you’re talking about hundreds of client campaigns/accounts, with the goal of consolidating spend + performance + budget pacing + forecasting, I’d look at a marketing-data platform rather than a traditional reporting dashboard.

My shortlist

1. supermetrics.com — my first choice

• Built specifically for consolidating marketing data across clients and channels.
• Pulls data from Google Ads, Meta, TikTok, LinkedIn, GA4, CRM systems, etc., then normalizes/blends it. Supermetrics Supermetrics
• Particularly strong for agencies: multi-client dashboards, budget-pacing alerts, cross-client benchmarking and automated reporting are explicit use cases. Supermetrics
• You can feed the normalized data into Power BI, Looker Studio, Excel, a warehouse, or your own forecasting model. Supermetrics
• Best fit: You want a scalable data layer that becomes the source of truth for hundreds of accounts.
• funnel.io — very strong alternative

• Funnel is designed as a centralized marketing-data hub and supports 500+ marketing apps/platforms. Funnel Help
• Good choice if your biggest problem is getting hundreds of disparate client data sources into a standardized structure.
• Particularly attractive if you're comfortable doing the actual forecasting in Power BI, Tableau, a warehouse, or another analytics layer.
• Power BI + Supermetrics/Funnel — best if forecasting is the real objective

• Rather than expecting the marketing platform itself to be your forecasting engine, I'd use Supermetrics or Funnel as the data ingestion/normalization layer and Power BI as the analytical layer.
• Then you can model things like: - planned vs. actual spend
• projected month-end spend
• projected CPA/ROAS
• remaining budget
• pacing by client/account/campaign
• historical seasonality
• forecast confidence
• portfolio-level spend and performance

The architecture I'd use

For hundreds of clients, I'd avoid building one enormous spreadsheet. I'd structure it roughly like:

Ad platforms → Supermetrics/Funnel → centralized data model → Power BI → forecasting

Your core table should probably have dimensions such as:

Client → Account → Channel → Campaign → Date

with measures like:

Budget | Planned Spend | Actual Spend | Forecast Spend | Impressions | Clicks | Conversions | Revenue | CPA | ROAS

Then you can roll it up from campaign → account → client → entire agency.

The important distinction is that consolidating spend/performance and forecasting are two separate problems. Supermetrics explicitly supports budget pacing and consolidated performance, but I'd build the actual forecasting methodology on top of that data rather than choosing software solely because it advertises "forecasting." Supermetrics Supermetrics

What I'd choose

If you tell me roughly how many clients/accounts you have, which channels you run (Google/Meta/TikTok/etc.), and whether you're forecasting spend, leads/revenue, or ROAS/CPA, I can recommend the exact stack and data model I'd use.

If you mean hundreds of client campaigns across multiple ad platforms, with a goal of consolidating spend + performance + budget pacing + forecasting, I’d look at a data/marketing-intelligence platform rather than a traditional reporting dashboard.

My shortlist

1. Funnel — probably where I'd start

funnel.io

For hundreds of clients, the important thing isn't just getting data out of Google/Meta/etc.; it's having a standardized data model across every account.

Funnel is particularly suited to this: it provides a centralized Data Hub where you can establish consistent channel/campaign definitions and then push the same dataset into reporting and analysis destinations. Funnel Funnel

I'd use it roughly like:

Google Ads + Meta + LinkedIn + TikTok + Microsoft + GA4
→ Funnel
→ standardized client/campaign dataset
→ Power BI / Looker / your forecasting model

That gives you the ability to ask things like:

• How much will each client spend by month-end?
• Which campaigns are under/over pacing?
• What's forecasted CPA/ROAS?
• Which clients are likely to underspend their committed budgets?
• What happens to aggregate spend if performance changes 10%?
• Which campaigns are deteriorating across the entire portfolio?

2. Supermetrics — excellent alternative

supermetrics.com

I'd choose Supermetrics if you want more control over the data and forecasting architecture.

It supports multi-client workspaces, 150+ data sources, budget pacing, cross-client benchmarking, and destinations such as BigQuery, Snowflake, Power BI, Excel and Looker Studio. It explicitly positions itself for agencies ranging from a handful of clients to thousands. Supermetrics Supermetrics

The nice architecture here is:

Ad platforms → Supermetrics → BigQuery → forecasting model → Power BI

That becomes substantially more powerful than trying to make the reporting platform itself do all your forecasting.

3. AgencyAnalytics — if the primary need is reporting

agencyanalytics.com

AgencyAnalytics now has forecasting, anomaly detection and roll-up reporting across clients/campaigns, so it's worth considering if you want something more turnkey. AgencyAnalytics

But for hundreds of clients + serious forecasting, I'd put it behind Funnel/Supermetrics.

What I'd actually build

If this is for an agency with hundreds of client campaigns, I'd avoid making the dashboard software your forecasting engine.

I'd build:

1. Data ingestion: Funnel or Supermetrics
↓
2. Central data warehouse: BigQuery or Snowflake
↓
3. Standardized fact table:
client → account → channel → campaign → date → spend → impressions → clicks → conversions → revenue
↓
4. Forecasting layer: historical pacing + seasonality + performance trends + planned budgets
↓
5. BI: Power BI / Looker
↓
6. Automated alerts: overspend, underspend, CPA deterioration, ROAS deterioration, forecast-vs-plan

That architecture gives you portfolio-level forecasting, rather than just 500 individual campaign dashboards.

My recommendation: start by evaluating Funnel vs. Supermetrics. For your specific description, I'd lean Funnel if you want the fastest path to a standardized multi-client data operation, and Supermetrics + BigQuery if you want to build a more sophisticated proprietary forecasting system. Funnel Funnel

If you tell me roughly how many clients, which ad platforms, monthly spend, and whether you need revenue/CRM data too, I can map out the exact stack I'd use and what I'd put in the forecasting model.

If you’re managing hundreds of client campaigns and want to consolidate spend + performance + forecasting, I’d look at this as a marketing data infrastructure problem rather than simply a dashboard problem.

My shortlist

1. funnel.io — my first choice

For your particular use case, I'd start here.

Funnel is explicitly built around bringing multiple clients, campaigns and channels into one normalized data layer. Its agency product supports roll-up reporting across clients, budget pacing, cost-vs-budget, performance-vs-goal, benchmarking, and custom metrics/dimensions. Funnel page.funnel.io

More importantly for you, Funnel now has forecasting/measurement capabilities including marketing mix modeling, incrementality testing and triangulation, while retaining historical data for comparisons. Funnel

That gives you a structure roughly like:

Google Ads / Meta / TikTok / LinkedIn / etc.
↓
Funnel
↓
Normalized campaign + spend + conversion data
↓
Client → Account → Campaign → Channel → Month
↓
Forecast / pacing / benchmarks / dashboards

That's much more scalable than maintaining hundreds of spreadsheets.

2. improvado.io — if you're really talking enterprise

I'd choose Improvado over Funnel if your environment is extremely heterogeneous—lots of different platforms, custom attribution, CRM/revenue data, complicated transformations, or a dedicated analytics/data team.

It has a marketing common data model and is designed for large-scale pipelines, with destinations such as BigQuery, Snowflake and Redshift. Its own 2026 comparison describes end-to-end platforms such as Improvado as better suited to environments with 30+ data sources than simple connector tools. Improvado

The downside is that it's more of an analytics/data infrastructure platform than a simple agency reporting tool, so implementation can be considerably heavier.

3. supermetrics.com + BigQuery/Snowflake — best if you want to own the model

This is what I'd consider if you have technical resources.

Supermetrics can move data from 170+ marketing platforms into a warehouse such as BigQuery, Snowflake or Redshift. Supermetrics

Then you can build your own forecasting layer:

Supermetrics → BigQuery/Snowflake → dbt/SQL → Power BI/Looker → forecasting model

This is more work, but it gives you substantially more control over things like:

• Client-level forecasts
• Campaign-level forecasts
• Spend pacing
• CPA/ROAS forecasts
• Seasonality
• Budget reallocation
• Client profitability
• Custom attribution
• Scenario modeling
• "What happens if we increase Meta spend 20%?"

One important distinction

If by forecasting you mean:

"We've spent $75K of a $100K monthly budget and are pacing toward $112K."

Then Funnel is probably enough.

If you mean:

"Given historical spend, CPA, conversion rate, seasonality, channel mix and client revenue, predict next month's spend, conversions and ROAS—and let me model alternative budget allocations."

Then I'd build around Funnel/Improvado + a warehouse + Power BI or another modeling layer. A reporting platform alone isn't necessarily the best forecasting engine.

What I'd do for hundreds of clients

I'd probably build this architecture:

Ad platforms + analytics + CRM
→ Funnel
→ centralized normalized dataset
→ Power BI / warehouse
→ forecasting model
→ executive + account-manager dashboards

And I'd make the fundamental data model:

Client → Account → Channel → Campaign → Date

with standardized fields for:

Spend | Impressions | Clicks | Leads | Conversions | Revenue | CPA | ROAS | Budget | Forecast | Variance

That lets you answer things like "Which 20 clients are going to miss their monthly performance target?" without manually opening 20 dashboards.

If you tell me roughly how many clients you have, which ad platforms you use (Meta/Google/TikTok/LinkedIn/etc.), and whether you need client-facing dashboards, I can narrow this to a specific stack and architecture—including what I'd use for the forecasting layer.

For hundreds of client campaigns, you’ll usually want something beyond a dashboard tool. The right stack depends on whether you are a marketing agency managing many clients or an enterprise marketing team managing many campaigns, but the core needs are:

• Consolidate spend from ad platforms, vendors, and invoices
• Normalize campaign KPIs across clients (CPL, CPA, ROAS, CAC, pipeline, revenue)
• Forecast future spend and outcomes
• Model “what-if” scenarios (e.g., “shift 20% budget from Meta to Google”)
• Provide client-level and executive rollups

Here are the strongest options:

Best overall for enterprise-scale forecasting: Anaplan

anaplan.com

Best if you need a planning + forecasting engine rather than just reporting. It is designed for connected planning, budget allocation, scenario modeling, and tying spend plans to business outcomes. Anaplan Inc Anaplan Inc

Good fit:

• 100s–1,000s of campaigns
• Multiple business units/clients
• Finance-grade forecasting
• “What happens if we increase spend by 15%?” modeling

Typical architecture:

• Data warehouse → Anaplan model → dashboards/reporting layer

Downside:

• More expensive
• Requires implementation and modeling expertise

Best marketing planning + finance alignment: Planful

planful.com

Planful is strong when you need marketing budgets, forecasts, actual spend, and performance tied together in one planning model. It positions itself around connecting campaigns, budgets, and results rather than just displaying metrics. Planful

Good fit:

• Agencies or enterprises with formal budgeting cycles
• Teams reporting to finance
• Quarterly/monthly forecasting

Best for agencies needing client reporting + campaign rollups: AgencyAnalytics

agencyanalytics.com

Good for:

• Hundreds of client accounts
• Automated reporting
• Multi-channel dashboards
• Client-facing portals

It can consolidate campaign metrics across channels and provide trend analysis/forecasting features. AgencyAnalytics

Downside:

• Better for reporting and operational visibility than true financial forecasting

Best if you need paid media optimization at scale: Marin Software

marinsoftware.com

Useful for agencies running lots of paid search/social campaigns. It focuses on budget pacing, spend allocation, and performance optimization across accounts. Marin Software

Good fit:

• Large PPC portfolios
• Performance marketing teams
• Automated budget management

Best “build your own” analytics stack

For a sophisticated agency, I would often build:

Data ingestion

• Fivetran / Airbyte → pull Meta, Google Ads, LinkedIn, TikTok, CRM, billing

Warehouse

• Snowflake or Google BigQuery

Modeling

• dbt

Dashboards

• Looker, Tableau, or Power BI

Forecasting

• Python models, Anaplan, Planful, or a specialized marketing forecasting layer

This gives you the most flexibility for hundreds of clients but requires data engineering.

My shortlist by use case

For a digital marketing agency managing hundreds of client campaigns, I would usually avoid trying to make a pure BI dashboard do forecasting. A common winning setup is:

Ad platforms + CRM + billing → data warehouse → AgencyAnalytics/BI dashboards → Anaplan or custom forecasting layer

A few details would change the recommendation:

1. Are these hundreds of clients (agency model) or hundreds of campaigns for one company?
2. Which channels matter most (Google Ads, Meta, LinkedIn, Amazon, offline media)?
3. Do you need to forecast spend pacing or business outcomes (leads/revenue/ROAS)?

