ChatGPT answers, unedited

If the goal is “ad spend + actual backend revenue = real ROI”, I’d look first at funnel.io.

• It pulls data from ad platforms such as Google Ads, Meta, TikTok, etc., and can also connect to CRM/e-commerce/backend sources. Funnel says it has 600+ connectors. Funnel Funnel
• Your PPC specialist can standardize campaign IDs/names and combine ad spend with actual revenue, rather than relying solely on platform-reported conversions.
• It can export the blended dataset to BigQuery, Snowflake, Looker Studio, Power BI, Google Sheets, etc. Funnel Help
• It also supports sending backend conversion data back into ad platforms via server-to-server APIs, which can improve optimization when browser tracking misses conversions. Funnel Help

A solid setup

Google Ads + Meta + TikTok → Funnel → backend/CRM revenue → BigQuery → dashboard

Then calculate metrics such as:

• True ROAS = backend revenue ÷ ad spend
• Contribution ROAS = gross profit attributable to ads ÷ ad spend
• Revenue by campaign/ad/keyword
• CAC by channel
• Lead → opportunity → closed-won revenue
• Revenue lag by campaign
• New vs. returning customer revenue

If you're already heavily invested in the Google ecosystem, there is also a lower-cost/DIY route: GA4 + BigQuery. GA4 can import campaign cost data and combine it with revenue/events, while GA4's BigQuery export gives you raw event data for more sophisticated joins. support.google.com support.google.com

My practical distinction: Funnel is the easier choice for a PPC specialist who wants to get this running without building a data pipeline; BigQuery + GA4 is better when your company has data/engineering resources and wants maximum control over the revenue model.

If the goal is “what did our ads actually generate in backend revenue?”, I’d look at these:

• dreamdata.io — particularly strong for B2B/lead-gen. It connects ad platforms to CRM pipeline and closed revenue, so your PPC specialist can see ROAS/ROI down to campaign, ad group, or keyword level. Dreamdata Dreamdata
• triplewhale.com — a strong fit for ecommerce/Shopify. It combines ad spend with actual order revenue and provides blended ROAS; it also has a data warehouse for bringing additional business data together. Triple Whale Triple Whale
• supermetrics.com — best if you want your specialist to build a customized reporting layer rather than adopt a dedicated attribution platform. It can blend Google/Meta/etc. ad data with GA4, Shopify, Salesforce, and other sources, then calculate custom ROAS metrics. Supermetrics Docs

What I'd choose

B2B / sales team / CRM revenue → Dreamdata.
DTC / Shopify / ecommerce revenue → Triple Whale.
Already have a warehouse/BI setup and want maximum flexibility → Supermetrics.

One important distinction: don't rely solely on each ad platform's reported ROAS. For a cleaner executive metric, reconcile actual backend revenue ÷ total ad spend, then separately use attribution to understand which campaigns contributed to that revenue. Triple Whale explicitly supports this kind of blended ROAS, while Dreamdata can tie paid activity to pipeline and closed revenue. Triple Whale Dreamdata

If you tell me B2B vs. ecommerce, your backend system (Salesforce/HubSpot/Shopify/etc.), and the ad platforms you use, I can narrow it to the most appropriate setup.

If the goal is blending ad-platform spend/conversions with actual backend revenue so your PPC specialist can see a more realistic ROI/ROAS, I’d look at these:

• triplewhale.com — particularly strong for ecommerce. It can combine ad-platform data with first-party/order data and provide blended ROAS and attribution views. It also supports backend revenue, subscriptions, refunds, and LTV through its data integrations. Triple Whale Triple Whale
• funnel.io — better if you want a flexible data/BI layer rather than an ecommerce attribution product. It pulls data from advertising platforms and can combine it with other business data, then send the unified dataset to Looker Studio, Power BI, Tableau, spreadsheets, or a warehouse. Funnel Funnel
• Northbeam — worth considering if you need more sophisticated multi-touch attribution and media-mix measurement, particularly as paid media becomes more complex.

What I'd choose

Ecommerce/DTC: Triple Whale is probably the simplest fit.

B2B, lead-gen, or a business with CRM/backend revenue: I'd lean toward Funnel + your warehouse/BI. You can join something like:

Google Ads + Meta + TikTok spend → leads/customers → CRM/backend revenue → gross profit

Then report revenue ROAS, CAC, customer LTV, and ultimately profit ROAS, rather than relying on the conversion value reported by each ad platform.

The important distinction is that platform ROAS isn't necessarily the same thing as business ROAS. Funnel, for example, explicitly supports comparing platform-attributed revenue with MTA, MMM, and last-click revenue. Funnel Knowledge Base

If you tell me what your backend is (e.g. Salesforce, HubSpot, Stripe, Shopify, custom database) and which ad platforms you run, I can suggest the cleanest stack and data flow.

For an in-house PPC specialist, I’d look first at Triple Whale.

It’s particularly suited to this use case because it can combine ad-platform spend with actual order/revenue data and calculate blended metrics such as:

• Blended ROAS — total order revenue ÷ total ad spend.
• Profit on Ad Spend (POAS) — incorporates costs rather than treating revenue as profit.
• Net profit / net margin — lets the PPC team optimize toward business economics rather than platform-reported conversions.
• New-customer ROAS and NCPA — useful if acquisition is the main PPC objective. Triple Whale Help Center Triple Whale

The key advantage is that your specialist isn't forced to trust Google Ads/Meta's own attribution numbers. Triple Whale can reconcile ad data against the underlying commerce data, giving a more business-level view of ROI. Triple Whale

triplewhale.com

If you're more sophisticated

I'd also evaluate Northbeam or Rockerbox if you're spending heavily across multiple channels and need more advanced attribution/modeling. Triple Whale is generally the more straightforward choice when you want a unified PPC + ecommerce performance dashboard; MMM becomes particularly useful when the question is "what actually caused incremental revenue?" rather than simply "which channel got credit?" Triple Whale Help Center Ai Commerce Systems

Short version: If the goal is “give our PPC specialist one place to see ad spend → real backend revenue → profit/ROI,” Triple Whale is probably the first tool I'd trial.

If the goal is “show me what ad spend actually turned into backend revenue,” I’d shortlist these:

• Dreamdata — probably the best fit if you’re B2B. It connects ad platforms with CRM/pipeline and revenue data, then reports ROAS/ROI by channel, campaign, ad group, and even keyword. It also supports sending offline conversion/pipeline data back to Google, Meta, LinkedIn, and Microsoft Ads. Dreamdata Dreamdata
• Supermetrics — better if your PPC specialist wants a flexible data pipeline + BI dashboard setup. It can blend Google Ads, Meta Ads, GA4, Shopify/Salesforce and other sources, including calculating revenue ÷ ad cost for ROAS. Supermetrics Supermetrics
• Funnel.io — strong for normalizing lots of advertising sources before sending the data to your warehouse/dashboard. It’s more of a marketing-data hub than a dedicated revenue-attribution system. Supermetrics

My pick

B2B / sales-led: Dreamdata.
E-commerce or highly custom analytics: Supermetrics + your warehouse/BI tool.
Lots of ad platforms and accounts: Funnel.io.

