ChatGPT answers, unedited

Yes. What you’re describing is essentially a marketing measurement/attribution platform: it pulls ad spend from Meta/Google/TikTok/etc., connects it to actual orders and customer journeys, and then ranks campaigns by metrics like ROAS, CAC, and new-customer revenue.

A few strong options:

• northbeam.io — probably the closest match. It combines ad-platform spend, first-party customer journey data, and backend order data, then attributes revenue across campaigns/ads. It also has tools specifically for identifying which campaigns are wasting spend and which are driving profitable growth. Northbeam Northbeam Documentation
• triplewhale.com — particularly strong for ecommerce. Its attribution data can combine spend, attributed revenue, ROAS/CPA, and new-customer acquisition down to campaign/ad level. It also incorporates post-purchase survey data into its Total Impact model. Triple Whale Triple Whale
• northbeam.io — worth noting that Northbeam is more of an enterprise/growth-marketing product; its current listed plans start around $1,500/month. Northbeam

The important distinction

If by “profitable” you mean actual profit, rather than just ROAS, you want something that can incorporate:

Ad spend → customer acquisition → revenue → COGS/margins → refunds → repeat purchases/LTV

Most attribution tools stop somewhere around revenue/ROAS. Northbeam and Triple Whale get much closer to the customer-level picture, but you should verify whether your particular costs and LTV model can be incorporated.

If you tell me what you're selling (ecommerce/SaaS/etc.), your ad channels, and roughly how much you spend per month, I can narrow this down to the 2–3 tools that would actually fit your setup.

Yes. There are tools built specifically for this: they connect ad platforms (Meta, Google, TikTok, etc.) with revenue sources (Shopify, Stripe, CRM, subscription systems) and use attribution/AI analysis to identify campaigns that are actually profitable—not just ones with good click or platform-reported ROAS. Attribo Adepth

A few examples:

• Attribo — focused on profit-based ad analytics. It combines ad spend with store/payment data to show profit by campaign, ad set, and ad, and surfaces recommendations about what to scale or cut. Attribo
• Adepth — aimed more at B2B teams. It connects ad platforms with CRM data (such as closed deals) to calculate real ROI and highlight budget leaks or scaling opportunities. Adepth
• Flowjat — built for app/SaaS companies; ties ad spend from multiple networks to revenue sources like Stripe, Paddle, and RevenueCat and provides optimization suggestions. Flowjat
• PayHelm — more e-commerce focused; combines revenue, margins, ad spend, and channel data with AI-driven analysis. PayHelm

The category is usually called:

• profit attribution
• closed-loop attribution
• marketing intelligence
• AI ad optimization
• revenue attribution

The important distinction is whether the tool optimizes for profit rather than ROAS. A campaign can show a 5× ROAS but still lose money after:

• product costs
• shipping
• refunds
• discounts
• customer acquisition costs
• customer lifetime value differences

If you want something that automatically surfaces winners, look for these features:

1. Campaign → customer → revenue matching

• "This Meta ad generated 143 customers worth $28k."
• Profit calculation

• Includes COGS/margins, not just top-line revenue.
• Customer quality analysis

• Finds campaigns producing repeat buyers or high-LTV customers.
• Budget recommendations

• "Move $5k/month from Campaign A to Campaign B."
• Alerts

• "Spend increased 40% but profit dropped."

The best fit depends on your business model:

• Shopify/e-commerce: Attribo, PayHelm, similar profit analytics tools
• SaaS/subscription: Flowjat, Adepth, CRM-linked attribution
• Lead generation/B2B: Adepth-style CRM attribution
• Large companies: often a warehouse + BI setup (e.g., ad connectors feeding BigQuery/Snowflake) rather than a single app. OwoX

If you tell me your stack (Shopify, Stripe, HubSpot, Salesforce, Meta Ads, Google Ads, etc.) and monthly ad spend range, I can narrow it down.