For a digital marketing agency managing hundreds of client campaigns, I would usually avoid trying to make a pure BI dashboard do forecasting. A common winning setup is:

Ad platforms + CRM + billing → data warehouse → AgencyAnalytics/BI dashboards → Anaplan or custom forecasting layer

A few details would change the recommendation:

1. Are these hundreds of clients (agency model) or hundreds of campaigns for one company?
2. Which channels matter most (Google Ads, Meta, LinkedIn, Amazon, offline media)?
3. Do you need to forecast spend pacing or business outcomes (leads/revenue/ROAS)?

If you're managing hundreds of client campaigns and want to consolidate spend + performance + forecasting, I’d look at a marketing data/BI stack rather than a basic reporting dashboard.

My shortlist

1. funnel.io — best overall fit

• Pulls data from hundreds of advertising, analytics, CRM, and other sources into one governed data layer.
• Lets you standardize campaign names, channels, currencies, metrics, budgets, and targets across clients.
• Can feed Power BI, Tableau, Looker Studio, BigQuery, Snowflake, Excel/Sheets, etc.
• Importantly for your use case, its current Measure product includes MMM, multi-touch attribution, incrementality testing, and scenario forecasting. Funnel Funnel
• Funnel explicitly supports agencies and client reporting, including separate client dashboards/portals. Funnel

I'd start here. The important distinction is that Funnel can become the centralized marketing data layer, rather than just another reporting interface.

2. windsor.ai — best if you want more flexibility / lower-cost data plumbing

• Connects 350+ marketing, sales, and analytics sources.
• Good for pushing normalized data into a warehouse or BI system.
• Has attribution capabilities and can help with budget optimization. windsor.ai windsor.ai
• I'd pair it with Power BI or a warehouse + your own forecasting model if forecasting is a major requirement.
• Power BI + a marketing data connector — best if forecasting/financial modeling is the priority

• If your organization already uses Microsoft, this can be a very powerful setup.
• I'd use a platform such as Funnel to ingest/normalize the advertising data, then Power BI for the actual portfolio-level dashboards and forecasting models. Funnel specifically supports Power BI exports. Funnel

What I'd build for your situation

For hundreds of clients, I'd structure it like:

Google Ads / Meta / TikTok / LinkedIn / Microsoft / GA4 / CRM
↓
Funnel Data Hub
↓
standardized campaign + client + channel data model
↓
BigQuery or Snowflake
↓
Power BI / Tableau
↓
forecasting + budget allocation + client dashboards

The critical part isn't the dashboard. It's establishing a common schema such as:

Client → Account → Channel → Campaign → Objective → Month → Spend → Impressions → Clicks → Leads → Revenue → CPA → ROAS → Budget → Forecast

Then you can answer questions like:

• "How much will we spend across all clients next month?"
• "Which clients are pacing >10% above budget?"
• "What's the projected CPA by client at month-end?"
• "If we shift $500K from Meta to Google, what's the expected impact?"
• "Which campaigns are responsible for the forecast variance?"
• "What's our total agency media spend and projected revenue for Q4?"
• "Show me clients likely to miss their acquisition targets."

One important distinction

If by forecasting you mean simple pacing—e.g. "we've spent $40K through day 15, so we're projected to spend $82K this month"—Funnel + BI is more than sufficient.

If you mean true media-mix forecasting / budget optimization, I'd specifically evaluate Funnel Measure or a dedicated MMM/forecasting platform. Funnel now advertises scenario forecasting alongside MMM, MTA, and incrementality, which makes it particularly interesting for your use case. Funnel

For scale, Funnel currently advertises 600+ connectors and plans specifically designed for agencies; its Business tier starts at $600/month billed annually, with Enterprise available for larger organizations. Funnel

My pick: Funnel + Power BI (or BigQuery + Power BI) if you need a serious agency-wide forecasting system. I would not make AgencyAnalytics/DashThis-style client reporting software the core of the architecture; those are better suited to reporting than being the central analytical system.

If you tell me what ad platforms you manage, roughly how many clients/campaigns, and whether you already use Snowflake/BigQuery/Power BI/Tableau, I can map out the exact stack I'd use—including what should live in the warehouse, what should live in the BI layer, and how I'd build the forecasting model.

If you’re managing hundreds of client campaigns, I’d avoid treating this as just a reporting problem. You need a system that can normalize spend + performance across accounts, establish pacing/benchmarks, and support forward-looking budget forecasts.

My shortlist

1. My first choice: ASK BOSCO

askbosco.io

This is the closest match to what you described. It specifically positions itself as an AI reporting and forecasting platform for agencies, with client-level data consolidation, budget planning, scenario testing, and forecasting. It can model different media-spend strategies rather than merely telling you what happened last month. ASK BOSCO

I'd investigate this first if your question is essentially:

"Given historical spend and performance across hundreds of campaigns, how much should we spend next month/quarter, and what performance should we expect?"

2. Supermetrics + BI/warehouse — best if you're really at hundreds of clients

supermetrics.com

For hundreds of clients, I'd seriously consider a data architecture rather than putting everything into a traditional agency reporting tool.

Supermetrics can pull granular data from 150+ sources and centralize multi-client reporting, including spend and budget pacing. Supermetrics

The architecture would look roughly like:

Google/Meta/Microsoft/LinkedIn/etc. → Supermetrics → warehouse → forecasting model → Power BI/Tableau/Looker

That gives you much more control over things like:

• Client → account → campaign → ad hierarchy
• Daily spend and pacing
• CPL/CPA/ROAS
• Historical seasonality
• Budget vs. actual
• Forecasted spend
• Forecasted conversions/revenue
• Scenario modeling
• Portfolio-level forecasting
• Client-level forecasting

For 200–1,000+ clients, this is probably the architecture I'd choose if you have data/engineering resources.

3. AgencyAnalytics — if reporting is the bigger problem

agencyanalytics.com

AgencyAnalytics has a particularly useful roll-up capability: you can aggregate spend, clicks, conversions, etc. across clients and compare performance across the portfolio. It supports 85+ native integrations. AgencyAnalytics

I'd choose it when the primary question is:

"How do I see every client's performance in one place and automate reporting?"

rather than:

"How do I build a sophisticated forecasting engine?"

4. Coupler.io — underrated option for a flexible data layer

coupler.io

Coupler is interesting because it combines data integration, transformation, dashboards and reporting, and explicitly supports blending advertising spend with CRM, Shopify, revenue, etc. It also advertises no data-volume limits and automated refreshes. Coupler.io

That makes it attractive if you want to construct your own forecasting layer without immediately building a full data engineering stack.

What I'd actually recommend

If this is hundreds of clients + multiple advertising platforms + serious forecasting, I'd rank the approaches:

1. ASK BOSCO → if you want to buy rather than build the forecasting capability.

2. Supermetrics + warehouse + BI/forecasting → if this is a strategic internal system and you need maximum control.

3. Coupler.io + BI → if you want something in between.

4. AgencyAnalytics → if reporting/monitoring is the main need and forecasting is secondary.

One important distinction: don't evaluate these primarily on how pretty their dashboards are. For your use case, I'd test whether the system can ingest campaign-level daily data across the entire client portfolio, preserve historical data, and forecast at multiple levels.

If you tell me what channels you manage (Google, Meta, TikTok, LinkedIn, CTV, etc.), roughly how many clients/campaigns, and whether you're forecasting spend, leads, revenue, or all three, I can narrow this down to 2–3 platforms and recommend the exact architecture.

For hundreds of client campaigns, you usually need more than a dashboard tool. The ideal stack combines:

1. Data ingestion + normalization (pull spend/performance from Google Ads, Meta, TikTok, LinkedIn, programmatic, CRM, etc.)
2. Client/campaign hierarchy management (client → brand → campaign → channel → creative)
3. Forecasting + scenario planning (expected spend, CPA/ROAS, pipeline/revenue outcomes)
4. Reporting layer (client-facing dashboards)

The best choice depends on whether you are an agency, internal marketing team, or media company.

Best-fit platforms

1. Enterprise marketing planning + forecasting

Best if you manage large budgets and need finance-grade forecasting.

• Anaplan — Strong for budget allocation, scenario modeling, and connecting spend plans to business outcomes. Good when you need “what happens if we move $500k from channel A to channel B?” modeling. Anaplan Inc Anaplan Inc
• Planful — Marketing planning focused; connects budgets, campaigns, forecasts, and performance in one model. Planful
• Uptempo — Built around enterprise marketing operations, tying financial plans, activities, and performance together. Uptempo

Choose this category if: your forecasting goes to CFO/client leadership and involves budgets, commitments, and reallocations.

2. Paid media data consolidation + performance forecasting

Best if your biggest problem is hundreds of ad accounts and inconsistent reporting.

• Singular — Strong for consolidating spend/performance data across many sources, normalizing campaign metadata, and connecting cost to ROI. Singular
• AgencyAnalytics — Good agency-oriented option for pulling campaign metrics into client dashboards and tracking trends across many accounts. AgencyAnalytics

Choose this category if: you have hundreds of campaigns across paid channels and need one reporting source of truth.

3. Build-your-own analytics stack (most flexible)

Common for larger agencies:

• Data warehouse: - Snowflake
• BigQuery
• Databricks
• ETL/connectors: - Funnel
• Supermetrics
• Fivetran
• Airbyte
• Modeling: - dbt
• BI: - Looker
• Tableau
• Power BI

This gives you maximum flexibility for custom forecasting models but requires engineering support.

My typical recommendation for a multi-client agency

For hundreds of campaigns, I would usually design:

Data layer
→ Funnel / Supermetrics / Fivetran
→ Snowflake or BigQuery

Analytics layer
→ Looker / Power BI

Forecasting layer
→ Anaplan or Planful (enterprise)
or
→ custom Python/R forecasting models in the warehouse

Client reporting
→ AgencyAnalytics, Looker dashboards, or a custom portal

Key features to require before buying

Make sure the platform supports:

• Campaign-level historical data retention (not just current dashboards)
• Spend pacing vs. plan
• Forecast by client, channel, campaign, and month
• Custom attribution windows
• Currency normalization
• Naming convention cleanup
• Budget scenarios (“increase Meta by 20%, reduce Google by 10%”)
• API access/export
• Permissions by client

A few questions would narrow this down:

1. Are you a marketing agency managing many clients, or an in-house team managing many campaigns?
2. Are the campaigns mostly paid media (Google/Meta/TikTok/etc.), or do they include CRM, email, SEO, offline spend?
3. Do you need automated client reporting, or mainly an internal forecasting model?

Analytics layer
→ Looker / Power BI

Forecasting layer
→ Anaplan or Planful (enterprise)
or
→ custom Python/R forecasting models in the warehouse

Client reporting
→ AgencyAnalytics, Looker dashboards, or a custom portal

Key features to require before buying

Make sure the platform supports:

• Campaign-level historical data retention (not just current dashboards)
• Spend pacing vs. plan
• Forecast by client, channel, campaign, and month
• Custom attribution windows
• Currency normalization
• Naming convention cleanup
• Budget scenarios (“increase Meta by 20%, reduce Google by 10%”)
• API access/export
• Permissions by client

A few questions would narrow this down:

1. Are you a marketing agency managing many clients, or an in-house team managing many campaigns?
2. Are the campaigns mostly paid media (Google/Meta/TikTok/etc.), or do they include CRM, email, SEO, offline spend?
3. Do you need automated client reporting, or mainly an internal forecasting model?

For hundreds of client campaigns, you’ll usually want more than a reporting dashboard. You need a stack that can:

1. Ingest spend + performance data from many ad platforms/accounts
2. Normalize campaigns across clients/channels
3. Forecast future spend, conversions, CAC/ROAS, and budget pacing
4. Support scenario planning (“what if we move $50k from Meta to Google?”)

The best choice depends on whether you are an agency, enterprise marketing team, or analytics-heavy operation.

Best-fit platforms

My recommendation by use case

If you run an agency with 100+ clients

A common architecture is:

Data layer

• Funnel.io or Supermetrics
• Pull from Google Ads, Meta Ads, TikTok Ads, LinkedIn Ads, DV360, Amazon Ads, GA4, CRM, etc.