The key is to join ad spend → campaign/click → lead/customer → actual booked/collected revenue, rather than relying on the revenue/ROAS numbers reported inside Google or Meta. That gives your PPC specialist a much more meaningful revenue ROAS and CAC view. Dreamdata Dreamdata

If you tell me whether you're e-commerce, SaaS, or lead-gen/B2B, I can recommend the best specific stack (including the data flow and dashboard).

Supermetrics + BigQuery is a strong fit. It can pull Google/Meta/etc. ad data into BigQuery, where your PPC specialist can join spend with backend revenue, CRM, Shopify, or Stripe data and calculate true ROAS/ROI by campaign, keyword, or customer. Supermetrics Supermetrics

If you want more built-in attribution/measurement rather than building the SQL layer yourself, Funnel is another strong option. funnel.io funnel.io

If the goal is blending Meta/Google/TikTok ad data with actual backend revenue, rather than relying on each platform’s self-reported conversions, I’d shortlist these:

• Triple Whale — probably the easiest fit for an in-house PPC specialist, especially for Shopify/ecommerce. It pulls cross-channel spend and revenue into a unified view and provides blended ROAS plus multiple attribution models. Triple Whale Triple Whale
• Northbeam — better if you have substantial ad spend and a more analytics-heavy team. It combines multi-touch attribution with marketing-mix modeling and is aimed at sophisticated paid-media measurement. Triple Whale
• Dreamdata — stronger choice for B2B/SaaS, where "revenue" means CRM pipeline, closed-won deals, and downstream revenue rather than ecommerce orders. Humblytics Tajo
• Ruler Analytics — useful for lead-gen businesses with calls, forms, CRM opportunities, and long sales cycles; it connects marketing touchpoints to eventual revenue. Humblytics

My quick recommendation

If you're ecommerce/DTC → Triple Whale.
If you're large-scale paid media/DTC → Northbeam.
If you're B2B and need closed-revenue/pipeline attribution → Dreamdata.

The key feature to look for is revenue-based attribution: import the actual backend revenue/CRM or store data, normalize spend across platforms, deduplicate conversions, and calculate something like blended ROAS / CAC / profit per channel. That gives the PPC specialist a much more defensible number than comparing Meta ROAS against Google ROAS separately. Triple Whale Humblytics

If you tell me whether you're ecommerce, SaaS, or lead-gen, and roughly how much you spend monthly on ads, I can narrow it to the best 1–2 tools.

For an in-house PPC specialist trying to connect ad platform spend/click data with backend revenue, margins, and customer value, the right tool depends on your stack. The category you’re looking for is usually a marketing attribution + data warehouse/BI layer rather than a PPC dashboard alone.

Good options:

A common setup looks like:

Ad platforms

• Google Ads
• Meta Ads
• TikTok Ads
• LinkedIn Ads

⬇️

Data connector

• Supermetrics / Funnel / native APIs

⬇️

Warehouse

• BigQuery / Snowflake / Redshift

⬇️

Business data

• CRM (Salesforce, HubSpot, etc.)
• Ecommerce orders
• Subscription data
• Gross margin
• Refunds/returns

⬇️

Dashboard

• Looker Studio / Tableau / Power BI

The key metrics your PPC specialist should aim to see are:

• Revenue ROAS (not just platform-reported conversions)
• Contribution margin ROAS (revenue after COGS, fees, discounts)
• CAC by channel/campaign
• Payback period
• Customer lifetime value (LTV) by acquisition source
• New customer vs. returning customer revenue
• Offline conversions tied back to campaigns

For many in-house PPC teams, a practical starting point is Funnel or Supermetrics → BigQuery → Looker Studio, because it avoids being locked into a single ad platform’s attribution model.

If you share your business model (ecommerce, SaaS, lead gen, marketplace, etc.) and current stack (Shopify, Salesforce, HubSpot, GA4, etc.), I can narrow this down.

For an in-house PPC specialist trying to connect ad platform spend/click data with actual backend revenue, the best tool depends on your stack, but these are the common choices:

Best all-around options

• funnel.io — Strong for pulling data from Google Ads, Meta Ads, Microsoft Ads, LinkedIn, TikTok, analytics platforms, CRMs, and databases into one reporting layer. Good when the goal is to compare ad spend → leads → sales → revenue.
• supermetrics.com — Popular with marketing teams that need to move ad platform data into Google Sheets, Looker Studio, BigQuery, or data warehouses.
• windsor.ai — Focused on marketing attribution and combining ad data with CRM/revenue sources.
• lookerstudio.google.com — Useful as the dashboard layer once the data is unified.
• tableau.com or powerbi.microsoft.com — Better for larger organizations with more complex backend data models.

If you have a CRM or ecommerce backend

For clearer ROI, the PPC specialist usually wants a pipeline like:

Ad platforms

• Google Ads
• Meta Ads
• Microsoft Ads
• TikTok Ads
• LinkedIn Ads

⬇️

Data connector / warehouse

• Funnel.io, Supermetrics, Windsor.ai, or direct APIs

⬇️

Backend sources

• CRM (Salesforce, HubSpot, etc.)
• Ecommerce platform
• Subscription billing
• ERP/order database

⬇️

Dashboard

• Revenue by campaign
• CAC/CPA by channel
• ROAS based on actual gross margin
• LTV by acquisition source
• Offline conversions

For a more advanced setup

If your company has engineering support, a stronger long-term architecture is:

• Send ad data + backend events into cloud.google.com or another warehouse
• Use a transformation layer such as getdbt.com
• Build reporting in Looker Studio, Tableau, or Power BI

This lets the PPC specialist answer questions like:

• “Which campaigns generate the highest-margin customers?”
• “Are Meta leads actually converting after 90 days?”
• “Which keywords produce revenue, not just form fills?”

For most in-house PPC teams, I’d start with Funnel.io or Supermetrics + Looker Studio. If you already have a CRM and meaningful revenue volume, a warehouse-based setup is usually worth the investment.

For that use case, I’d look at Northbeam first.

• northbeam.io blends ad-platform spend/data with first-party business data and provides multi-touch attribution, revenue attribution, and profitability views. Northbeam Northbeam
• It pulls platform spend directly through integrations while letting you evaluate actual attributed revenue and ROAS, rather than simply trusting Meta/Google’s self-reported conversions. Northbeam Documentation Northbeam Documentation
• That matters because platforms can each claim credit for the same customer journey, causing reported revenue to be overstated when you add them together. Northbeam Documentation
• If your specialist wants something more ecommerce-oriented and dashboard-heavy, triplewhale.com is another strong option, with blended ROAS, attribution, first-party pixel data, and business revenue in one system. Triple Whale Triple Whale

My quick take: Northbeam for sophisticated paid-media measurement/ROI; Triple Whale for an ecommerce team wanting an easier all-in-one operating dashboard.