Yes. The category you’re looking for is usually called marketing attribution + revenue analytics (or profit optimization / closed-loop attribution). These tools connect ad spend (Meta, Google, LinkedIn, TikTok, etc.) with CRM/customer data and revenue sources (Stripe, Shopify, Salesforce, HubSpot, etc.) to identify which campaigns actually produce profitable customers. www.cometly.com

A few examples:

The “automatically surface profitable campaigns” part usually requires these capabilities:

• Closed-loop attribution: “This $10k campaign generated these actual paying customers.”
• Customer-level revenue matching: not just leads or clicks, but purchases, subscriptions, renewals, LTV.
• Profit metrics: CAC, contribution margin, payback period, LTV:CAC—not just ROAS.
• Automated actions: alerts like “move budget from Campaign A to Campaign B” or “this audience is producing high-LTV customers.” adepth.iodrivemetadata.com

A key distinction: many ad dashboards claim to show “profitable” campaigns, but they often rely on the ad platform’s own attribution model, which can over-credit itself. The stronger tools reconcile ad spend with first-party customer and revenue data. www.attributionapp.com

If you tell me your setup (e.g., Shopify + Meta, Google Ads + HubSpot, B2B SaaS + Salesforce, etc.), I can narrow this to the best options.

Yes. What you’re describing is essentially a marketing attribution + profit analytics platform: it combines ad spend, actual revenue/orders, and first-party customer journey data to determine which campaigns are genuinely profitable—not just which ones report the highest ROAS.

A few strong options:

• Northbeam — probably the closest match. It combines ad-platform spend, orders/revenue, and customer touchpoints, then uses multi-touch attribution to identify campaigns driving profitable growth. It also has Profit Benchmarks and tools that can feed its performance data back into ad platforms. www.northbeam.io
• Triple Whale — particularly strong for ecommerce. It combines ad data, first-party customer data, revenue, and post-purchase survey data, with multi-touch/Total Impact attribution. Its newer Moby AI operator can investigate performance and automate recurring growth work. www.triplewhale.com

The important distinction

A basic dashboard might tell you:

Meta campaign A → $100k revenue → 4.0 ROAS

A system like Northbeam or Triple Whale can get closer to:

Campaign A spent $25k and generated $100k attributed revenue, but 40% came from existing customers who would likely have purchased anyway. Campaign B generated fewer sales but substantially more new-customer profit.

That's the difference between optimizing for ROAS and optimizing for actual profitable growth. Northbeam explicitly uses first-party data and fractionalizes revenue across customer touchpoints, while Triple Whale incorporates customer journey and post-purchase survey data into its attribution models. www.triplewhale.comdocs.northbeam.io

If you're thinking about building something like this rather than buying it, I can also lay out the architecture/data model for an automated system that ingests Meta + Google + Shopify/CRM data and automatically flags profitable/unprofitable campaigns.

Yes. What you’re describing is essentially profit-aware marketing attribution / optimization: connect ad spend + customer identity/behavior + actual revenue, then rank campaigns by profit or contribution margin, rather than just clicks or platform-reported ROAS.

A few tools are close:

• Rockerbox — probably the closest match. It combines ad spend, first-party customer data, revenue, attribution, and LTV. Its Optimize product can make spend recommendations against ROAS/CPA goals, while its modeling can estimate incremental revenue and diminishing returns. www.rockerbox.com
• Triple Whale — particularly strong for ecommerce/DTC. Its attribution system combines first-party data and post-purchase survey data and provides campaign/ad-set/creative-level performance views. www.triplewhale.com
• HubSpot + attribution tooling — better suited to B2B or businesses where customer/deal data lives in a CRM. It can connect marketing spend and CRM revenue to measure CAC, LTV:CAC, and campaign ROI. www.hubspot.com

The important distinction: most tools can surface profitable campaigns automatically, but truly optimizing for profit requires feeding them more than revenue. Ideally you give the system:

Ad spend → customer → order/revenue → COGS → refunds → fulfillment/fees → gross profit → LTV

Then you can have it automatically say things like:

Scale: Campaign A — $42 CAC, $118 contribution profit/customer
Hold: Campaign B — $61 CAC, $65 contribution profit/customer
Cut: Campaign C — $89 CAC, $31 contribution profit/customer

If you tell me whether you're ecommerce, SaaS, or lead-gen/B2B, I can narrow this down to the 3–5 tools that best fit and explain which ones can actually take automated action on the ad platforms, rather than just report the winners.

Yes. What you’re describing is essentially marketing attribution + profitability intelligence: a system that combines ad spend, actual revenue, and customer-level data to tell you which campaigns are genuinely profitable—not just which ones have the best platform-reported ROAS.