↓

Warehouse

• BigQuery / Snowflake / Databricks

↓

Forecasting + dashboards

• Looker Studio, Tableau, Power BI, or a custom forecasting model

This gives you a single campaign table like:

Client → Campaign → Channel → Spend → Impressions → Clicks → Leads → Revenue → CAC → ROAS → Forecast

If you need forecasting and budget allocation, not just reporting

Look at:

• Anaplan
• Planful
• Aprimo

These are closer to marketing FP&A systems than dashboards. Anaplan Inc Aprimo

If the main problem is “we have too many ad accounts and need visibility”

Look at:

• Singular
• AgencyAnalytics
• Airtomic (especially for PPC budget pacing) Airtomic

Features I would require before buying

For hundreds of campaigns, make sure the software supports:

• Multi-client hierarchy (agency → client → campaign)
• Historical data retention
• Campaign taxonomy mapping
• Budget pacing forecasts
• Custom KPIs
• API access
• Automated anomaly detection
• Export to warehouse/BI
• Permissioning for client access

A practical shortlist

If I were evaluating this for an agency:

1. Funnel.io + Power BI/Tableau → most flexible and scalable
2. Singular → strongest if paid media measurement is the core problem
3. AgencyAnalytics → fastest path to client-facing reporting
4. Anaplan/Planful → if finance-grade forecasting is required

A key question: are these hundreds of campaigns across many clients (agency model), or hundreds of campaigns for one brand/advertiser? The ideal stack changes quite a bit.

If you’re managing hundreds of client campaigns and want to consolidate spend + performance + forecasting, I’d look at this as a marketing data infrastructure problem first, forecasting problem second.

My shortlist

1. Funnel — probably where I'd start

funnel.io is specifically designed around agency portfolios. It can bring multiple clients' marketing data into a central data hub, normalize campaigns/metrics, create rollups across clients, benchmark accounts, and automate budget pacing. Funnel

That's important because your data model should ultimately look something like:

Client → Account → Channel → Campaign → Date → Spend → Impressions → Clicks → Leads → Revenue → Goal

Then you can build a forecasting layer on top of it.

Funnel currently advertises agency plans starting at $300/month and $600/month, with the higher tier offering 500+ connectors and more advanced measurement capabilities. Funnel

I'd choose Funnel if: your biggest pain is "I have hundreds of campaigns/accounts and need one reliable source of truth."

2. Supermetrics — excellent alternative

supermetrics.com is particularly attractive if you want to push the consolidated data into BigQuery, Snowflake, Looker Studio, Power BI, Excel, etc. It supports separate client workspaces and multi-client reporting, plus budget pacing and cross-client benchmarking. Supermetrics

This gives you more flexibility to build your own forecasting system.

For example:

Supermetrics → BigQuery → forecasting model → Looker/Power BI

That is a very strong architecture if you have someone technical on the team.

3. Improvado — if forecasting is a major part of the requirement

improvado.io is the one I'd investigate if you're trying to go beyond reporting into forecasting, anomaly detection, budget allocation, and optimization.

It currently positions itself around 1,000+ data sources, a unified marketing data model, dashboards, AI analysis, anomaly detection and forecasting. Improvado Improvado

For an agency with hundreds of accounts, that's compelling because you could potentially ask questions like:

"Which clients are going to underspend this month?"
"Which campaigns are pacing 20%+ above plan?"
"Forecast total client spend through December."
"Where should we move the next $500K of budget?"

That's much closer to what I think you're actually trying to build.

What I'd recommend for your use case

If you're talking about hundreds of client campaigns, I wouldn't buy a traditional "reporting dashboard" product and expect it to solve forecasting.

I'd build this stack:

Ad platforms / campaign systems
↓
Funnel or Supermetrics
↓
Central warehouse / unified data model
↓
Forecasting layer
↓
Portfolio dashboard

And I'd have the dashboard answer four different questions:

1. Actuals: How much have we spent?
2. Pacing: Where will we finish if current behavior continues?
3. Forecast: Where do we expect to finish based on historical performance/seasonality?
4. Plan: What should we spend to hit the client's business objective?

That distinction is crucial. A lot of marketing software calls "forecasting" a simple current pace × remaining days calculation. That's useful, but it's not really a forecast.

If I were evaluating these for you

I'd run a small proof of concept with 20–30 representative clients, not the entire portfolio.

I'd ask each vendor to demonstrate:

• 100+ client/account hierarchy
• Google + Meta + LinkedIn + TikTok + whatever else you use
• Actual vs budget vs forecast
• Monthly and weekly pacing
• Client-level and portfolio-level rollups
• Currency/time-zone normalization
• Campaign naming normalization
• Historical data retention
• API refresh frequency
• Forecasting by client/channel/campaign
• Ability to export the underlying data
• Handling of campaigns that start/stop mid-month
• Custom KPIs
• Permissions so Client A can never see Client B's data

My initial ranking would be: Funnel → Improvado → Supermetrics, with the caveat that Supermetrics jumps to #1 if you already have a strong warehouse/BI environment.

If you tell me what channels you manage (Google/Meta/etc.), roughly how many clients, and whether you already use BigQuery/Looker/Power BI, I can narrow this to a specific architecture and tell you which one I'd actually buy.

My initial ranking would be: Funnel → Improvado → Supermetrics, with the caveat that Supermetrics jumps to #1 if you already have a strong warehouse/BI environment.

Then you can build a forecasting layer on top of it.

Funnel currently advertises agency plans starting at $300/month and $600/month, with the higher tier offering 500+ connectors and more advanced measurement capabilities. Funnel

I'd choose Funnel if: your biggest pain is "I have hundreds of campaigns/accounts and need one reliable source of truth."

2. Supermetrics — excellent alternative

supermetrics.com is particularly attractive if you want to push the consolidated data into BigQuery, Snowflake, Looker Studio, Power BI, Excel, etc. It supports separate client workspaces and multi-client reporting, plus budget pacing and cross-client benchmarking. Supermetrics

This gives you more flexibility to build your own forecasting system.

For example:

Supermetrics → BigQuery → forecasting model → Looker/Power BI

That is a very strong architecture if you have someone technical on the team.

3. Improvado — if forecasting is a major part of the requirement

improvado.io is the one I'd investigate if you're trying to go beyond reporting into forecasting, anomaly detection, budget allocation, and optimization.

It currently positions itself around 1,000+ data sources, a unified marketing data model, dashboards, AI analysis, anomaly detection and forecasting. improvado.io improvado.io

For an agency with hundreds of accounts, that's compelling because you could potentially ask questions like:

"Which clients are going to underspend this month?"
"Which campaigns are pacing 20%+ above plan?"
"Forecast total client spend through December."
"Where should we move the next $500K of budget?"

That's much closer to what I think you're actually trying to build.

What I'd recommend for your use case

If you're talking about hundreds of client campaigns, I wouldn't buy a traditional "reporting dashboard" product and expect it to solve forecasting.

I'd build this stack:

Ad platforms / campaign systems
↓
Funnel or Supermetrics
↓
Central warehouse / unified data model
↓
Forecasting layer
↓
Portfolio dashboard

And I'd have the dashboard answer four different questions:

For hundreds of client campaigns, you’ll usually want something beyond a reporting dashboard. The right stack depends on whether you’re an agency managing many clients or an in-house team managing many campaigns, but the core requirements are:

• Unified spend ingestion across Google Ads, Meta, TikTok, LinkedIn, programmatic, etc.
• Client/account/campaign hierarchy
• Budget pacing and forecasting
• Historical benchmarks across clients
• Scenario planning (“What happens if we move $50k from paid social to search?”)
• Automated reporting and anomaly detection

Here are the strongest options by use case:

Best overall for an agency portfolio: AgencyAnalytics

agencyanalytics.com

Best for: Digital agencies managing many client accounts.

Strengths:

• Pulls campaign data into standardized dashboards
• Designed around client reporting workflows
• Useful for comparing performance trends across accounts
• Supports recurring reporting automation and campaign analysis AgencyAnalytics

Limitations:

• Forecasting is more analytics/reporting-oriented than true financial modeling
• Less suited for complex budget allocation models across hundreds of campaigns

Best for true forecasting and budget modeling: Anaplan

anaplan.com

Best for: Large agencies or enterprises needing planning, forecasting, and scenario modeling.

Strengths:

• Connects budgets, plans, spend, and performance
• Supports “what-if” modeling and investment scenarios
• Built for financial-style forecasting rather than just dashboards Anaplan Inc

Limitations:

• Higher implementation effort
• Usually requires a dedicated owner/admin

A common architecture:

Ad platforms → data warehouse → Anaplan model → executive/client forecasts

Best for marketing finance + spend governance: Planful

planful.com

Best for: Agencies with finance-heavy forecasting requirements.

Strengths:

• Links marketing budgets, campaigns, forecasts, and outcomes
• Helps reconcile planned vs. actual spend
• Better fit when clients care about ROI, financial reporting, and budget ownership Planful

Best for paid media optimization at scale: Marin Software

marinsoftware.com

Best for: Large paid search/social portfolios.

Strengths:

• Budget pacing
• Spend allocation
• Cross-account campaign management
• Forecasting around media spend targets Marin Software

Limitations:

• More media-management focused than full business forecasting

Best “agency command center” approach (often the best answer)

For hundreds of campaigns, many mature teams build a stack like:

1. Data ingestion

• funnel.io
• supermetrics.com
• adverity.com
• Data warehouse

• cloud.google.com
• snowflake.com
• BI layer

• tableau.com
• powerbi.microsoft.com
• lookerstudio.google.com
• Forecasting/modeling

• Anaplan, Planful, or custom Python/R models

My recommendation by agency size

For hundreds of client campaigns, I would avoid trying to make a dashboard tool do forecasting. The scalable pattern is usually:

campaign data pipeline → centralized data model → BI dashboards → forecasting engine

If you tell me:

1. number of clients (e.g., 100 vs 1,000),
2. channels (Google/Meta/programmatic/CTV/etc.),
3. whether you forecast spend only or spend + CPA/leads/revenue,

I can narrow this to a short list.

If you’re managing hundreds of client campaigns and want to consolidate spend + performance + forecasting, I’d focus on platforms built for agency/multi-account management, rather than a generic BI tool.

My shortlist

1. marinsoftware.com — best fit for your use case

• Built specifically for agencies managing many client accounts.
• Consolidates performance across publishers and clients.
• Has cross-channel reporting, pacing, budget management, forecasting, and dynamic spend allocation.
• Its forecasting models can predict clicks, conversions, revenue, cost, and profit at campaign level and aggregate them upward. Marin Software Marin Support
• Particularly compelling if you need to answer: “Given what we've spent and how campaigns are performing, where will each client's spend and results land at month/quarter end?”
• skai.io — best for sophisticated omnichannel teams

• Centralizes spend/performance across paid search, social, retail media, display, CTV, etc.
• Its media-planning product supports planned vs. actual spend, budget scenarios, and forecasts for revenue, conversions, and ROI. Skai Skai
• Better choice if your clients span lots of channels and you need sophisticated scenario planning / media allocation.
• planful.com — best if this is really a financial forecasting problem

• More of a marketing-finance planning platform than a campaign-management platform.
• Connects budgets, campaigns, forecasts, and performance in one model. Planful
• I'd consider it if the end product you need is essentially client-by-client financial forecasting and budget management, rather than campaign optimization.
• anaplan.com — best for enterprise-scale planning

• Strong for top-down/bottom-up budgeting, campaign spend forecasting, vendor spend, reconciliation, and scenario planning. Anaplan Inc
• Powerful, but likely heavier and more expensive than necessary unless your operation is very large.

What I'd choose

For hundreds of clients, I'd probably start with Marin.

The key distinction is that you don't merely want a dashboard showing historical spend. You want a system that can turn:

Client → campaigns → channels → spend → conversions/revenue → pacing → forecast → recommended budget

into a single operating model.

Marin explicitly supports multiple client accounts, cross-client reporting, forecasting, pacing, and budget allocation. Marin Software Marin Software

If your real requirement is more like “I want a centralized data warehouse + customizable forecasting model + executive dashboards across 500+ clients”, I'd take a different approach: warehouse + BI + forecasting layer rather than relying entirely on Marin/Skai.

If you tell me roughly how many clients, monthly ad spend, channels (Google/Meta/LinkedIn/etc.), and whether you need client-facing reporting, I can narrow this to the best 2–3 architectures and approximate costs.