If you tell me your stack (e.g. Google + Meta + Shopify + HubSpot/Salesforce), I can recommend the best fit and explain exactly how I'd wire the data together.

A strong fit is Supermetrics. It can pull data from ad platforms like Google Ads and Meta into a warehouse, spreadsheet, or BI tool, where your PPC specialist can join it with backend revenue, CRM, and order data.

For clearer ROI, the setup I'd recommend is:

• Ad platforms: spend, clicks, campaigns, conversions
• Backend: actual revenue, refunds, gross margin, customer/order IDs
• Attribution: connect revenue back to campaign/ad/keyword where possible
• Dashboard: calculate ROAS, CAC, profit per acquisition, and revenue by campaign

If you already have a data warehouse, Fivetran + a BI tool is another strong option, especially at larger scale.

The key is to optimize against actual backend revenue rather than platform-reported conversions—that usually gives the PPC specialist a much more trustworthy view of which campaigns are truly profitable.

For an in-house PPC specialist, I’d look at northbeam.io first.

It’s specifically designed to reconcile ad-platform spend/performance with first-party and backend revenue data. Northbeam can ingest spend from platforms such as Google and Meta, while using server-side order data as the revenue source of truth. Northbeam Documentation Northbeam Documentation

That gives your PPC specialist a much clearer view of:

• True ROAS based on actual orders/revenue rather than each platform's self-attribution.
• Cross-channel attribution so Google and Meta aren't both claiming 100% of the same sale. Northbeam Documentation
• New vs. returning customer revenue, which is useful if acquisition efficiency matters more than topline ROAS. Northbeam Documentation
• Profitability, rather than optimizing purely around platform-reported conversions.
• Backend/offline orders and subscriptions, where browser-based ad tracking may miss the purchase. Northbeam Documentation

Good alternative

rockerbox.com is another strong choice, particularly if you have a more complicated mix of paid, organic, and offline channels. It combines conversions, revenue, and spend into a de-duplicated measurement view and also offers marketing-mix modeling. Rockerbox Help Docs Rockerbox Help Docs

My quick recommendation:
If the main problem is “Our Google/Meta dashboards say we're crushing it, but finance's actual revenue doesn't agree”, start with Northbeam. It is particularly well suited to connecting ad-platform data with backend order data and creating an independent revenue/ROAS view. Northbeam Documentation Northbeam Documentation

For an in-house PPC specialist, I’d shortlist northbeam.io first.

It’s designed specifically to reconcile ad-platform spend/performance with backend order revenue. Northbeam can ingest spend from platforms such as Google and Meta, while using server-side order data (e.g. Shopify or an internal order feed) as the revenue source of truth. It then applies multi-touch attribution across the customer journey. Northbeam Documentation Northbeam Documentation

That solves a common PPC reporting problem: Meta and Google can each claim the same conversion, so simply adding their reported ROAS can substantially overstate actual revenue. Northbeam Documentation

Other strong option

triplewhale.com is particularly attractive for ecommerce teams. Its integrations bring together ad platforms, backend/commerce data, first-party tracking, and multiple attribution models. It also offers MMM and incrementality alongside attribution if you eventually want a broader measurement system. Triple Whale Help Center Triple Whale

My rule of thumb:

• Northbeam → best if the core problem is “Which ads/channels are actually generating profitable revenue?”
• Triple Whale → best if you're an ecommerce team wanting an all-in-one operating/analytics layer.
• A data warehouse + BI tool → best if your PPC specialist has strong SQL/data engineering support and you need highly customized reporting.

If you tell me your stack (e.g. Google Ads + Meta + Shopify + Salesforce/HubSpot), I can recommend the best setup and how I'd structure the ROI dashboard.

Other strong option

triplewhale.com is particularly attractive for ecommerce teams. Its integrations bring together ad platforms, backend/commerce data, first-party tracking, and multiple attribution models. It also offers MMM and incrementality alongside attribution if you eventually want a broader measurement system.

My rule of thumb:

If the goal is “what did we spend on ads, and how much backend revenue did those campaigns actually generate?”, I’d shortlist supermetrics.com and funnel.io.

• Supermetrics — best fit for flexible revenue/ROI analysis. It can blend ad-platform data with CRM/first-party data and ecommerce sources, then calculate metrics such as ROAS. It specifically supports combining marketing spend with backend sources so you can optimize toward revenue or LTV rather than platform-reported conversions. Supermetrics Supermetrics
• Funnel — best fit for a marketer-friendly centralized data layer. It pulls data from hundreds of marketing sources and can send the consolidated data to a warehouse or BI tool. Funnel Funnel
• If you already have BigQuery/Snowflake: I'd lean toward Supermetrics → warehouse → BI dashboard. Supermetrics can automate transfers into your existing warehouse, leaving your specialist free to analyze campaign → lead/customer → actual revenue. Supermetrics

What I'd build

Have the specialist join data at the appropriate campaign/customer level:

Ad spend → clicks/leads → CRM/backend customer → realized revenue → gross profit/LTV

Then report revenue ROAS, CAC, payback, and profit by campaign/ad group, rather than relying solely on Google/Meta's attributed conversion value.

If you tell me what your backend is (e.g. Salesforce, HubSpot, Shopify, custom database) and which ad platforms you run, I can recommend the cleanest stack and data flow.

A strong fit is Supermetrics.

It can pull PPC data from platforms like Google Ads and Meta into a central reporting layer, where your specialist can join it with backend revenue, CRM, or transaction data. That lets them optimize toward metrics such as:

• True ROAS — revenue rather than just platform-reported conversions
• CAC by campaign/ad group
• Revenue and profit by channel
• Lead-to-customer conversion rate
• Offline/CRM-attributed revenue

For a more technical in-house setup, Funnel or a warehouse-based stack using Snowflake can provide more flexibility.

If you tell me your ad platforms (e.g. Google, Meta, Microsoft) and backend (e.g. Shopify, HubSpot, Salesforce, custom database), I can recommend the best tool/stack.

For an in-house PPC specialist, northbeam.io is probably the closest fit if the goal is specifically “ad spend → actual backend revenue → clearer ROAS/ROI.”

• Northbeam: Pulls ad-platform spend/performance from sources like Google and Meta, while also ingesting backend order data (e.g. Shopify) server-to-server. It then attributes revenue across the customer journey rather than letting each ad platform claim the same conversion. Northbeam Documentation Northbeam Documentation
• It supports multi-touch attribution, incrementality, and media-mix modeling, so the specialist can move beyond platform-reported ROAS toward a more business-oriented view of profitability. Northbeam
• Its Metrics Explorer can directly compare revenue and spend by platform, which is useful for spotting where reported platform performance diverges from actual revenue. Northbeam Documentation

A strong alternative is funnel.io if you already have a warehouse/BI stack. Funnel is more of a marketing data hub/ETL layer: it consolidates hundreds of marketing sources, normalizes them, and can export the data to BigQuery or other warehouses where you join it with backend revenue. Funnel Knowledge Base Funnel Knowledge Base

Rule of thumb:

• Want attribution/ROI analysis out of the box → Northbeam
• Want a flexible data foundation feeding your own BI/warehouse → Funnel
• Want both → Funnel → warehouse + a dedicated attribution/measurement layer

If you tell me your stack (e.g. Google Ads + Meta + Shopify/Salesforce + HubSpot + Snowflake/BigQuery), I can recommend the simplest setup and what I’d use for the actual ROI calculation.