A few strong options:

• Northbeam — probably the closest fit if profitability is the primary goal. It combines multi-touch attribution, first-party customer data, ad spend, and revenue, and its Profit Benchmarks automatically establish targets for ROAS/MER and flag campaigns against profitability goals. www.northbeam.io
• Triple Whale — particularly strong for ecommerce. Its pixel connects customer journeys to purchases, lets you drill from campaign → ad → customer/order, and combines spend, attributed revenue, new-customer acquisition and other metrics. kb.triplewhale.com
• Triple Whale Attribution — its Total Impact model also incorporates post-purchase survey data, so you can combine behavioral attribution with what customers actually say influenced their purchase. www.triplewhale.com

The important distinction

A basic dashboard says:

Campaign A: $10k spend → $30k revenue → 3.0 ROAS

A better system says:

Campaign A: $10k spend → $30k attributed revenue → $18k gross profit → 65% new customers → strong 90-day LTV → profitable, scale it

And it can distinguish that from:

Campaign B: $10k spend → $35k revenue → $3.5 ROAS → mostly existing customers → low margin → actually less attractive

That customer/LTV layer is the part that makes these tools substantially more useful than Meta Ads Manager or Google Ads reporting alone.

If you're looking to build something like this yourself, the architecture is very doable: ad platforms + Shopify/CRM + payment/order data → unified customer identity → campaign attribution → contribution margin/LTV → automated “scale / hold / kill” recommendations.

If you tell me what you sell (ecommerce, SaaS, lead gen, etc.) and what platforms you're using (Meta, Google, Shopify, HubSpot, Stripe, etc.), I can narrow this down to the 2–3 tools that fit best.

Yes. What you’re describing is essentially profit-aware marketing attribution: connect ad spend + actual revenue + customer/order data, then rank campaigns by profitability rather than just platform-reported ROAS.

A few strong options:

• Northbeam — probably the closest match. It combines first-party customer/order data with ad-platform spend and uses multi-touch attribution to determine which campaigns are actually driving revenue. It also has Profit Benchmarks specifically aimed at identifying profitable growth. www.northbeam.io
• Triple Whale — particularly strong for ecommerce. Its data model combines campaign/ad spend, attributed revenue, orders, and new-customer acquisition, and it supports multiple attribution models. triplewhale.readme.io
• Northbeam Apex — interesting if by “automatically” you mean actually feed the conclusions back into ad optimization. Northbeam can pass its first-party attribution signal back to participating ad platforms so their algorithms can optimize toward the better signal rather than their default attribution. docs.northbeam.io

The important distinction

Most tools say they show you ROAS. The more sophisticated ones can get closer to:

Ad spend → customer acquisition → orders → revenue/LTV → contribution profit → automatically shift budget toward profitable campaigns

For example, if Campaign A has a 3.0x ROAS but mostly acquires one-time, low-margin customers, while Campaign B has 2.2x ROAS but acquires customers with substantially higher repeat purchase rates, a customer-aware system should favor B.

Northbeam explicitly supports customer/order-level touchpoint data and LTV-oriented attribution, while Triple Whale exposes new-vs-returning customer metrics alongside spend and revenue. docs.northbeam.io

If you tell me your stack (e.g. Shopify + Meta + Google + Klaviyo, or Salesforce + HubSpot + Google Ads), I can narrow this down to the 2–3 tools that would actually fit and tell you which can automatically take action, not just make dashboards.

Yes. There are several tools now that do essentially this: combine ad spend + actual revenue + customer/CRM data, calculate profitability, and automatically tell you which campaigns to scale or cut.

A few worth looking at:

• Northbeam — More established/enterprise-oriented. Combines multi-touch attribution, incrementality, and media-mix modeling to connect spend to revenue and optimize profitable growth. www.northbeam.io
• Metriq — Particularly interesting for Shopify brands. Combines Meta/Google/Shopify data, calculates true profit after COGS, fees, returns, and ad spend, then has an AI analyst recommend campaigns to pause or scale. gometriq.com
• Attribo — Focused specifically on profit-based ad analytics. Connects Meta spend with Stripe/Shopify revenue and can surface daily recommendations to scale or kill campaigns. www.attribo.app
• Adepth — Better fit if you're lead-gen/B2B. It joins ad data with CRM closed-won revenue and automatically surfaces ROI insights and opportunities. adepth.io
• Cavor — Unifies ad platforms, CRM, and payment data and provides automated scorecards/attribution. It explicitly positions itself around identifying what's wasting money and what to do next. gometriq.comwww.cavorhq.com

The important distinction

If by "profitable" you mean actual business profit, rather than just ROAS, I'd prioritize something that can calculate:

Ad spend → customer → revenue → COGS/fees → contribution profit → LTV

rather than a conventional attribution dashboard.