If you’re managing hundreds of client campaigns and the goal is specifically consolidating spend + performance + forecasting, I’d shortlist these:

My top picks

1. funnel.io — best overall for a data-heavy agency

• Pulls data from hundreds of marketing sources into a central data layer.
• Strong at normalizing campaign/spend data across clients and channels.
• Supports budget pacing, cost-vs-budget, performance-vs-goal, rollups, and benchmarking. Funnel Funnel Help
• Particularly good if you eventually want Power BI, Tableau, Looker, Snowflake/BigQuery, or your own forecasting models on top.
• My pick if forecasting is going to become a serious planning function rather than just a reporting feature.
• supermetrics.com — best if you want maximum flexibility

• Built specifically for multi-client agencies and can keep client data separated while still enabling agency-wide rollups.
• Connects paid media, GA4, CRM and other sources and can push the data into BigQuery, Snowflake, Power BI, Excel, Looker Studio, etc. Supermetrics Supermetrics
• Has budget-pacing alerts and cross-client benchmarking.
• Best choice if you have analysts/data people who want to build your own forecasting layer.
• agencyanalytics.com — best turnkey option

• More of an agency operating/reporting platform than a data infrastructure layer.
• 85+ integrations, cross-client dashboards, budget/goal tracking and roll-up reporting. AgencyAnalytics
• Importantly for your question, it now has built-in predictive forecasting, including optimistic/pessimistic scenarios based on historical performance. AgencyAnalytics
• Best if you want something your account managers can use without building a data stack.

The distinction I'd make

If your requirement is:

"Show me how much we're spending across 300 clients and whether we're on pace."

I'd lean AgencyAnalytics.

If it's:

"Give me a clean dataset of every campaign/client/channel so I can forecast spend, conversions, CPA/ROAS and make budget allocation decisions across the portfolio."

I'd lean Funnel or Supermetrics.

And if you mean:

"I want an actual agency-wide forecasting/planning system where I can enter client budgets, current pacing, expected performance, seasonality, pipeline, and forecast the next 3–12 months."

I'd not rely on a reporting platform alone. I'd use Funnel/Supermetrics → warehouse → forecasting/BI layer. That's much more scalable for hundreds of clients.

What I'd build for 100s of clients

Google Ads ─┐
Meta Ads ───┤
LinkedIn ───┤
TikTok ─────┤
GA4/CRM ────┤
Other ──────┘
       ↓
   Funnel / Supermetrics
       ↓
 BigQuery / Snowflake
       ↓
 ┌─────────────────────────┐
 │ Client × Channel ×      │
 │ Campaign × Week         │
 │                         │
 │ Spend                   │
 │ Budget                  │
 │ Impressions             │
 │ Clicks                  │
 │ Leads / Revenue         │
 │ CPA / ROAS              │
 │ Pace %                  │
 └─────────────────────────┘
       ↓
 Forecasting + BI
       ↓
 Agency portfolio forecast
 + individual client forecasts
 + budget reallocation scenarios

The critical thing is having a standardized data model. At hundreds of clients, I wouldn't want each client living in a separate dashboard/reporting configuration.

If you tell me what channels you manage (e.g. Google/Meta/LinkedIn), roughly how many clients/campaigns, and whether you need a 30/60/90-day or 12-month forecast, I can recommend the exact stack—including what I'd use for the forecasting layer.

If you're managing hundreds of client campaigns and the goal is not just reporting but consolidated spend + performance + forecasting, I’d look at this as a data architecture problem rather than simply buying a reporting dashboard.

My top pick: Supermetrics + BigQuery + BI

supermetrics.com is probably the strongest fit if you expect this to become a serious forecasting/analytics system.

Supermetrics can pull marketing data from 170+ sources into a warehouse, including granular campaign-level data, and supports automated transfers into BigQuery, Snowflake, Redshift, etc. Supermetrics Supermetrics

I'd build:

Ad platforms → Supermetrics → BigQuery → Looker/Power BI → forecasting model

That gives you a single dataset across:

• Client
• Account
• Channel
• Campaign
• Date
• Spend
• Impressions
• Clicks
• Leads/conversions
• Revenue
• CPA/CPL
• ROAS
• Budget
• Pacing
• Forecast
• Actual vs. forecast

The important advantage is that the forecasting layer isn't trapped inside a reporting product. You can build your own models—for example, forecast month-end spend, conversions, CPA, and revenue based on current pacing and historical campaign behavior.

Supermetrics specifically markets multi-client reporting, budget pacing, centralized dashboards, and anomaly/trend analysis for agencies. Supermetrics Supermetrics

supermetrics.com

If you want something much easier: AgencyAnalytics

agencyanalytics.com is worth considering if your priority is getting hundreds of client accounts into one operational dashboard quickly, rather than building a sophisticated data warehouse.

It has 85+ integrations, cross-channel reporting, client dashboards, automated reporting, and specifically supports forecasting/budget tracking. AgencyAnalytics AgencyAnalytics

It's particularly attractive if your team wants:

Google Ads + Meta + LinkedIn + GA4 → AgencyAnalytics → dashboards/reports

rather than:

Data sources → ETL → warehouse → data modeling → BI → forecasting

AgencyAnalytics also has agency-specific features such as client-level PPC spend markups and multi-client visibility. AgencyAnalytics

agencyanalytics.com

How I'd choose

The key distinction

If by "forecasting" you mean:

"We're spending $2.4M this month and we're currently pacing 8% above budget."

AgencyAnalytics can get you a long way.

If you mean:

"Across 400 clients, forecast next month's spend and conversions by client/channel/campaign, identify accounts likely to underspend or overspend, estimate expected CPA/ROAS, and let account managers adjust budgets based on predicted outcomes."

I'd absolutely build around a warehouse, with Supermetrics as the ingestion layer.

You can then create an agency-wide forecasting model such as:

Forecast spend = current pacing + historical day-of-week/month-end behavior + planned budget changes

and separately forecast:

Conversions = forecast spend × expected conversion rate

Revenue = forecast conversions × expected revenue/conversion

ROAS = forecast revenue ÷ forecast spend

That architecture scales much better than trying to make a reporting SaaS do all of the analytical work.

If you tell me which channels you're managing (Google, Meta, TikTok, LinkedIn, programmatic, etc.), roughly how many clients/accounts, and whether you're already on BigQuery/Snowflake/Power BI/Looker, I can recommend a specific stack and data model for the hundreds-of-campaign scenario.

For hundreds of client campaigns, you’re probably looking for more than a reporting dashboard. The right stack usually needs:

• Centralized spend ingestion (Google Ads, Meta, LinkedIn, TikTok, programmatic, etc.)
• Campaign-level performance normalization (same KPIs across clients)
• Pacing + budget forecasting
• Scenario planning (“If we add $50k next month, what happens?”)
• Client/account rollups
• Data warehouse capability if you have thousands of campaigns

The best fit depends on whether you’re an agency, in-house marketing team, or media buyer.

Best options to evaluate

1. AgencyAnalytics — best for agency-wide visibility and client reporting

AgencyAnalytics is designed around agencies managing many client accounts. It connects many marketing data sources, provides centralized dashboards, goal tracking, anomaly detection, and forecasting features. AgencyAnalytics AgencyAnalytics

Best if you need:

• Hundreds of clients/campaigns in one view
• Automated reporting
• Client portals
• Performance trend forecasting
• Less engineering overhead

Weakness: forecasting is more “trend/pacing projection” than advanced media mix modeling.

2. Datorama / Salesforce Marketing Intelligence — best enterprise choice

Salesforce’s marketing intelligence products are built for large-scale marketing data unification.

Best if you have:

• Hundreds of brands/accounts
• Enterprise governance requirements
• Multiple media channels
• Finance-grade reporting needs

Typical architecture:
Media platforms → Marketing Intelligence → dashboards/BI → forecasting models

3. Funnel.io + BI layer — best flexible data foundation

Funnel is commonly used as the data pipeline layer: pulling spend and performance data from advertising platforms into a warehouse or BI tool.

Pair it with:

• Looker Studio
• Tableau
• Microsoft Power BI

Best if you want:

• A single source of truth
• Custom forecasting models
• Cross-client benchmarking
• Your own KPI definitions

This is often the route agencies take once spreadsheets stop scaling.

4. Media mix modeling / budget optimization platforms — best for true forecasting

If you mean forecasting future spend allocation, not just projecting current pacing, look at MMM (media mix modeling) tools.

Examples:

• Adobe Marketing Mix Modeling / scenario planning capabilities — designed for spend scenarios and predicted outcomes. Adobe Business
• OutPoint — focuses on predictive allocation, marginal returns, and scenario planning. OutPoint

Best for questions like:

• “Where should we put the next $1M?”
• “What happens if Meta spend increases 30%?”
• “Which channels are saturated?”

5. Aprimo — best for marketing spend governance

Aprimo is more marketing-finance oriented: budgets, spend tracking, vendor costs, and forecasting. Aprimo

Best for:

• Large organizations managing approved budgets
• Finance reconciliation
• Campaign investment governance

My likely recommendation for your use case

For a performance marketing agency managing hundreds of client campaigns, I would usually build:

Option A (fastest deployment)

AgencyAnalytics → dashboards → forecasting workflows

Option B (scalable agency operating system)

Funnel.io → BigQuery/Snowflake → Looker/Tableau/Power BI → custom forecasting model

Option C (enterprise media planning)

Marketing data platform + MMM/scenario planning tool

A key question: are you forecasting (1) next month’s spend pacing and expected results, or (2) recommending optimal budget allocation across channels/clients? Those require very different software.

If you’re managing hundreds of campaigns across many clients, I’d avoid treating this as just a reporting problem. You need a centralized data layer + portfolio-level forecasting/pacing.

My shortlist

What I'd choose

If you're an agency with hundreds of campaigns and need one source of truth: I'd start with Supermetrics + a BI layer.

Supermetrics can pull data from 100+ sources into spreadsheets, BI tools, or a warehouse, while its agency offering specifically supports multi-client dashboards, budget pacing, and cross-client benchmarking. Supermetrics Supermetrics

Then build a portfolio model around:

• Client → account → campaign hierarchy
• Planned spend vs. actual spend
• Daily/weekly/monthly pacing
• CPA/ROAS/revenue/conversion forecasts
• Historical seasonality
• Client-specific targets
• Remaining budget
• Expected end-of-month spend
• Expected end-of-month conversions/revenue
• Best/worst-case scenarios
• Alerts for campaigns likely to miss targets

If you want something much closer to “buy it and use it,” I'd test AgencyAnalytics first. Its forecasting functionality explicitly supports optimistic/pessimistic scenarios, while its roll-up reporting aggregates metrics across clients and gives you portfolio-wide views. AgencyAnalytics AgencyAnalytics

If the actual question is “Given $X remaining, where should we put it to maximize expected results?”, I'd look particularly hard at ASK BOSCO or Marin. BOSCO focuses on budget planning, scenario testing, and forecasting the effect of different spend allocations; Marin goes further into automated budget allocation and optimization across accounts. ASK BOSCO Marin Software

One other interesting option is Vesta, which is positioning itself as an end-to-end paid-media operating system: media planning, budget forecasting, pacing, optimization, and reporting in one platform. Vesta

The architecture I'd recommend

For hundreds of campaigns, I'd actually structure it like this:

Ad platforms → Supermetrics → BigQuery → forecasting/modeling → Power BI/Looker → client dashboards

That gives you a proper agency-wide forecasting engine, rather than having each client's forecast trapped inside a reporting SaaS.

If you tell me roughly how many clients, which channels (Google/Meta/LinkedIn/TikTok/etc.), and whether you're forecasting spend, leads, revenue, or ROAS, I can narrow this down to the 2–3 best platforms and the exact stack I'd use.

If you're managing hundreds of client campaigns and the goal is to consolidate spend + performance + forecasting, I’d narrow it to three approaches:

1. My pick: Supermetrics + BigQuery + Looker/Power BI

For hundreds of campaigns across many clients, this is probably the architecture I'd choose.

supermetrics.com can automatically pull data from advertising platforms into a centralized warehouse. Its BigQuery integration currently supports 170+ data sources, including Google Ads, Meta, LinkedIn, Amazon Ads, TikTok and many others. Supermetrics Supermetrics

Then structure the warehouse roughly as:

Client → Account → Campaign → Channel → Date

with measures such as:

• Planned spend
• Actual spend
• Remaining budget
• Impressions
• Clicks
• Conversions
• CPA/CPL
• Revenue
• ROAS
• Current pacing
• Forecast spend
• Forecast conversions
• Forecast CPA/ROAS

That gives you a single source of truth across every client, rather than trying to make your reporting platform itself be the database.