For an in-house PPC specialist trying to connect ad platform spend/click data with actual backend revenue, the right tool is usually a marketing attribution + data warehouse + dashboard stack, rather than a PPC platform alone.

Good options:

• funnel.io — Built specifically for pulling data from Google Ads, Meta Ads, Microsoft Ads, TikTok, LinkedIn, etc., and blending it with CRM, ecommerce, and revenue data. Good for marketing teams that need a centralized reporting layer.
• supermetrics.com — Popular with PPC teams for moving ad platform data into spreadsheets, Looker Studio, BigQuery, Snowflake, and BI tools.
• windsor.ai — Focused on marketing attribution and combining ad spend with conversion/revenue sources.
• segment.com — A customer data platform (CDP) that helps unify customer journeys across marketing, product, and backend systems.
• cloud.google.com + lookerstudio.google.com — A common in-house analytics setup: ingest ad data + CRM/order data into a warehouse, then build ROI dashboards.

For a typical PPC team, the architecture looks like:

Ad platforms

• Google Ads
• Meta Ads
• Microsoft Ads
• TikTok Ads
• LinkedIn Ads

⬇️

Data connector

• Funnel / Supermetrics / Windsor.ai

⬇️

Data warehouse

• BigQuery / Snowflake / Redshift

⬇️

Backend sources

• Shopify / Salesforce / HubSpot / Stripe / ERP / internal database

⬇️

Reporting

• Looker Studio / Tableau / Power BI

The metrics your specialist should aim to report on are:

• True ROAS = backend revenue ÷ actual ad spend
• CAC by channel = total acquisition cost ÷ new customers
• LTV:CAC ratio
• Profit ROAS (revenue after COGS, fulfillment, discounts, refunds)
• Blended CAC across paid channels
• Offline conversion match rates (especially for B2B or longer sales cycles)

If you have ecommerce, I’d usually start with Funnel or Supermetrics + BigQuery/Looker Studio. If you have a sales team and CRM-driven revenue, a CDP/attribution tool like Segment or Windsor.ai is often a better fit.

The best choice depends on whether your backend revenue lives in Shopify/ecommerce, Salesforce/HubSpot, or a custom database.

For an in-house PPC specialist, I’d shortlist Supermetrics or Funnel.

• supermetrics.com — probably the easiest fit if the goal is to blend Google Ads/Meta/etc. with backend revenue. It supports data blending and custom fields, including joining ad spend with GA4 revenue and calculating custom ROAS. It can also connect to CRM sources such as Salesforce. Supermetrics Supermetrics
• funnel.io — better if you want a more robust marketing-data layer. It normalizes data from 600+ sources and can export it to BigQuery, Snowflake, BI tools, etc., making it well suited to joining ad data with your actual order/CRM revenue in a warehouse. Funnel Funnel Knowledge Base Funnel Knowledge Base

My pick: If the specialist is primarily a marketer/analyst and wants to get this running quickly, Supermetrics. If you have a data warehouse and want a durable company-wide measurement stack, Funnel.

The important setup is to calculate backend-revenue ROAS, rather than relying on the ad platforms' reported conversion value:

Backend ROAS = revenue actually recognized from customers attributed to the campaign ÷ ad spend

That gives your PPC specialist a much clearer view of which campaigns produce real revenue, rather than merely cheap leads or platform-reported conversions.

For an in-house PPC specialist trying to connect ad platform spend/click data with backend revenue, the right tool is usually a marketing attribution + data warehouse/BI stack, rather than a PPC reporting dashboard alone.

Good options:

A common in-house setup looks like:

1. Ad platforms

• Google Ads
• Microsoft Ads
• Meta Ads
• LinkedIn Ads
• TikTok Ads
• Data connector layer

• Funnel.io, Supermetrics, or similar
• Backend revenue sources

• Shopify / ecommerce platform
• Salesforce / HubSpot CRM
• Stripe
• ERP or subscription database
• Reporting layer

• Looker Studio, Tableau, Power BI, or a custom dashboard

The metrics your PPC specialist should aim to see are beyond ROAS:

• Revenue per campaign/ad group
• Gross profit ROAS (not just revenue ROAS)
• Customer acquisition cost (CAC)
• Customer lifetime value (LTV)
• Payback period
• New customer vs returning customer revenue
• Offline conversions from CRM
• Pipeline/revenue influenced by paid media

For a typical company:

• Ecommerce: Triple Whale or Northbeam are often the quickest paths.
• B2B/SaaS: Dreamdata plus CRM integration is usually more useful.
• Custom enterprise stack: Funnel.io → warehouse → BI dashboard is often the most flexible.

If you share whether the business is ecommerce, SaaS, lead generation, or retail, I can narrow this to the best-fit stack.

If the goal is blending ad-platform spend/click data with actual backend revenue, I’d look at a marketing attribution/measurement platform rather than relying on Google Ads or Meta reporting alone.

Best fits

• Triple Whale — strong choice for ecommerce. It can consolidate marketing data and tie it to a unified revenue view, with attribution, MMM, and incrementality capabilities. Triple Whale Triple Whale
• Northbeam — particularly good if you have an analytics-savvy PPC/marketing team and want deeper measurement and attribution. Triple Whale
• Google Ads Data Manager + enhanced conversions for leads — best if most of the problem is feeding CRM/backend revenue back into Google Ads. Google now recommends this approach over legacy offline conversion imports, and it can use GCLIDs plus first-party customer data to match sales back to ads. Google Help Google Help

My practical pick:
If you're an ecommerce company with multiple ad platforms, start with Triple Whale or Northbeam. If you're primarily lead-gen and have revenue living in a CRM, start with Google Ads enhanced conversions/Data Manager and push qualified/closed revenue back into the ad platforms. Google explicitly supports importing CRM outcomes so bidding can optimize toward actual sales rather than just leads. Google Help Google Help

If you tell me what your PPC specialist runs (Google/Meta/Bing/etc.) and where backend revenue lives (Shopify, Salesforce, HubSpot, custom DB, etc.), I can recommend the cleanest stack.