For example, a campaign doing 5× ROAS can actually be worse than one doing 3× ROAS if the 5× campaign attracts customers with poor margins or low retention.

If you tell me whether you're running e-commerce, SaaS, or lead-gen—and what you're using for ads + CRM/payment (e.g. Meta + Shopify, Google + HubSpot, etc.)—I can narrow this down to the 2–3 best options for your setup.

Yes. What you’re describing is essentially profit-focused marketing attribution / marketing intelligence: connect ad spend → customers → actual revenue (and ideally margin/LTV), then automatically identify which campaigns are worth scaling or cutting.

A few tools that fit particularly well:

• Cometly — Strong fit if you want ad + CRM + Stripe/customer revenue in one system. It can attribute trials, purchases, renewals and LTV back to campaigns, and its AI Ads Manager can rebalance spend toward winners. www.cometly.com
• Attribution — Particularly good for tying individual user-level ad costs to revenue, CAC payback and LTV:CAC. It connects ad platforms with payment systems such as Stripe. www.attributionapp.com
• Adepth — Good B2B/SaaS option. It connects Google/Meta ad spend with CRM deals and closed-won revenue, then surfaces AI insights and budget opportunities. adepth.io
• Triple Whale — Strong e-commerce choice, with customer segments, blended ROAS, first-party data, AI analysis and automation. Its higher tiers are designed to automate recurring growth work. www.triplewhale.com
• Converge — E-commerce/DTC-focused and combines multi-touch attribution, ad spend, customer behavior, revenue and margin, with AI-driven media optimization. www.runconverge.com
• Attribo — More narrowly focused on profit: combines Meta spend with Stripe/Shopify revenue and can account for COGS/fees, then recommends campaigns to scale or kill. www.triplewhale.comwww.runconverge.comwww.attribo.app

The key distinction

If you want “which campaigns generated revenue?”, lots of attribution tools can do it.

If you want “which campaigns generated profitable customers, and automatically move budget toward them?”, I'd prioritize a system that incorporates:

Ad spend + customer identity + actual collected revenue + COGS/margin + repeat purchases/LTV + attribution + automated recommendations/actions.

For example, a useful output would look like:

If you tell me whether you're running e-commerce, SaaS/B2B, or lead-gen/high-ticket sales—and what you use for ads/CRM/payments (e.g. Meta + HubSpot + Stripe)—I can narrow this to the 2–3 best options for your stack.

Yes. What you’re describing is essentially a marketing attribution / marketing intelligence platform: it connects ad spend with actual revenue and customer journeys, then tells you which campaigns are truly profitable.

A few good options:

• Northbeam — probably the closest match. It combines ad-platform spend, first-party customer journey data, and order/revenue data. You can see performance down to campaign/ad level, including ROAS, CAC, new vs. returning customers, and attributed revenue. www.northbeam.iodocs.northbeam.io
• Triple Whale — particularly popular with ecommerce brands; its attribution system combines first-party and post-purchase data to assess which marketing channels generate revenue. kb.triplewhale.com
• Dreamdata — stronger fit for B2B. It connects marketing activity to pipeline/revenue and provides campaign-level attribution. dreamdata.io

The important distinction

If by “surface profitable campaigns automatically” you mean:

“Connect Meta + Google + Shopify/CRM → calculate actual customer-level economics → identify campaigns producing profitable customers → tell me where to increase/decrease spend”

then Northbeam is very close to that workflow. It can distinguish new vs. returning customers and calculate metrics such as new-customer CAC and ROAS rather than simply accepting Meta/Google's own attribution. www.northbeam.iodocs.northbeam.io

The catch is that ROAS ≠ profitability. A really useful system should ideally incorporate gross margin, discounts, refunds, shipping, COGS, and customer LTV, so it can answer “Did we actually make money?” rather than merely “Did this campaign generate revenue?”

If you tell me whether you're ecommerce, SaaS/B2B, or lead-gen, I can narrow this down to the 2–3 tools that best fit and explain how automated the “find profitable campaigns” part actually is.

Yes. There are several tools now that essentially do “ad spend + actual revenue + customer/LTV data → profitable campaigns” rather than just reporting ROAS.