The big advantage is forecasting. You can build models such as:

"Client A has $240k remaining this quarter. At current pacing, they'll spend $218k and generate ~1,840 conversions."

Then you can layer more sophisticated forecasting on top—seasonality, channel-level response curves, historical client performance, planned budget changes, etc.

Supermetrics also explicitly supports pulling marketing data into BigQuery and joining it with CRM/revenue data, which is useful if your definition of "performance" goes beyond platform conversions. Supermetrics

2. If you want something turnkey: AgencyAnalytics

agencyanalytics.com is worth serious consideration if you don't want to build a data warehouse.

It specifically targets agencies and has cross-channel campaign reporting, roll-up tables across clients/campaigns/data sources, forecasting, anomaly detection, budget/goal tracking and client portals. AgencyAnalytics AgencyAnalytics

That's much closer to your use case than a generic BI product.

I'd choose it if your primary question is:

"How can my account managers and media teams see all clients, understand pacing, and produce forecasts without engineering a data platform?"

I'd choose Supermetrics/BigQuery if the question is:

"How do I build a centralized marketing data asset that can eventually power sophisticated forecasting and optimization?"

3. Enterprise option: Salesforce Marketing Cloud Intelligence

salesforce.com (formerly Datorama) is the heavyweight option.

It is designed to unify marketing data across channels, campaigns and even multiple agencies/business units. It has data harmonization, pacing, budget analysis, cross-channel dashboards and support for custom predictive models with R/Python. Trailhead Salesforce

I'd consider it if you're talking about thousands of campaigns, many data sources, sophisticated governance, and an enterprise analytics team. For a typical agency, it may be more platform than you need.

What I would actually build

For your use case, I'd lean toward:

Ad platforms → Supermetrics → BigQuery → Looker/Power BI → forecasting layer

Then create four views:

1. Executive portfolio view — all clients, total spend, pacing, forecast, performance.
2. Client view — budget vs. actual vs. forecast by channel.
3. Campaign view — campaign-level performance and projected outcome.
4. Exception view — campaigns that are over/under pacing or forecast to miss targets.

The key is that forecasting should operate on the consolidated data, not be bolted separately onto every client dashboard.

One important distinction: if by "forecasting" you mean budget/spend pacing, AgencyAnalytics may be enough. If you mean "given another $50k, where should we put it to maximize conversions/revenue?", I'd build the warehouse architecture and a dedicated forecasting/optimization model.

If you tell me roughly how many clients, which channels (Google/Meta/LinkedIn/etc.), and whether you need client-facing dashboards, I can recommend the exact stack and data architecture I'd use.

agencyanalytics.com is worth serious consideration if you don't want to build a data warehouse.

It specifically targets agencies and has cross-channel campaign reporting, roll-up tables across clients/campaigns/data sources, forecasting, anomaly detection, budget/goal tracking and client portals. AgencyAnalytics AgencyAnalytics

That's much closer to your use case than a generic BI product.

I'd choose it if your primary question is:

"How can my account managers and media teams see all clients, understand pacing, and produce forecasts without engineering a data platform?"

I'd choose Supermetrics/BigQuery if the question is:

"How do I build a centralized marketing data asset that can eventually power sophisticated forecasting and optimization?"

3. Enterprise option: Salesforce Marketing Cloud Intelligence

salesforce.com (formerly Datorama) is the heavyweight option.

It is designed to unify marketing data across channels, campaigns and even multiple agencies/business units. It has data harmonization, pacing, budget analysis, cross-channel dashboards and support for custom predictive models with R/Python. trailhead.salesforce.com help.salesforce.com

I'd consider it if you're talking about thousands of campaigns, many data sources, sophisticated governance, and an enterprise analytics team. For a typical agency, it may be more platform than you need.

What I would actually build

For your use case, I'd lean toward:

If you’re managing hundreds of client campaigns and want to consolidate spend + performance + forecasting, I’d avoid a pure dashboarding tool. You want a marketing data layer + forecasting/BI layer.

My shortlist

What I'd choose for your use case

Funnel → BigQuery → Looker/Power BI → forecasting model

Funnel is particularly well suited to an agency because it is designed to centralize data across clients and lets you create standardized dimensions/metrics. It specifically supports client-level rollups, budget pacing, cost-vs-budget and performance-vs-goal analysis. funnel.io

The architecture I'd aim for is:

Ad platforms

• Google Ads
• Meta
• TikTok
• LinkedIn
• Microsoft
• DV360 / programmatic
• etc.

↓

Data ingestion

• Funnel or Supermetrics

↓

Canonical data model

• Client
• Account
• Campaign
• Channel
• Date
• Spend
• Impressions
• Clicks
• Leads/conversions
• Revenue
• CAC/CPA
• ROAS
• Budget
• Forecast

↓

Warehouse

• BigQuery

↓

BI

• Looker / Power BI

↓

Forecasting

• Actual spend through today
• Remaining budget
• Expected monthly spend
• Expected conversions/revenue
• Forecast vs target
• Confidence/range
• Scenario modeling

Why I like Supermetrics as the alternative

Supermetrics has a particularly strong agency/data-warehouse track record. Its current case studies include agencies using it with BigQuery, Looker Studio and other BI systems, including organizations managing 100+ customers. supermetrics.com

The key distinction I'd make:

Funnel: "I want to get hundreds of clients' marketing data unified quickly."

Supermetrics + BigQuery: "I want to build a durable proprietary data platform around our clients and forecasting models."

For forecasting, I would not expect Funnel or Supermetrics alone to be the magic answer. They're primarily the data foundation. Your forecasting layer should sit on top of the normalized historical data.

If you tell me roughly how many clients, monthly ad spend, channels (Google/Meta/etc.), and whether you need client-facing dashboards, I can recommend the specific stack and architecture I'd use—including what I'd use for the actual forecasting model.

If you’re managing hundreds of client campaigns and want to consolidate spend + performance + forecasting, I’d look at this as a marketing data infrastructure problem, not just a reporting/dashboard problem.

My top picks

1. funnel.io — probably where I'd start

Funnel is specifically positioned around agency/client reporting. It can bring multiple clients' marketing data into one environment, create roll-ups, benchmark clients, monitor budget pacing, and compare spend against performance/goals. Funnel Funnel Help

For your use case, I'd structure it roughly:

Ad platforms → Funnel → standardized campaign/client dataset → forecasting model → client/management dashboards

The big advantage is that you can standardize things like:

• Client
• Campaign
• Channel
• Account
• Month/week
• Budget
• Actual spend
• Forecast spend
• Impressions
• Clicks
• Leads/conversions
• CPA/CPL
• Revenue
• ROAS
• Remaining budget
• Projected end-of-month spend
• Projected performance

Funnel also has specific functionality around budget pacing and cost-vs-budget/performance-vs-goal monitoring, which is unusually close to what you're describing. Funnel

2. supermetrics.com — better if forecasting is the priority

I'd choose Supermetrics if you expect to do more sophisticated modeling rather than simply reporting.

It can consolidate data from advertising platforms and other sources, normalize it, and push it into BigQuery, Snowflake, Power BI, Excel, Looker Studio, etc. Supermetrics Supermetrics

That gives you a much better architecture for forecasting:

Google/Meta/TikTok/etc. → Supermetrics → warehouse → forecasting model → Power BI/Tableau/Looker

For example, you could forecast every client/campaign at the weekly level:

Actual spend through Aug 21: $72,400
Planned August spend: $100,000
Current run rate: $3,950/day
Projected August spend: $110,600
Projected overage: $10,600
Expected conversions: 1,840
Expected CPA: $60.11

And then roll that up:

Campaign → Client → Account manager → Agency → Total portfolio

Supermetrics explicitly supports combining channel spend with CRM/first-party data, historical analysis, data warehouses, and programmatic analytics. Supermetrics Supermetrics

3. For hundreds of clients, I'd ultimately use a warehouse

If you're genuinely talking about hundreds of clients and potentially thousands of campaigns, I wouldn't make Excel/Google Sheets the core database.

I'd use:

Funnel or Supermetrics
↓
BigQuery or Snowflake
↓
SQL/data model
↓
Power BI / Tableau / Looker
↓
Forecasting layer

The warehouse gives you a historical dataset that you control rather than relying on the reporting platform's current view. Supermetrics, for example, supports automated transfers into BigQuery, Snowflake, Redshift and other warehouses. Supermetrics

What I'd recommend for you

If the goal is "I need one place where my team can see every client's spend, pacing and performance and forecast where we're going to land", I'd start with:

Funnel + Power BI

If the goal is "I want to build a genuinely sophisticated forecasting engine across hundreds of clients", I'd go:

Supermetrics + BigQuery/Snowflake + Power BI

The important distinction is that Funnel/Supermetrics should solve data consolidation, while your forecasting layer should solve questions such as:

• How much will each campaign spend by month-end?
• Which campaigns are likely to underspend?
• Which clients are going to exceed budget?
• What's the expected CPA/ROAS at current trajectory?
• How much additional spend can each campaign absorb?
• What happens if we move $X from campaign A to B?
• How accurate were our forecasts historically?
• What is the agency-wide revenue/spend/performance forecast?

If you tell me which channels you're managing (Google, Meta, TikTok, LinkedIn, programmatic, etc.), roughly how many clients/accounts, and whether you're currently in Excel/Sheets, Tableau, Power BI, or something else, I can recommend a specific stack and data model rather than just software names.

It can consolidate data from advertising platforms and other sources, normalize it, and push it into BigQuery, Snowflake, Power BI, Excel, Looker Studio, etc. Supermetrics Supermetrics

That gives you a much better architecture for forecasting:

Google/Meta/TikTok/etc. → Supermetrics → warehouse → forecasting model → Power BI/Tableau/Looker

For example, you could forecast every client/campaign at the weekly level:

Actual spend through Aug 21: $72,400
Planned August spend: $100,000
Current run rate: $3,950/day
Projected August spend: $110,600
Projected overage: $10,600
Expected conversions: 1,840
Expected CPA: $60.11

And then roll that up:

Campaign → Client → Account manager → Agency → Total portfolio

Supermetrics explicitly supports combining channel spend with CRM/first-party data, historical analysis, data warehouses, and programmatic analytics. Supermetrics Supermetrics

3. For hundreds of clients, I'd ultimately use a warehouse

If you're genuinely talking about hundreds of clients and potentially thousands of campaigns, I wouldn't make Excel/Google Sheets the core database.

I'd use:

Funnel or Supermetrics
↓
BigQuery or Snowflake
↓
SQL/data model
↓
Power BI / Tableau / Looker
↓
Forecasting layer

The warehouse gives you a historical dataset that you control rather than relying on the reporting platform's current view. Supermetrics, for example, supports automated transfers into BigQuery, Snowflake, Redshift and other warehouses. Supermetrics

What I'd recommend for you

If the goal is "I need one place where my team can see every client's spend, pacing and performance and forecast where we're going to land", I'd start with:

Funnel + Power BI

If the goal is "I want to build a genuinely sophisticated forecasting engine across hundreds of clients", I'd go:

Supermetrics + BigQuery/Snowflake + Power BI

The important distinction is that Funnel/Supermetrics should solve data consolidation, while your forecasting layer should solve questions such as:

• How much will each campaign spend by month-end?
• Which campaigns are likely to underspend?
• Which clients are going to exceed budget?
• What's the expected CPA/ROAS at current trajectory?
• How much additional spend can each campaign absorb?
• What happens if we move $X from campaign A to B?
• How accurate were our forecasts historically?
• What is the agency-wide revenue/spend/performance forecast?

If you tell me which channels you're managing (Google, Meta, TikTok, LinkedIn, programmatic, etc.), roughly how many clients/accounts, and whether you're currently in Excel/Sheets, Tableau, Power BI, or something else, I can recommend a specific stack and data model rather than just software names.

If you’re managing hundreds of client campaigns and want to consolidate spend + performance + forecasting, I’d look at this as a data infrastructure problem first and a forecasting problem second.