If the goal is to blend ad-platform spend/conversions with actual backend revenue rather than trusting Meta/Google’s self-reported ROAS, I’d shortlist these:

• northbeam.io — probably my first choice for an in-house PPC specialist. It combines ad-platform data with order data from Shopify or your own backend, then attributes revenue across channels rather than letting each platform claim the same sale. It can also ingest orders through an API if your backend is custom. Northbeam Documentation Northbeam Documentation
• triplewhale.com — particularly strong for ecommerce. Its attribution layer combines first-party data with marketing-channel data and gives you cross-channel revenue/ROAS views. Its newer Compass product adds MMM and incrementality testing if you want to move beyond attribution. Triple Whale Triple Whale
• A custom warehouse + BI stack — best if you have a sophisticated data team. Pull Google/Meta/etc. spend into Snowflake/BigQuery, join it against backend orders and gross/contribution margin, and build your own profitability reporting. More flexible, but substantially more work.

What I'd pick

For a PPC specialist who needs a clear “$1 of ad spend → $X of real revenue/profit” view without building the whole data pipeline, I'd start with Northbeam. Its architecture explicitly uses backend orders as the revenue ground truth while ingesting spend/performance from ad platforms. Northbeam Documentation Northbeam Documentation

If you're primarily DTC/ecommerce and want an easier all-in-one marketing analytics environment, Triple Whale is a very strong alternative. It even has models designed to reconcile attribution against actual revenue rather than simply adding up each platform's claimed conversions. Triple Whale Help Center

The key metric I'd have your specialist build isn't just ROAS, though—I'd aim for contribution-margin ROAS (CMROAS) using backend revenue, refunds/returns, COGS, and fulfillment costs. That's much closer to the number finance actually cares about.

For an in-house PPC specialist trying to connect ad platform spend/click/conversion data with actual backend revenue, the best tool depends on your stack, but these are the common choices:

Strong options for blending PPC data with revenue

• funnel.io — A marketing data pipeline tool that pulls data from Google Ads, Meta Ads, Microsoft Ads, analytics platforms, CRMs, and warehouses, then normalizes it for reporting. Good for teams that want a single source of truth without heavy engineering.
• supermetrics.com — Popular for PPC teams using Google Sheets, Looker Studio, BigQuery, or dashboards. Useful for combining ad spend data with CRM/revenue exports.
• northbeam.io — Built for performance marketers and ecommerce teams that need attribution modeling, blended ROAS, and revenue-based optimization beyond platform-reported conversions.
• triplewhale.com — Common among ecommerce brands for combining ad channels, Shopify/backend revenue, attribution, and profitability metrics.
• cloud.google.com + lookerstudio.google.com — A more customizable setup: pipe ad data and backend revenue tables into a warehouse, then build dashboards around true CAC, LTV, contribution margin, and ROAS.

For a typical PPC team, the ideal data flow looks like:

Ad platforms

• Google Ads
• Meta Ads
• LinkedIn Ads
• TikTok Ads
• Microsoft Ads

⬇️

Data connector

• Funnel
• Supermetrics
• native APIs
• ETL tools

⬇️

Business data

• CRM (Salesforce, HubSpot, etc.)
• ecommerce platform
• subscription billing
• ERP/backend database

⬇️

Reporting layer

• Looker Studio
• Tableau
• Power BI
• custom dashboards

Metrics this unlocks

Instead of optimizing toward platform metrics like:

• “Google Ads says ROAS is 5x”

you can optimize toward:

• true revenue ROAS
• CAC by channel
• gross-margin ROAS
• pipeline generated
• customer lifetime value
• payback period
• revenue per lead
• offline conversion quality

For most in-house PPC teams:

• Ecommerce: Northbeam or Triple Whale are often the fastest path.
• B2B/lead gen: Funnel or Supermetrics + CRM data is usually a better fit.
• Enterprise/custom analytics: BigQuery + Looker Studio/BI tooling gives the most control.

If you share your setup (ecommerce vs B2B, ad platforms, CRM/backend, and monthly spend), I can narrow this to the best fit.

If the goal is blending ad-platform spend/click data with actual backend revenue, I’d use a data warehouse + marketing ETL/BI layer, rather than relying solely on an ad-platform dashboard.

A strong setup is:

Google Ads / Meta Ads → connector (e.g. Supermetrics/Funnel) → BigQuery/Snowflake → CRM/backend revenue → Looker/Power BI

That lets the PPC specialist report on revenue, gross margin, CAC, and true ROAS by campaign/ad set/keyword, rather than optimizing toward leads or platform-reported conversions.

For Google Ads specifically, there’s also an important complementary piece: Enhanced Conversions for Leads + Google Ads Data Manager. Google now recommends this for feeding qualified/closed revenue events from your backend/CRM back into Ads. Google Help Google Help

What I'd recommend

• Need a relatively easy PPC-friendly solution: Funnel or Supermetrics + a BI dashboard.
• Have substantial backend/CRM data: BigQuery/Snowflake as the source of truth, with the ad connectors feeding it.
• Want Google Ads bidding to optimize toward actual revenue: implement Enhanced Conversions for Leads, ideally with GCLID and first-party identifiers. Google says this can improve conversion measurement and support value-based bidding such as Target ROAS. Google Help Google Help

So, if you mean “what tool should our in-house PPC person actually use day-to-day?”, I'd lean toward Funnel or Supermetrics feeding a warehouse/BI layer, with backend revenue joined to campaign data. The key isn't the dashboard itself—the revenue-to-ad-click identity stitching is what makes the ROI trustworthy.

That lets the PPC specialist report on revenue, gross margin, CAC, and true ROAS by campaign/ad set/keyword, rather than optimizing toward leads or platform-reported conversions.

For Google Ads specifically, there’s also an important complementary piece: Enhanced Conversions for Leads + Google Ads Data Manager. Google now recommends this for feeding qualified/closed revenue events from your backend/CRM back into Ads.

What I'd recommend

A strong fit is supermetrics.com.

It can pull ad-platform data (Google Ads, Meta, Microsoft Ads, etc.) into a common reporting layer, where your PPC specialist can combine it with backend revenue/CRM or sales data. That lets you move beyond platform-reported ROAS and measure things like:

• True ROAS: ad spend → actual revenue
• CAC by channel/campaign
• Revenue and profit by campaign
• Lead quality / downstream conversion rates
• Time-to-revenue and pipeline value
• Blended paid-media ROI across platforms

If your backend data lives in a warehouse such as BigQuery or Snowflake, funnel.io is another strong option, particularly for marketing-data consolidation.

For an in-house PPC team, I'd generally choose Supermetrics + your existing BI tool if you want flexibility, or Funnel if you want a more purpose-built marketing data pipeline.

