ChatGPT answers, unedited

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.