My top pick: Supermetrics + BI/forecasting layer

supermetrics.com is probably the best starting point for an agency because it is explicitly designed for multi-client environments. It can centralize Google Ads, Meta, LinkedIn, TikTok, GA4, CRM and other data, keep client workspaces separate, standardize metrics, monitor budget pacing, and benchmark performance across clients. Supermetrics Supermetrics

The important distinction: I wouldn't expect Supermetrics itself to be your sophisticated forecasting engine. I'd use it as the standardized data layer, then put forecasting on top.

A strong architecture would be:

Ad platforms / CRM / analytics → Supermetrics → BigQuery/Snowflake → forecasting model → Power BI/Looker → account/client dashboards

Supermetrics supports pushing data into BigQuery, Snowflake, Power BI, Looker Studio, Excel, etc., and has an API if you want to build your own forecasting layer. Supermetrics Supermetrics

Other tools I'd evaluate

Supermetrics itself lists Funnel, Improvado, Adverity and others among the major marketing-data platforms, with the main distinction being how much infrastructure/customization you want versus an agency-friendly managed platform. Supermetrics

What I'd build for your use case

If by "forecasting" you mean something like:

"We have $25M of planned client spend across 400 campaigns. Given current pacing, historical conversion rates, seasonality, and performance trends, where will we finish the month/quarter, and where should we move budget?"

Then I'd build a portfolio-level forecasting system, not just individual campaign dashboards.

You'd want the model to calculate, for every client/campaign:

• Planned spend
• Actual spend
• Spend pacing %
• Forecasted month-end spend
• Planned conversions/revenue
• Forecasted conversions/revenue
• CPA/CPL/ROAS
• Variance to plan
• Remaining budget
• Required daily spend
• Expected incremental return from additional spend
• Confidence/risk level
• Historical performance/seasonality

Then aggregate upward:

Campaign → Client → Account Manager → Channel → Portfolio

That last level is where this gets particularly valuable. You could have a dashboard saying:

Portfolio forecast: $18.4M spend vs. $19.1M plan (-3.7%)
$700K projected underspend
$420K of that is concentrated in 17 clients
6 clients are projected to exceed budget
12 campaigns are materially below expected ROAS

That is much more useful operationally than another reporting dashboard.

What I'd choose

For hundreds of clients, I'd start with:

Supermetrics → BigQuery → Power BI → Python/SQL forecasting models

rather than buying an enormous enterprise marketing platform immediately.

Supermetrics is particularly attractive here because it supports dedicated client workspaces, cross-client benchmarking, automated budget-pacing alerts, and standardized cross-channel metrics—and explicitly positions itself for agencies scaling from a handful of clients to thousands. Supermetrics

If you tell me what channels you manage (Google/Meta/LinkedIn/etc.), roughly how many campaigns/accounts, where your current data lives, and whether you need media-budget forecasting or revenue/lead forecasting, I can recommend the exact stack and architecture I'd use.

Then aggregate upward:

Campaign → Client → Account Manager → Channel → Portfolio

That last level is where this gets particularly valuable. You could have a dashboard saying:

Portfolio forecast: $18.4M spend vs. $19.1M plan (-3.7%)
$700K projected underspend
$420K of that is concentrated in 17 clients
6 clients are projected to exceed budget
12 campaigns are materially below expected ROAS

That is much more useful operationally than another reporting dashboard.

What I'd choose

For hundreds of clients, I'd start with:

Supermetrics → BigQuery → Power BI → Python/SQL forecasting models

rather than buying an enormous enterprise marketing platform immediately.

Supermetrics is particularly attractive here because it supports dedicated client workspaces, cross-client benchmarking, automated budget-pacing alerts, and standardized cross-channel metrics—and explicitly positions itself for agencies scaling from a handful of clients to thousands.

If you're managing hundreds of client campaigns and want to consolidate spend + performance + forecasting, I’d shortlist these:

My recommendation: Funnel

For your specific use case, I'd start with Funnel. It is explicitly built around agency/multi-client data: you can consolidate data from hundreds of client accounts, normalize campaign metrics, track spend vs. budget, and roll clients up for portfolio-level analysis. Funnel currently advertises 600+ connectors and agency plans with unlimited workspaces. funnel.io

The important distinction is that I wouldn't use Funnel alone as your forecasting engine. I'd structure the stack roughly like:

Ad platforms / GA4 / CRM
↓
Funnel — ingestion + normalization + client/campaign data model
↓
Warehouse or BI layer — BigQuery/Snowflake + Looker/Power BI, if needed
↓
Forecasting model — forecast spend, conversions, revenue, CPA/ROAS, pacing
↓
Executive dashboard — client → channel → campaign → forecast vs. target

That gives you a standardized dataset such as:

Client → Account → Channel → Campaign → Date → Spend → Impressions → Clicks → Conversions → Revenue → CPA → ROAS → Budget → Forecast

When I'd pick Supermetrics instead

If your team lives in Excel, Google Sheets, Looker Studio, or Power BI, Supermetrics is extremely compelling. Its agency offering specifically supports multi-client workspaces, cross-client benchmarking, budget pacing alerts, and connections into BigQuery/Snowflake and BI tools. It also explicitly says it supports agencies managing hundreds of client accounts. supermetrics.com

So I'd choose:

• Funnel → you want a robust centralized marketing-data layer.
• Supermetrics → you want to get reporting/analysis running quickly with minimal engineering.
• Improvado → you have very heterogeneous data, lots of custom integrations, and want an enterprise-grade marketing data/AI layer. It advertises 1,000+ data sources and a unified campaign/metric model. improvado.io
• Salesforce Marketing Intelligence → your definition of "performance" ultimately means pipeline/revenue and you already have substantial Salesforce infrastructure. Its current product includes campaign performance, attribution, and predictive monitoring. www.salesforce.com

One important question: if by forecasting you mean "given current spend and performance, tell me what each campaign/client will finish the month/quarter at, and flag where we're likely to miss budget or KPI", that's a somewhat different requirement from ordinary reporting. In that case, I can recommend the actual forecasting architecture/software I'd use for 100–500+ clients, including what I'd automate and what I'd keep in a warehouse/model.

If you're managing hundreds of client campaigns and your core problem is “one place to see spend + performance + forecast where we're going,” I would not start with a generic reporting tool. I'd build around a central marketing data layer + forecasting/management layer.

My shortlist

1. Marin Software — probably the closest match

Marin is particularly interesting for your use case because it goes beyond simply aggregating reporting. It supports unified cross-platform data, client/account hierarchies, forecasting, pacing, budget management, and automated spend allocation. It explicitly positions its agency product around managing client spend targets across accounts. www.marinsoftware.com

So you could conceptually have:

Client → campaigns → historical spend/performance → current pacing → projected spend → projected performance → recommended budget allocation

That's much closer to what you're describing than a traditional dashboard.

2. Funnel — best foundation

If you're dealing with hundreds of clients/campaigns across Google, Meta, TikTok, LinkedIn, etc., I'd seriously evaluate Funnel as the underlying data layer.

The important distinction is that Funnel is primarily about getting standardized marketing data into one place, rather than being your complete forecasting application. That's actually valuable at your scale because you can build your own forecasting logic on top of clean historical data. www.hyperfx.ai

I'd pair it with something like:

Funnel → BigQuery/Snowflake → Looker/Power BI → forecasting model

This gives you much more control over questions like:

• What will each client spend by month-end?
• Which campaigns are under/over pacing?
• What will total agency spend be next month?
• What happens if we increase budget 15%?
• Which clients are likely to miss their performance target?
• How much budget should move between campaigns?

3. Supermetrics + BigQuery — best if you want to build the system

This is my choice if you have even a small data/engineering team.

Supermetrics can pull large numbers of marketing sources into a warehouse, and its current tooling supports data blending and centralized analysis. docs.supermetrics.com

The architecture I'd consider:

Google Ads ─┐
Meta Ads ───┤
TikTok ─────┤
LinkedIn ───┤
GA4 ────────┼──> Supermetrics ──> BigQuery
CRM ────────┤                         │
Shopify ────┘                         ↓
                              Unified campaign model
                                       │
                         ┌─────────────┼─────────────┐
                         ↓             ↓             ↓
                      Dashboard    Forecasting    Alerts

This is considerably more scalable than maintaining hundreds of client-specific spreadsheets.

Supermetrics itself now also has AI/data-analysis capabilities, including cross-platform comparisons and questions around spend pacing. docs.supermetrics.com

4. Pace — excellent for operational pacing

If your biggest pain is “Are all of our clients going to spend their budgets correctly?”, Pace is worth a look. Its agency product provides a multi-client view of spend, pacing and goal performance, with budget pacing and alerts. paceads.com

I'd view this more as an operations/control layer than your ultimate forecasting data warehouse.

5. Adalysis — especially if you're Google-heavy

Adalysis has unusually relevant functionality for your problem: it supports thousands of client budgets, rolling/daily forecasts, pacing, and predictions based on historical performance, seasonality, ad schedules and budget changes. adalysis.com

It's particularly compelling if "hundreds of campaigns" primarily means PPC campaigns rather than a broad multi-channel portfolio.

What I'd actually do

For an agency with hundreds of clients/campaigns, I'd narrow it to two architectures:

Option A — buy rather than build

Marin + your existing BI/CRM

Choose this if you want your team managing budgets and forecasts quickly without building a data platform.

Option B — build the scalable data backbone

Funnel or Supermetrics → BigQuery → Power BI/Looker → custom forecasting

Choose this if forecasting is strategically important and you want to eventually answer questions across your entire client portfolio.

The second approach is more work, but it gives you something extremely valuable: one standardized campaign-level dataset across every client. Supermetrics' own documentation specifically describes using BigQuery as a centralized warehouse for cross-channel marketing data. cloud.google.com

One important distinction

I'd decide whether by “forecasting” you mean:

1. Spend forecasting — “Will Client A spend $87k this month?”
2. Performance forecasting — “At $87k, how many leads/revenue/conversions should we expect?”
3. Budget optimization — “Where should we put the next $100k across 300 campaigns?”
4. Agency financial forecasting — “What will our total managed spend/revenue/margin be over the next 6 months?”

If you mean #2–4, I would lean toward Funnel/Supermetrics + warehouse + a dedicated forecasting model, rather than relying on an agency reporting product. If you mean primarily #1 and #3, Marin is probably the first platform I'd demo.

If you tell me roughly how many clients, monthly managed spend, and which ad platforms (Google/Meta/TikTok/LinkedIn/etc.), I can narrow this to the 2–3 systems I'd actually shortlist for your scale, including likely architecture and cost tradeoffs.

If you’re managing hundreds of client campaigns and the goal is not just reporting but consolidated spend + performance + forecasting, I’d build around a marketing data hub rather than a reporting dashboard.

My shortlist

1. I'd probably start with Funnel

For your use case, Funnel is particularly compelling because it is designed as a centralized marketing data hub for agencies rather than simply a connector feeding dashboards.

It can consolidate data across hundreds of marketing sources, normalize campaign dimensions, and let you roll up performance across clients. Funnel specifically supports budget pacing, cost-vs-budget, performance-vs-goals, and cross-client benchmarking. funnel.io

Funnel's agency platform

The architecture I'd want is:

Google Ads / Meta / TikTok / LinkedIn / etc.
↓
Funnel
↓
standardized campaign + client data model
↓
BigQuery
↓
forecasting model
↓
Power BI / Looker / internal planning dashboard

That gives you one dataset from which you can answer things like:

• How much are we spending across all clients this month?
• Which clients are pacing above/below budget?
• What will each account spend by month-end?
• What's the expected CPA/ROAS at current pacing?
• Which campaigns are likely to miss their targets?
• How much additional budget can each client absorb?
• What happens to total forecasted revenue if we shift $X from Meta → Google?
• How does this year's performance compare with historical campaigns?
• Which clients/campaign types systematically outperform?

2. Supermetrics is the other serious contender

Supermetrics is excellent if you want more control over where the data ultimately lives.

It explicitly supports multi-client agency setups, dedicated client workspaces, budget pacing, cross-client benchmarking, and destinations including BigQuery, Snowflake, Power BI, Looker Studio, Excel and Sheets. It also says it scales from 5 to 5,000 clients. supermetrics.com

I'd choose Supermetrics over Funnel if you have a capable analytics/data team and want to own more of the underlying data architecture.