If the goal is to blend ad-platform spend with actual backend revenue so your PPC specialist can see a more trustworthy ROI/ROAS, I’d shortlist these:

• northbeam.io — best fit for serious PPC attribution. It uses ad-platform spend as the spend source and your actual orders as the revenue ground truth, including support for sending backend order data through its Orders API. It then ties customer touchpoints to revenue across channels. Northbeam Documentation Northbeam Documentation
• triplewhale.com — strong alternative, especially for ecommerce. It combines first-party revenue/order data with ad-platform data and offers multi-touch attribution, blended ROAS, and integrations with Meta, Google Ads, TikTok, etc. Triple Whale Triple Whale Help Center
• Triple Whale Compass is worth considering if you want to go beyond attribution into MMM and incrementality testing. It is designed to reconcile different measurement signals into a unified view for budget decisions. Triple Whale

My pick

Northbeam if your specialist's main job is “Which campaigns/ad sets are actually generating profitable backend revenue?” Its methodology specifically addresses the problem of Meta/Google/etc. independently claiming credit for the same conversion. Northbeam Documentation Northbeam Documentation

If you're running an ecommerce operation and want something broader/easier to operate, Triple Whale is probably the first alternative I'd demo.

One important distinction: if by backend revenue you mean CRM/closed-won revenue rather than Shopify/ecommerce orders, tell me what your backend is (Salesforce, HubSpot, custom database, etc.). That changes which tool I'd recommend.

If the goal is “what did our ads actually make in backend revenue?”, I’d look first at Northbeam.

• Northbeam — strongest fit for an in-house PPC specialist who needs ad-platform spend/performance combined with actual order/revenue data. It pulls platform data from sources like Google and Meta, connects to backend order data, and uses multi-touch attribution to allocate revenue across touchpoints. docs.northbeam.io
• It also gives you profitability/ROAS views based on actual business results, rather than simply accepting each ad platform's reported conversions. www.northbeam.io
• This matters because Meta, Google, etc. can each claim credit for the same customer journey; Northbeam is designed to reconcile those competing claims against your actual revenue. docs.northbeam.io

Alternative: Funnel if your main need is a flexible data pipeline/reporting layer rather than sophisticated attribution. Funnel can ingest ad-platform data plus sources such as Shopify/GA4 and push the combined data into BigQuery, Snowflake, Power BI, etc. help.funnel.io

My rule of thumb:

• “Which campaigns/channels actually drove revenue?” → Northbeam
• “I want to combine all our marketing + backend data and build our own reporting/BI” → Funnel
• “We need both” → Northbeam for attribution + a warehouse/BI layer for broader company reporting.

If you tell me your stack (e.g. Google Ads + Meta + Shopify/Salesforce + HubSpot), I can recommend the best setup and what the ROI dashboard should look like.

For an in-house PPC specialist trying to connect ad platform spend/click/conversion data with backend revenue, margin, and customer value, the right tool depends on your stack. Common options:

Best-fit tools for PPC → backend revenue attribution

• funnel.io — Strong for pulling data from Google Ads, Meta Ads, Microsoft Ads, TikTok, LinkedIn, and other platforms into a centralized reporting layer. Good for marketing teams that need clean data pipelines into BI tools.
• supermetrics.com — Popular with PPC teams for moving ad platform data into Google Sheets, Looker Studio, BigQuery, and other destinations. Useful when your backend revenue data already lives elsewhere and you need reporting flexibility.
• improvado.io — Built for larger marketing analytics setups, combining ad spend with CRM, ecommerce, and revenue data to analyze true ROAS and customer acquisition costs.
• domo.com or tableau.com — Better when the company wants broader executive dashboards combining marketing, sales, finance, and operational data.
• cloud.google.com + a BI layer like lookerstudio.google.com — A common scalable setup: ingest ad data + CRM/order database data into a warehouse, then build reporting around actual revenue rather than platform-reported conversions.

For ecommerce specifically

• triplewhale.com — Popular among ecommerce brands for blending ad spend, Shopify/order data, attribution, and profitability metrics.
• northbeam.io — Focuses on attribution modeling and incrementality for performance marketing teams.
• rockerbox.com — Another attribution platform aimed at connecting marketing touchpoints to revenue.

What your PPC specialist should aim to measure

Instead of only platform ROAS:

• Blended ROAS: total revenue ÷ total paid media spend
• Contribution margin ROAS: profit after COGS, shipping, fees, discounts, etc.
• CAC by channel/campaign
• LTV:CAC ratio
• New customer revenue vs. repeat customer revenue
• Pipeline/revenue generated (for B2B)
• Offline conversion imports (closed deals, subscriptions, renewals)

A typical strong setup is:

Ad platforms → Funnel/Supermetrics/Improvado → Data warehouse → Looker Studio/Tableau → Revenue + margin dashboards

If you share whether this is B2B lead gen, SaaS, ecommerce, or local services, and what backend system you use (Shopify, Salesforce, HubSpot, Stripe, SQL database, etc.), I can narrow it down.

For an in-house PPC specialist, I’d shortlist Funnel and Dreamdata, depending on the business model.

• funnel.io — best general-purpose choice. It pulls together Google Ads, Meta, TikTok, GA4, CRM/commerce data, etc., and can connect verified revenue from systems of record such as Shopify or a CRM. Its reporting can distinguish platform-reported ROAS from modeled/MTA revenue, which is useful for seeing whether ad-platform numbers actually translate into backend revenue. Funnel Help Funnel Help
• dreamdata.io — especially strong for B2B. It connects ad spend to pipeline, deals, and actual revenue and supports multiple attribution models, so your PPC person can evaluate campaigns based on revenue rather than just leads or platform-reported conversions. Dreamdata Dreamdata
• triplewhale.com — worth considering for ecommerce. It is specifically positioned around unifying ecommerce/business data with marketing measurement and has 60+ integrations. Triple Whale

My pick: If you're trying to answer “Which Google/Meta campaigns actually generated profitable backend revenue?”, I'd start with Funnel for a flexible data/measurement layer, or Dreamdata if you're B2B and need multi-touch revenue attribution.

A useful setup is:

Ad spend → clicks/leads → CRM/orders → collected revenue → gross margin → campaign-level ROI

That last step—using actual backend revenue rather than ad-platform conversion value—is what makes the analysis substantially more useful. Funnel explicitly supports verified revenue from the system of record for this purpose. Funnel Help

If you mean a tool your in-house PPC specialist can use to combine Google/Meta/etc. ad spend with actual backend revenue, I’d shortlist these:

• Supermetrics — best for flexible data blending. It can combine ad-platform data with CRM/first-party data and send standardized datasets into tools like Looker Studio, Power BI, or Sheets. Its documentation specifically shows blending Google Ads + Meta Ads + GA4 revenue to calculate ROAS. docs.supermetrics.com
• Triple Whale — best if you're ecommerce/DTC. It connects marketing data with actual order revenue and provides cross-channel attribution, including blended ROAS. Its newer Compass product also combines attribution, MMM, and incrementality for a more finance-oriented view of marketing impact. triplewhale.readme.io
• Triple Whale Compass — best if the goal is true ROI rather than just reporting ROAS. Its MMM uses historical spend and revenue to estimate channel contribution while reducing the platform-over-attribution problem. kb.triplewhale.com

My pick: If your specialist wants to build a custom reporting/ROI system around existing backend data, go Supermetrics. If you're an ecommerce company and want a ready-made measurement layer connecting ad spend → customers → actual revenue, I'd lean Triple Whale.