Strong options

• Northbeam — Strong for sophisticated attribution. Combines ad spend with first-party customer/revenue data, multi-touch attribution, incrementality, and media-mix modeling. It can identify which campaigns are actually creating demand rather than merely capturing existing customers. www.northbeam.io
• Triple Whale — Probably the most turnkey choice for e-commerce. It brings together revenue, spend, profitability, new-vs-returning customers, attribution and customer cohorts, and its AI layer can surface insights and help optimize campaigns. www.triplewhale.com
• HYROS — Particularly interesting if you have longer sales cycles, SaaS, info products, or high-ticket sales. It tracks customers through their transactions and can connect campaigns to downstream revenue, LTV and churn—not just the initial conversion. hyros.com
• Cometly — More explicitly oriented around connecting ad platforms to CRM/revenue data. Its current product includes campaign-level net profit, high-LTV customer audiences, and AI that can rebalance spend toward winning campaigns. www.cometly.com
• Attribo — A more narrowly focused profit-first option for e-commerce. It combines Meta spend with Stripe/Shopify revenue, calculates actual profit per campaign/ad set/ad, and provides automated “scale or kill” recommendations. www.attribo.app

The important distinction

What you really want isn't simply ROAS:

Campaign → acquired customer → revenue/LTV → gross margin → ad spend → actual profit

For example, Campaign A might have a 5× ROAS but mostly acquire one-time, low-margin buyers. Campaign B might have only 3× ROAS but acquire customers who subsequently spend 4× more. A good system should identify B as the better campaign.

If you tell me whether you're e-commerce, SaaS, lead-gen/high-ticket, or something else, I can narrow this down to the 2–3 tools that best fit and compare their automation, integrations, pricing, and ability to actually move budget automatically.

Yes. The category you’re looking for is marketing intelligence / attribution platforms—they connect ad spend, actual revenue, and customer/order data, then identify which campaigns are generating profitable growth.

Two particularly strong options:

• Northbeam — Probably the closest match if your goal is “tell me where my next dollar should go.” It combines first-party customer journey data with ad spend and revenue, offers multi-touch attribution, incrementality, and media-mix modeling, and has Profit Benchmarks that flag campaigns against profitability targets. docs.northbeam.io
• Triple Whale — Particularly strong for ecommerce. It combines ad spend, orders, revenue, customer acquisition, refunds, COGS and other costs, and can calculate Net Profit, Profit on Ad Spend (POAS), New Customer ROAS, etc. Its attribution can go down to campaign/ad level. kb.triplewhale.com

The important distinction

If by “profitable campaigns automatically” you mean:

“Campaign A generated $100k revenue, but after ad spend, COGS, refunds, and the value of new vs. returning customers, it actually made $22k profit—and Campaign B only looks good because it’s taking credit for existing customers.”

Then Triple Whale or Northbeam are much closer to what you want than something like Google Analytics or a standard ad-platform dashboard.

For example, Triple Whale explicitly calculates POAS as profit generated per dollar of ad spend and can distinguish new-customer acquisition. kb.triplewhale.comtriplewhale.readme.io Northbeam similarly focuses on identifying which ads are actually creating demand versus wasting budget on returning customers. docs.northbeam.iowww.northbeam.io

If you tell me your stack (e.g. Shopify + Meta + Google + Klaviyo, B2B Salesforce + Google Ads, etc.) and roughly how much you spend/month, I can narrow it down to the best 2–3 options and tell you which one can actually automate the “find profitable campaigns” part.

Yes. There’s a growing category of profit-first marketing analytics / attribution tools that do exactly this: connect ad spend to actual revenue and customer outcomes, then identify which campaigns are genuinely profitable—not merely showing high ROAS.

A few worth looking at:

• Triple Whale — probably the most established fit for ecommerce. It combines ad spend, revenue, new/returning customers, profitability, attribution, and now AI agents that surface anomalies and growth opportunities. www.triplewhale.comwww.triplewhale.com
• Northbeam — stronger on sophisticated attribution and profitability analysis. Its Profit Benchmarks can automatically flag campaigns against the ROAS/MER needed to hit profitability goals. docs.northbeam.io
• Attribo — more narrowly focused on turning Meta + Shopify/Stripe data into profit per campaign/ad set/ad, with automated daily recommendations about what to scale or pause. www.attribo.appwww.triplewhale.com
• Cavor — particularly interesting if you want customer-level attribution. It connects the first click through to closed revenue and calculates true CAC/profit rather than relying on ad-platform reporting. www.triplewhale.comdocs.northbeam.iowww.cavorhq.comadepth.iowww.triplewhale.com
• DriveMetaData — geared more toward companies where customer quality, retention, LTV, and CRM revenue matter. It connects spend → acquisition → activation → revenue → retention and can recommend budget shifts. drivemetadata.com
• Adepth — interesting for B2B: connects ad spend with CRM pipeline and closed-won revenue, then automatically surfaces ROI insights and recommended actions. adepth.io