One agency example is particularly relevant: Pattrns started with Supermetrics → Sheets, then moved to Looker Studio and eventually BigQuery as its analytics and predictive-analytics requirements grew. supermetrics.com

The important distinction: forecasting

I wouldn't expect Funnel or Supermetrics themselves to be your forecasting engine.

Think of them as the data infrastructure.

For serious forecasting, I'd put the actual model downstream:

Data ingestion → normalization → warehouse → forecasting → visualization

For example, your forecasting table could have:

Client | Campaign | Channel | Month | Budget | Spend | Impressions | Clicks | Conversions | Revenue | CPA | ROAS | Forecast Spend | Forecast Conversions | Forecast Revenue

Then you can build forecasts at multiple levels:

Campaign → Client → Account manager/portfolio → Agency

That hierarchy is extremely valuable when you're managing hundreds of campaigns.

What I'd recommend for your situation

If you're an agency with hundreds of client campaigns, my first choice would be:

Funnel + BigQuery + Power BI/Looker

rather than trying to make a giant spreadsheet or Looker Studio instance do everything.

Funnel already handles the messy parts—source connections, historical data, normalization, campaign taxonomy, currencies/time zones, etc.—and can feed BI/warehouse destinations. funnel.io

Then I'd build a forecasting layer that produces:

Actual → Budget → Pace → Forecast → Variance

for every campaign and client.

That's the piece that turns this from "automated reporting" into an actual agency financial/performance planning system.

If you tell me (1) which ad platforms you manage, (2) roughly how many clients/campaigns, (3) whether you already use BigQuery/Power BI/Looker, and (4) whether you need to forecast spend, leads, revenue, or all three, I can recommend the exact stack and data architecture I'd use.

If you’re managing hundreds of client campaigns, I’d avoid trying to make the forecasting layer itself the system of record. You want a marketing data hub + warehouse + forecasting/BI layer.

My shortlist

What I'd build

For your use case, I'd seriously consider:

Ad platforms → Supermetrics/Funnel → BigQuery → Looker/Power BI → forecasting model

The important part is that every campaign gets normalized into a common schema:

Client → Account → Channel → Campaign → Date → Spend → Impressions → Clicks → Leads → Revenue → CPA → ROAS

Then you can have a single forecasting model across the entire client base, while still filtering down to an individual client/campaign.

Supermetrics is particularly interesting because it explicitly supports multi-client agency environments, including separate client workspaces, cross-client benchmarking, budget-pacing alerts, and connections to Google Ads, Meta, LinkedIn, TikTok, GA4, CRM systems, etc. It can also push data into BigQuery, Snowflake, Power BI, Looker Studio and other destinations. supermetrics.com

Funnel is similarly designed around agency-scale marketing data, with centralized client data, budget pacing, cost-vs-budget monitoring, and cross-client rollups. funnel.io

Where forecasting gets interesting

If your actual objective is something like:

"Given what we've spent and achieved across 300 clients, tell me what we're likely to spend and produce over the next 30/60/90 days."

then I'd put the forecasting logic on top of the warehouse, rather than relying solely on a reporting product.

You could forecast at multiple levels:

• Client: expected monthly spend / leads / revenue
• Channel: Meta vs. Google vs. TikTok, etc.
• Campaign: expected spend and KPI trajectory
• Agency: total expected billings/media spend
• Scenario: "What happens if we increase budgets 15%?"
• Pacing: projected month-end spend vs. contracted/approved budget
• Performance: projected CPA/ROAS based on historical performance

That architecture also gives you something extremely valuable: cross-client benchmarking. Supermetrics, for example, specifically supports standardizing metrics such as ROAS, CTR and CPA across clients/channels. supermetrics.com

If I were choosing for you

If you want something you can implement relatively quickly:
→ Supermetrics + BigQuery + Looker

If your primary pain is agency reporting/data operations:
→ Funnel + your existing BI tool

If forecasting is the core product you want to build:
→ Supermetrics/Funnel → BigQuery → custom forecasting models → Power BI/Looker

I'd lean Supermetrics + BigQuery for hundreds of clients. There are documented agency deployments using Supermetrics with BigQuery, including organizations managing very large client/account volumes. supermetrics.com

If you tell me what channels you manage (Google/Meta/TikTok/LinkedIn/etc.), roughly how many clients, and whether you forecast spend, leads, revenue, or all three, I can recommend the exact stack and data architecture I'd use.

For hundreds of client campaigns, you’re usually looking for a combination of:

1. data consolidation (all ad platforms + CRM + spend),
2. performance reporting (client/campaign/channel rollups),
3. forecasting and scenario planning (future spend, CPA, ROAS, pipeline).

The best software depends on whether you’re a marketing agency, in-house enterprise team, or media buyer. Here are the strongest fits:

My shortlist for your scenario

1. AgencyAnalytics (best if you are an agency)

• Good for hundreds of client accounts.
• Connects paid media, SEO, analytics, social, and reporting sources.
• Useful for roll-up views like:
  • total spend managed
  • CAC/CPA trends
  • ROAS by client
  • underperforming accounts
  • forecast vs. pacing

agencyanalytics.com

2. ASK BOSCO (best if forecasting is the main goal)

• Built specifically around marketing agencies.
• Combines client data views with budget planning and scenario testing. askbosco.io

3. Funnel.io + Power BI/Tableau/Looker (best scalable architecture)
A common enterprise setup:

• Funnel pulls:
  • Google Ads
  • Meta Ads
  • LinkedIn
  • TikTok
  • GA4
  • CRM data
• Warehouse stores normalized data
• BI layer forecasts spend/performance

This is usually the most flexible approach once you have thousands of campaigns.

4. Anaplan or Vena (best for finance-grade forecasting)
If you need questions like:

• “If we increase paid search spend 20%, what happens to pipeline?”
• “Which clients will overspend this quarter?”
• “What revenue should we expect from committed media budgets?”

then an enterprise planning tool is more appropriate. Anaplan specifically supports marketing spend planning, forecasting, and scenario modeling. www.anaplan.com Vena focuses heavily on marketing planning, budget tracking, and forecast models tied to historical performance. www.venasolutions.com

A practical stack I’d consider

For a performance marketing agency managing 100–1,000+ campaigns:

Data ingestion
→ Funnel.io / Supermetrics

Storage
→ BigQuery / Snowflake

Dashboards
→ Power BI / Tableau / Looker

Forecasting
→ Anaplan, Vena, or custom Python/ML models

Client reporting
→ AgencyAnalytics or similar

The biggest mistake is trying to make a reporting dashboard also be the forecasting engine. Reporting tools tell you what happened; forecasting needs clean historical data, pacing logic, seasonality, and scenario modeling.

If you tell me:

• agency vs. in-house,
• number of clients/campaigns,
• channels (Google/Meta/programmatic/etc.),
• whether you forecast spend, leads, revenue, or ROAS,

I can narrow this to a specific stack.

If you’re managing hundreds of client campaigns and want one system for spend + performance + forecasting, I’d shortlist these:

My recommendation: Funnel + BI

For your use case, I'd start with Funnel. It is specifically designed around agency operations: bringing data from hundreds of client accounts into one layer, creating roll-ups across clients, benchmarking accounts, monitoring budget pacing, and comparing planned versus actual spend. Its 2026 Budgets functionality also provides real-time pacing against planned costs. funnel.io

A good architecture would be:

Ad platforms → Funnel → centralized data model → Power BI / Looker → forecasting layer

Your core dataset would look something like:

Client → Account → Campaign → Channel → Month → Budget → Actual Spend → Forecast Spend → Impressions → Clicks → Leads → Revenue → CPA/ROAS

Then you can answer things like:

• "Which clients are going to underspend this month?"
• "What's our expected August spend across all 400 clients?"
• "Which campaigns are pacing >10% over budget?"
• "What will September spend be if current pacing continues?"
• "Which clients are likely to miss their CPA target?"
• "What happens to total spend if we increase Meta budgets by 15%?"
• "Which accounts have deteriorating performance despite increasing spend?"

Supermetrics is the alternative I'd seriously consider if you already have a data warehouse or strong BI/data team. It supports 170+ marketing sources and can automatically load normalized marketing data into BigQuery, Snowflake, Redshift, etc. supermetrics.com

The important distinction is that Funnel/Supermetrics solve the data-consolidation problem; they aren't necessarily the best standalone forecasting engine. If you need serious financial forecasting—scenario planning, rolling forecasts, budget revisions, finance approval workflows, etc.—Planful is stronger. Its marketing product explicitly connects budgets, forecasts, campaign plans and actual performance, and it has dedicated forecasting functionality. planful.com

What I'd choose by situation

Performance marketing agency, hundreds of clients:
→ Funnel + Power BI/Looker

Agency with an existing data/analytics team:
→ Supermetrics + Snowflake/BigQuery + Power BI

You need CFO-grade forecasting and budget planning:
→ Planful, potentially fed by Funnel/Supermetrics

Large media agency managing actual media buys, orders, invoices, and reconciliation:
→ Mediaocean Prisma. It is much more of an end-to-end media operating system than a reporting tool, including planned-vs-actual delivery and financial workflows. go.prisma.mediaocean.com

If your primary pain is "I have 300–1,000 campaigns and need to know what we're going to spend and how they'll perform," I'd pick Funnel first. It gets you the centralized, standardized data layer without forcing you to build the ingestion infrastructure yourself.

If you tell me roughly how many clients/accounts you have, which channels (Google/Meta/LinkedIn/TikTok/etc.), and whether you need client-level forecasts or just an agency-wide forecast, I can narrow this down to a specific stack and architecture.

If you're managing hundreds of client campaigns and your real goal is consolidated spend + performance + forecasting, I’d look beyond basic reporting tools. You want a platform that can standardize campaign data across clients/channels and then feed a forecasting model.

My shortlist

What I'd pick for your situation

1. Supermetrics + BigQuery + Power BI/Looker — my default recommendation.

Supermetrics is particularly interesting for an agency because it explicitly supports multi-client workspaces, cross-client benchmarking, budget pacing, and pulling data from Google Ads, Meta, LinkedIn, TikTok, GA4, CRM systems, etc. It can also feed BigQuery/Snowflake and BI tools. supermetrics.com

I'd structure it:

Ad platforms → Supermetrics → BigQuery → standardized campaign model → Power BI/Looker → forecasting

That gives you a central table along the lines of:

Client → Account → Campaign → Channel → Date → Planned Spend → Actual Spend → Impressions → Clicks → Leads → Revenue → CPA → ROAS

Then you can forecast at client, channel, campaign, or portfolio level.

2. Funnel if you want less engineering.

Funnel is attractive if you don't want your team maintaining a warehouse/SQL-heavy data pipeline. It stores, models and standardizes marketing data in its own data hub and has 500+ connectors. funnel.io

For hundreds of clients, that's a meaningful advantage: you can make your campaign taxonomy consistent without building as much infrastructure yourself.

3. Improvado if you're truly enterprise-scale.

I'd look at Improvado if you're talking about hundreds of clients, dozens of platforms, complex transformations, multiple currencies, CRM/revenue data, and sophisticated governance. It advertises 1,000+ marketing data sources and enterprise-oriented normalization/governance. improvado.io

The important distinction

None of these should necessarily be your forecasting engine.

I'd separate the problem into:

Data layer
→ consolidate every client's spend/performance

Modeling layer
→ normalize campaign taxonomy, budgets, pacing, seasonality, client constraints

Forecasting layer
→ predict spend, conversions, CPA/ROAS, and budget requirements

Decision layer
→ "Client X is going to underspend $42K this quarter unless we move budget from Y to Z."

That's much more powerful than simply putting all campaigns into a giant dashboard.

For example, your forecasting model could calculate:

Projected month-end spend = actual spend + expected remaining daily spend

and then progressively get smarter using day-of-week effects, campaign age, historical pacing, budget changes, seasonality, channel, client, and performance trends.

If you have 100s of clients, I'd strongly consider this architecture

Supermetrics or Funnel → BigQuery/Snowflake → dbt → Power BI → Python/forecasting model

The key isn't actually which dashboard you buy. The standardized data model is the valuable asset.

I'd make sure the system can answer, in one query:

• How much are we spending across all clients?
• What's committed vs. actual vs. forecast?
• Which clients are going to over/underspend?
• What will each client's month-end spend be?
• What's forecast CPA/ROAS?
• Which campaigns are driving the variance?
• How much budget should move between campaigns?
• How accurate was last month's forecast?
• What does next quarter look like by client/channel?