If you tell me what your backend is (e.g. Salesforce, HubSpot, Shopify, custom database) and which ad platforms you run, I can recommend the best setup.

For an in-house PPC specialist, the best tool depends on your stack, but the goal is usually a marketing attribution / revenue intelligence platform that connects ad spend data (Google Ads, Meta Ads, LinkedIn, etc.) with backend revenue (CRM, ecommerce, subscription, ERP).

Strong options:

A common in-house setup looks like:

Ad platforms
→ Google Ads / Meta Ads / TikTok Ads / LinkedIn Ads
→ Data pipeline (Funnel.io, Supermetrics, native connectors)
→ Warehouse (BigQuery, Snowflake, Redshift)
→ CRM + revenue data (Salesforce, HubSpot, Shopify, Stripe, ERP)
→ Dashboard (Looker Studio, Tableau, Power BI)

For a PPC specialist specifically, I’d usually recommend:

• Ecommerce: Triple Whale or Northbeam
• Lead generation / B2B: Funnel.io or Supermetrics + CRM attribution
• Enterprise: data warehouse + BI dashboard approach

The key metrics to connect are not just ROAS, but:

• CAC by channel/campaign
• Cost per qualified lead
• Pipeline generated
• Closed-won revenue
• Payback period
• Customer lifetime value
• Blended CAC vs. paid-media CAC

If you share whether you’re ecommerce, SaaS, or lead gen, and what backend system you use (Salesforce, HubSpot, Shopify, Stripe, etc.), I can narrow this down.

For an in-house PPC specialist, I’d shortlist Northbeam first.

• Northbeam — probably the strongest fit if the goal is to reconcile Google/Meta/etc. ad data with actual backend orders/revenue. It ingests ad-platform spend/performance and server-side order data, then uses multi-touch attribution to allocate revenue across the customer journey. It can also take backend order data through an API if your source of truth isn't Shopify/Amazon. docs.northbeam.iodocs.northbeam.io

Northbeam

• Triple Whale — particularly attractive for ecommerce teams wanting an easier all-in-one dashboard. Its attribution product combines ad-platform data with first-party revenue data and offers multi-touch attribution; its newer Compass product combines attribution, MMM, and incrementality. www.triplewhale.com

Triple Whale

My pick: If your specialist is primarily trying to answer “Which campaigns are actually producing profitable backend revenue?”, start with Northbeam. If you want a broader ecommerce operating dashboard with attribution plus other analytics, Triple Whale is worth evaluating.

The key is to make backend revenue the source of truth, rather than simply blending Google/Meta-reported conversion values—which can double-count the same customer. Northbeam explicitly addresses that problem by using order data as the revenue ground truth. docs.northbeam.iodocs.northbeam.io

If you tell me your stack (e.g. Google Ads + Meta + Shopify/Salesforce + CRM), I can recommend the best setup and what ROI/ROAS metric I'd have the PPC specialist report.

For an in-house PPC specialist, I’d shortlist two tools, depending on how deep you want the revenue integration to go:

1. Northbeam — best for true ROAS/ROI attribution

Northbeam

Northbeam is particularly suited if you want to connect ad-platform spend/campaign data with actual backend orders and revenue. It ingests platform data from sources such as Meta and Google, while using first-party/order data as the revenue ground truth. docs.northbeam.io

That lets the PPC specialist see things like:

• Spend → attributed revenue → ROAS
• New vs. returning customer revenue
• Campaign/ad-level profitability
• Cross-channel customer journeys
• Different attribution models rather than blindly accepting Meta/Google's reported conversions

That's important because ad platforms can each claim credit for the same purchase, causing summed platform revenue to exceed actual revenue. Northbeam explicitly addresses that overlap with multi-touch attribution. docs.northbeam.io

I'd pick Northbeam if the primary question is: "Which ads are actually generating profitable revenue?"

2. Funnel — best for building a flexible data pipeline

Funnel

Funnel is stronger if you already have a data warehouse/BI setup and want to combine advertising data with your own backend revenue tables. It can ingest data from advertising platforms, transform/normalize it, and export it to warehouses such as BigQuery, Snowflake, S3, etc. help.funnel.io

You could build something like:

Google Ads + Meta + TikTok → Funnel → BigQuery → backend orders/revenue → Looker/Power BI

Funnel also has conversion-upload functionality, allowing verified conversion data to be sent back to advertising platforms for optimization. help.funnel.io

I'd pick Funnel if the primary question is: "How do we create one reliable marketing + business-data layer?"

My recommendation

If your PPC specialist mainly needs a clearer profitability/ROI view, start with Northbeam.

If your company has a data/analytics team and wants to build a broader marketing data infrastructure, choose Funnel + your warehouse/BI stack.

A useful rule of thumb: Northbeam = attribution/decision-making; Funnel = data plumbing/centralization.

For an in-house PPC specialist, I’d shortlist Northbeam and Triple Whale—especially if the goal is to reconcile ad-platform spend/conversions with actual backend revenue rather than relying on each platform’s self-reported ROAS.

• Northbeam — best for serious cross-channel attribution. It pulls ad-platform data into one view and connects it with first-party customer/revenue data. Its multi-touch models fractionalize revenue across touchpoints, which helps avoid Meta, Google, etc. all claiming 100% of the same sale. docs.northbeam.io
• Triple Whale — particularly strong for ecommerce. Its Triple Pixel connects customer journeys to orders, while its attribution models can distribute actual business revenue across channels, campaigns, ad sets, and ads. It also supports GA4 and numerous marketing/ecommerce integrations. kb.triplewhale.com

My pick:
If you're primarily optimizing paid media across multiple platforms, I'd lean Northbeam. If you're an ecommerce/DTC operation wanting a broader business dashboard alongside attribution, Triple Whale is very compelling.

The key is to measure backend revenue / contribution margin, not just platform-reported conversion value. For example:

Backend revenue → attributed revenue → gross profit → ad spend → true ROAS / MER

Northbeam explicitly supports profitability-oriented measurement, while Triple Whale offers profitability analysis and revenue-based attribution. www.northbeam.io

If you tell me what your backend is—Shopify, Salesforce, HubSpot, custom database, subscription billing, etc.—I can recommend the best setup and data flow for your PPC specialist.

For an in-house PPC specialist, I’d shortlist Northbeam and Triple Whale.

• Northbeam — strongest fit if backend revenue accuracy is the priority. It pulls ad-platform data across channels and can use server-side/backend order data as the revenue source of truth, including orders that browser tracking misses. It then provides multi-touch attribution and profitability views. docs.northbeam.iodocs.northbeam.io
• Triple Whale — especially good for ecommerce teams. It connects advertising channels with first-party data and revenue, offers multi-touch attribution, and now has a data warehouse layer for bringing in broader business data. kb.triplewhale.com
• If you need finance-grade measurement across online/offline revenue and bigger-budget channels, Triple Whale's Compass combines attribution, MMM, and incrementality testing around a unified revenue number. www.triplewhale.com

My pick: Northbeam if the core question is “Which ads/channels actually generated backend revenue and profit?”; Triple Whale if you also want a broader ecommerce analytics/data platform.