The important distinction

There are really three levels of automation:

1. Reporting: “Campaign A spent $10k and generated $40k.”
2. Profit attribution: “Campaign A generated $40k revenue but only $7k contribution profit after COGS, refunds, etc.”
3. Autonomous optimization: “Campaign A is profitable, Campaign B isn't; shift $5k from B → A because the expected incremental profit is higher.”

Most tools are excellent at #1 and increasingly #2. A smaller number are getting into #3. Triple Whale, for example, explicitly offers AI-agent recommendations and budget optimization; Attribo advertises daily “scale or kill” recommendations. www.attribo.appwww.triplewhale.com

If you're asking because you're considering building/buying a tool that does this, the interesting product opportunity is arguably the last mile: not another dashboard, but an “AI marketing CFO” that continuously connects spend → customers → revenue → margin → LTV and automatically tells you where the next dollar should go.

Yes. There are several tools that do this, although they vary in how “automatic” they are. The category is usually called profit attribution, revenue intelligence, marketing analytics, or AI campaign optimization.

Some options:

The capabilities you’re describing usually look like:

1. Pull ad data
   • Meta Ads
   • Google Ads
   • TikTok Ads
   • LinkedIn Ads

2. Match customers/revenue
   • Shopify orders
   • Stripe payments
   • CRM deals (Salesforce, HubSpot, etc.)
   • Customer lifetime value

3. Calculate true profitability
   • CAC
   • ROAS
   • contribution margin
   • LTV:CAC
   • payback period

4. Surface actions
   • “Increase budget on Campaign A”
   • “Pause Campaign B”
   • “This creative is attracting low-value customers”
   • “These customers have the highest lifetime value”

A key distinction: many ad dashboards optimize for platform metrics (clicks, conversions, reported ROAS). The more advanced tools optimize for business outcomes (profit, retained customers, closed revenue). adepth.io

If you tell me your setup (e.g., Shopify/e-commerce, B2B lead gen, mobile app, or agency managing clients) and your ad channels, I can narrow this to the best-fit tools.

Yes. There are several tools built specifically around this problem: joining ad spend → customers → actual revenue/profit → campaign-level decisions rather than relying on platform-reported ROAS.

A few worth looking at:

• Cometly — probably one of the closest matches if you want customer-level attribution. It connects ad clicks to CRM/Stripe revenue, tracks LTV, and has AI-driven recommendations/optimization. It can also rebalance spend toward winning campaigns. www.cometly.com
• Triple Whale — particularly strong for Shopify/e-commerce. It combines revenue, ad spend, new customers, attribution and profitability, with AI insights on higher tiers. www.triplewhale.com
• HYROS — good for businesses where customer value extends beyond the initial purchase. It ties purchases, subscriptions, churn and LTV back to the original ad/source. www.triplewhale.comhyros.comwww.attributionapp.com
• Adepth — interesting if you're B2B. It connects Google/Meta ad spend with CRM closed-won revenue and surfaces AI recommendations, including which campaigns are actually profitable. adepth.io
• Attribution — more attribution/data-infrastructure oriented. It connects ad-platform costs with individual customer journeys and payment data to calculate CAC, payback and LTV:CAC. www.attributionapp.com
• Cavor — newer option focused heavily on reconciling ad spend with actual customer revenue and profit, including automated alerts. www.cavorhq.com

The key distinction

What you're describing is more sophisticated than a normal marketing dashboard:

Ad spend
↓
campaign / ad / click
↓
identified customer
↓
purchase / closed deal
↓
revenue + COGS + other costs
↓
actual contribution profit
↓
"Scale this / hold / kill this"

The customer-level connection is the important part. A campaign with a 4× platform ROAS can actually be worse than one with 2.5× ROAS if the first campaign attracts low-LTV or high-refund customers.

If you tell me what kind of business you have (e-commerce, SaaS, agency, local/service business, etc.) and what you're using for ads + CRM/payments (Meta, Google, Shopify, Stripe, HubSpot, Salesforce, etc.), I can narrow this to the 2–3 tools that best fit your stack, including which ones can automatically tell you what campaigns to scale or cut.