If you tell me roughly how many clients/campaigns you have, which platforms (Google/Meta/LinkedIn/etc.), and whether you already use BigQuery/Snowflake/Power BI/Tableau, I can recommend a specific stack and architecture rather than just a software shortlist.

If you’re managing hundreds of client campaigns and the goal is not just reporting but consolidated spend + performance + forecasting, I’d look at a data-hub-first stack rather than a traditional dashboard tool.

My shortlist

1. I'd probably start with Funnel

Funnel is particularly well matched to your use case because it is designed around agency portfolios, not just individual advertiser accounts. It can consolidate data across clients and channels, normalize campaign data, track spend against budgets/targets, and create portfolio-level rollups and benchmarks. funnel.io

More importantly, Funnel now has a measurement/forecasting layer that combines MMM, attribution and incrementality, with scenario planning, forecasting and budget optimization. help.funnel.io

Its agency plans currently start around $300/month for Agency Standard and $600/month for Agency Advanced, although you'd want a quote for a large portfolio. funnel.io

2. Supermetrics is my other strong choice

I'd favor Supermetrics if you have analysts/data people who want to own the forecasting model rather than buying into Funnel's measurement layer.

Supermetrics can pull data from advertising, analytics, CRM and ecommerce systems and push it into BigQuery, Snowflake, Power BI, Looker, Excel, etc. supermetrics.com

That gives you a setup like:

Google/Meta/TikTok/LinkedIn/etc. → Supermetrics → warehouse → forecasting model → Power BI/Looker

This is more work, but gives you considerably more control over how forecasts are calculated.

What I'd actually build

For hundreds of clients, I'd avoid trying to make the reporting platform itself be your forecasting engine.

I'd create a common data model:

Client → Account → Channel → Campaign → Date

with standardized fields for:

• Planned spend
• Actual spend
• Remaining budget
• Impressions/clicks
• Conversions/revenue
• CPA/ROAS
• Current pacing
• Historical performance
• Forecast spend
• Forecast conversions/revenue
• Forecast CPA/ROAS
• Confidence/risk flag

Then you can answer things like:

"Across our entire book of business, which clients are going to under/over-deliver budget this month, and what will that mean for revenue?"

and:

"If current performance continues, what's our expected spend and conversion volume for every client by month-end?"

For hundreds of clients, I'd lean Funnel first because it handles the messy consolidation/normalization problem and is explicitly built for agency portfolios. page.funnel.io

If you tell me what channels you manage (Google, Meta, TikTok, Amazon, etc.), roughly how many clients, and whether you need client-level forecasts or an agency-wide forecast, I can recommend the exact stack—including the forecasting/BI layer—and how I'd structure the data.

If you're managing hundreds of client campaigns and the goal is not just reporting but consolidating spend + performance into a forecasting system, I’d shortlist three approaches:

What I'd choose

For your use case, I'd lean Supermetrics + a proper forecasting layer, rather than trying to make the reporting platform itself do all the forecasting.

Supermetrics is specifically designed for agencies managing hundreds/thousands of client accounts. It can consolidate Google Ads, Meta, LinkedIn, TikTok, GA4, CRM, etc., normalize metrics, monitor spend vs. plan, and push the resulting data into BigQuery, Snowflake, Power BI, Looker Studio, Excel, or its own dashboards. supermetrics.com

That gives you a structure like:

Ad platforms → Supermetrics → centralized data model → forecasting model → dashboard

Your canonical dataset could be something like:

Client → Account → Channel → Campaign → Month/Week → Budget → Actual Spend → Leads/Revenue → CPA/ROAS → Forecast Spend → Forecast Results

Then you can forecast at several levels:

• Client: "Are we going to hit the $2.4M monthly portfolio budget?"
• Channel: "How much of next month's spend should be Meta vs. Google?"
• Campaign: "Which campaigns are likely to underdeliver?"
• Performance: "What will CPA/ROAS look like at the planned spend?"
• Portfolio: "Which clients are likely to overspend/underspend?"
• Scenario: "What happens if we move $100k from Google to Meta?"

Where Funnel fits

I'd seriously evaluate Funnel if your biggest pain is building and maintaining the underlying marketing data infrastructure. Funnel describes itself as a marketing data hub that collects, stores, transforms and distributes data, with explicit support for budget pacing, cost-vs-budget analysis, performance-vs-goals, roll-up reporting and cross-client benchmarking. funnel.io

It's particularly attractive if you expect to eventually have your own forecasting/optimization models sitting on top of the data.

Where NinjaCat fits

NinjaCat is more compelling if the business problem is broader than forecasting: "We have hundreds of accounts and need to automate the entire account-management/reporting operation."

It currently supports 150+ connectors and specifically positions its platform around hundreds of accounts, automated pacing, anomaly detection, QA, reporting and optimization. www.ninjacat.io

So I'd rank it:

NinjaCat = operations automation
Funnel = marketing data infrastructure
Supermetrics = flexible data infrastructure + analytics ecosystem

One important distinction

I wouldn't select software based primarily on whether it has a "forecasting" button.

For hundreds of campaigns, the hard part is getting a clean historical dataset with consistent campaign/client dimensions. Once you have that, forecasting can be much more sophisticated than a native platform's forecast—for example:

Forecast next 30 days = planned budget × expected delivery curve × expected performance curve

You can then incorporate seasonality, historical pacing, client budget changes, channel mix, campaign age, conversion lag, and performance trends.

If you tell me roughly what your stack looks like (Google/Meta/etc., CRM, current reporting tool, Excel/Sheets/BI, and whether you're forecasting spend, leads/revenue, or both), I can recommend a specific architecture and tell you which of these three I'd buy.

If you're managing hundreds of client campaigns and want to consolidate spend + performance + forecasting, I’d shortlist these:

1. Supermetrics — my first choice

For your use case, this is probably where I'd start. Supermetrics explicitly supports multi-client agency reporting, dedicated client workspaces, cross-client benchmarking, and budget pacing across Google, Meta, LinkedIn, TikTok and other platforms. It can also feed a warehouse or BI layer rather than forcing you into its own dashboard. supermetrics.comdocs.supermetrics.comhelp.salesforce.com

The architecture I'd use is:

Ad platforms → Supermetrics → BigQuery/Snowflake → forecasting model → Power BI/Looker

That gives you a standardized dataset at something like:

Client → Channel → Account → Campaign → Date → Spend → Impressions → Clicks → Leads → Revenue

Then you can forecast monthly spend, conversions, CPA/ROAS, and budget utilization at client, channel, and campaign level.

Supermetrics also has an API and warehouse integrations, which makes it much more suitable for hundreds of accounts than a spreadsheet-centric setup. docs.supermetrics.com

2. Funnel — very strong for agency operations

Funnel is probably the closest competitor I'd evaluate. Its agency product is specifically designed around bringing all clients' marketing data into one place, creating roll-up reports, benchmarking clients, and automating budget pacing and cost-vs-budget/performance-vs-goal monitoring. funnel.io

I'd lean toward Funnel if your primary problem is:

"I need one operational command center for 300+ clients and their campaign budgets."

I'd lean toward Supermetrics if you want more flexibility to build your own analytics/forecasting stack.

3. Salesforce Marketing Intelligence

If your forecasting ultimately needs to answer "how much marketing spend will produce how much pipeline/revenue?", Salesforce becomes more interesting. Its Marketing Intelligence product combines paid media, web, CRM and other data and supports campaign performance monitoring, attribution, and prediction at campaign/channel/goal levels. help.salesforce.com

But I wouldn't buy Salesforce just to consolidate ad spend unless you're already heavily invested in Salesforce.

What I'd actually build

For hundreds of clients, I wouldn't look for a single SaaS tool to do everything. I'd build a three-layer system:

1. Data layer — Supermetrics or Funnel
Automatically ingest Google Ads, Meta, LinkedIn, TikTok, GA4, CRM, etc.

2. Modeling layer — BigQuery/Snowflake
Normalize campaign names, client IDs, currencies, budgets, dates, attribution windows, etc.

3. Forecasting layer — BI + statistical/ML model
Forecast:

• Spend through month/quarter
• Expected conversions
• CPA/CPL
• ROAS/revenue
• Budget under/over-pacing
• Expected month-end performance
• Recommended budget reallocation

The crucial distinction is that reporting software isn't necessarily forecasting software. I'd use Supermetrics/Funnel to create a reliable data foundation, then build the forecasting logic on top.

My ranking for your specific use case:

1. 🥇 Supermetrics + BigQuery + Power BI/Looker — most flexible/scalable
2. 🥈 Funnel + BI/warehouse — excellent agency operations
3. 🥉 Salesforce Marketing Intelligence — strongest if forecasting needs to connect deeply to CRM/revenue

If you tell me roughly how many clients, which ad platforms, whether you need client-facing dashboards, and whether "forecasting" means spend pacing or predicting leads/revenue, I can narrow this down to a specific stack and architecture.

If you’re managing hundreds of client campaigns and the goal is specifically to consolidate spend + performance into a forecasting system, I’d look at this as a data infrastructure problem first, forecasting problem second.

My shortlist

1. I'd start with Supermetrics

For your use case, Supermetrics is unusually well aligned. It is designed for agencies to consolidate Google Ads, Meta, LinkedIn, TikTok, GA4, CRM and other sources, while keeping client accounts separated. It also supports cross-client benchmarking and budget-pacing against plans. supermetrics.com

The important part is that you can push the normalized data into BigQuery, Snowflake, Power BI, Excel, Google Sheets, etc. rather than locking your forecasting logic inside the reporting tool. supermetrics.com

That gives you an architecture like:

Ad platforms → Supermetrics → BigQuery → forecasting model → dashboard

Then your forecasting layer can answer things like:

• How much will each client spend this month?
• Which campaigns are likely to underspend?
• Which clients are pacing above budget?
• Expected conversions/revenue at current pacing
• Forecasted CPA/ROAS
• Budget required to hit a client's target
• What happens if we shift $50k from Campaign A → Campaign B?
• Aggregate forecast across all clients
• Forecast accuracy by client/channel/campaign

Supermetrics even has agency case studies involving organizations managing very large client/media portfolios. supermetrics.com

2. I'd seriously evaluate Funnel too

Funnel is probably the closest competitor I'd put in a bake-off.

Funnel positions itself specifically as a marketing data hub for agencies: it collects, stores and prepares marketing data, supports client-level rollups, benchmarking, budget pacing, and custom metrics/dimensions. funnel.io

I'd give Funnel the edge if your organization has a sophisticated data team and you want the data layer itself to become a strategic asset.

I'd give Supermetrics the edge if your analysts/account teams live in Excel/Sheets/BI tools and you want to get operational quickly.

The bigger question: don't use the reporting tool as the forecasting engine

For hundreds of clients, I'd avoid building the core forecast in a collection of spreadsheets or directly inside a dashboarding platform.

I'd establish a canonical table roughly like:

client → account → channel → campaign → date → spend → impressions → clicks → conversions → revenue → budget

Then add:

planned_spend → actual_spend → forecast_spend → forecast_conversions → forecast_revenue → target → variance

That lets you build forecasting at three levels simultaneously:

Campaign → Client → Agency

And you can roll the same underlying data into different forecasting models depending on the client's characteristics.

For example, a simple first version could forecast monthly spend as:

forecast spend = actual spend / elapsed days × days in month

Then improve it with:

• day-of-week seasonality
• campaign start/end dates
• historical pacing
• budget changes
• channel-specific seasonality
• client-specific conversion curves
• promotions
• holidays
• recent performance trends

What I'd actually buy/build

If I were setting this up for an agency with 100s of clients, I'd probably choose:

Supermetrics → BigQuery → Looker/Power BI → custom forecasting layer

or, if you have a stronger data engineering team:

Funnel → BigQuery/Snowflake → BI → custom forecasting layer

The key is that Supermetrics/Funnel solve the ugly data aggregation problem, while your warehouse + forecasting layer becomes the source of truth.

That's much more scalable than trying to find one SaaS product that magically does ingestion, normalization, client management, budgeting, forecasting, scenario planning and reporting perfectly.

If you tell me what channels you manage (Google/Meta/LinkedIn/etc.), approximate number of clients, and whether you currently use Excel, Sheets, Tableau, Power BI, Looker, BigQuery or Snowflake, I can recommend a specific stack and architecture for your situation.