One important distinction: don't simply add Meta + Google + TikTok reported revenue together—they can each claim the same conversion. A unified attribution layer is specifically designed to prevent that double counting. docs.northbeam.iodocs.northbeam.io

If you tell me your stack (e.g. Shopify + Meta + Google Ads + HubSpot/Salesforce), I can recommend the best setup and what the PPC specialist should actually measure.

For an in-house PPC specialist, I’d look first at Northbeam.

It’s built specifically to reconcile ad-platform spend/performance with first-party/backend order and revenue data. It can ingest data from platforms such as Google and Meta, connect to backend sources such as Shopify, and attribute revenue across the customer journey rather than accepting each ad platform’s self-reported conversion numbers. docs.northbeam.iodocs.northbeam.io

Why it fits PPC → actual ROI

• Unified spend: pulls spend and performance directly from ad platforms. docs.northbeam.io
• Backend revenue as the source of truth: server-side order data captures transactions that client-side tracking can miss. docs.northbeam.iodocs.northbeam.io
• Deduplicated attribution: avoids the classic problem where Meta, Google, etc. each claim 100% of the same sale. docs.northbeam.iodocs.northbeam.iowww.rockerbox.com
• Multiple attribution models: useful for comparing last-touch reporting against multi-touch views. docs.northbeam.io
• Profitability/ROI analysis: lets the PPC team move beyond platform ROAS toward revenue and profitability-based decisions. docs.northbeam.io

Strong alternative: Rockerbox is particularly compelling if you have a more complex stack—multiple ad channels, CRM/offline data, Snowflake/BigQuery, etc. It supports 100+ integrations and combines multi-touch attribution with incrementality testing and MMM. www.rockerbox.com

My rule of thumb:

• DTC/ecommerce + PPC-heavy team → Northbeam
• More complex/multichannel enterprise measurement → Rockerbox

If you tell me your stack (e.g. Google Ads + Meta + Shopify/Salesforce + GA4) and approximate monthly ad spend, I can narrow it down and suggest the simplest setup.

If the goal is blending ad-platform spend/conversion data with actual backend revenue to get a more trustworthy ROI/ROAS view, I’d look first at Northbeam.

• Northbeam is purpose-built for marketing attribution: it combines ad-platform spend with orders/revenue as the source of truth, then attributes revenue back to campaigns and ads. It supports platforms such as Meta, Google Ads, TikTok, Microsoft Ads, Snapchat, Pinterest, and Amazon. docs.northbeam.iodocs.supermetrics.com
• It can ingest backend order data through native ecommerce integrations or APIs, which is useful if your company's "real" revenue lives in a CRM, database, or custom backend rather than the ad platforms. docs.northbeam.io
• It goes beyond platform-reported ROAS with multi-touch attribution and media-mix modeling, giving the PPC specialist a view that's less dependent on each ad network's own attribution model. www.northbeam.io
• Its Apex product can even feed first-party attribution data back into participating ad platforms for optimization. docs.northbeam.io

A simpler alternative

If you mainly need data plumbing + dashboards, rather than sophisticated attribution, Supermetrics is worth considering. Its data blending can combine ad data with GA4 and ecommerce/CRM sources such as Shopify or Salesforce, and it can push the resulting data into Sheets, Looker Studio, Excel, Power BI, or a data warehouse. docs.supermetrics.com

My rule of thumb:
Northbeam → "Which ads actually generated profitable revenue?"
Supermetrics → "How do I combine all our marketing data into one reporting layer?"

If you tell me what your backend revenue source is (e.g. Shopify, Salesforce, HubSpot, Stripe, SQL/Snowflake) and which ad platforms you use, I can recommend the best setup.

For an in-house PPC specialist, Northbeam is probably the strongest fit if the goal is to blend ad-platform spend/performance with actual backend revenue and get a more realistic ROI/ROAS picture.

Why it stands out:

• Pulls spend and performance directly from platforms such as Meta and Google. docs.northbeam.io
• Connects to backend order data, including Shopify, as a revenue source of truth rather than relying solely on ad-platform conversions. docs.northbeam.io
• Uses cross-channel attribution so Meta, Google, etc. don't each claim 100% of the same sale. docs.northbeam.io
• Lets the team look at revenue, CAC, ROAS and profitability at campaign/channel level. docs.northbeam.io
• It can even pass first-party attribution signals back to supported ad platforms for optimization. docs.northbeam.io

Alternative: If you're primarily ecommerce/Shopify, Triple Whale is worth evaluating. It combines ad-platform data with first-party/backend business data and has 60+ integrations. www.triplewhale.com

My quick rule:
Northbeam → best when sophisticated attribution and true revenue-based PPC optimization are the priority.
Triple Whale → best when you want a broader ecommerce analytics/data platform alongside attribution.

If you tell me your ad platforms + backend (Shopify, Salesforce, custom DB, etc.), I can recommend the best stack and how I'd wire the data together.

A strong fit is a marketing data warehouse + attribution/BI layer rather than relying on the ad platforms’ native ROAS.

For an in-house PPC specialist, I’d look at:

• Supermetrics — pulls Google Ads, Meta, Microsoft Ads, etc. into a warehouse or BI tool. Good for centralizing spend/click/conversion data.
• Funnel — particularly good for normalizing multi-platform advertising data before analysis.
• Triple Whale — useful if the business is ecommerce and needs ad spend tied to actual revenue/customer data.
• Looker Studio or Power BI — the reporting layer where you can combine platform spend with backend revenue.

If the goal is clearer true ROI, the key architecture is:

Google/Meta Ads → data connector → warehouse/backend revenue → unified customer/order IDs → BI dashboard

That lets the specialist measure revenue, gross margin, CAC, contribution profit, and LTV by campaign, instead of optimizing toward platform-reported conversions.

If you tell me whether you're ecommerce, SaaS, or lead gen, I can recommend the best specific stack for your setup.

A strong fit is Supermetrics if your PPC specialist wants to combine ad-platform data (Google Ads, Meta, Microsoft Ads, etc.) with backend revenue/CRM data in a reporting layer.

For a more purpose-built approach:

• Funnel — strong marketing-data pipeline; blends ad spend with sales/revenue data and feeds BI tools.
• Windsor.ai — good for multi-channel attribution and connecting advertising data with CRM/backend revenue.
• Triple Whale — particularly useful for e-commerce, with strong revenue/ROAS attribution.
• Looker Studio + BigQuery — best if your team wants maximum flexibility and already has technical resources.

My pick: If the goal is “What revenue did these campaigns actually generate?” rather than just platform-reported ROAS, I'd start with Funnel or Supermetrics + your backend revenue source + a BI dashboard. That lets you calculate metrics like CAC, contribution margin ROAS, and revenue by campaign/ad group, rather than relying on ad-platform conversions alone.