Prooflytics: The Blind Spot Between Your CRM and Your Ads

The blind spot is the gap between what your ad account knows (clicks, leads, CPL) and what your CRM knows (deals, revenue). Neither system sees the connection: which click turned into a closed deal. Prooflytics is a SaaS platform that closes this loop - it connects ad-account data with your CRM pipeline and shows the real CAC for each channel, not the cost of a lead.

In Exceltic.dev projects, this gap shows up with almost every client that spends money on traffic. The data is there: the ad account is full of metrics, the CRM is full of deals. But they live in two separate tabs, and there is no bridge between them. Once a month the marketer manually pulls a report together in a spreadsheet, sales look at their own pipeline, and the question “which channel actually brings in money” stays without a precise answer. This is not a problem with one tool - it is a structural gap between two halves of the business.

Why the Marketer Optimizes the Wrong Metric

A marketer works off what they can see. And what they see is the Ads Manager: CTR, CPM, CPL. Cost per lead is $18 - not bad, let’s scale it. The decision is made based on a metric that is available in real time and refreshes every hour.

The problem is that CPL is not the metric you should be optimizing your budget against. A channel with cheap leads can deliver expensive customers: the leads don’t close, the cycle drags on, the average deal size is lower. And a channel with twice the CPL can close deals three times more often and bring in more revenue for the same dollar.

The marketer doesn’t see this, because the cost per closed deal for a channel lives in the CRM, not in the ad account. And as long as that data isn’t connected, budget leaks into channels that look efficient on CPL and fail on revenue. That is the blind spot - not a lack of data, but the absence of a link between the data sets.

Where Exactly the Blind Spot Appears

The gap appears at the seam between two systems, each of which only knows its own half of the customer journey.

The ad account knows the clicks. It sees that a user saw an ad, clicked, filled out a form. That is where its knowledge ends. What happened to the lead next - whether it closed or dropped off on the first call - the account never knows.

The CRM knows the deals. It sees that a lead came in, moved through the pipeline stages, closed for an amount of X. But where it came from is often recorded as “ads,” “internet,” or an empty field the rep never filled in. The specific campaign, ad, or keyword - that information is usually not in the CRM.

Nobody knows the connection. Between “a click on a retargeting ad for a lookalike audience” and “a $4,000 deal closed after 6 weeks” there is no thread. That thread breaks at the exact moment the lead moves from the ad account into the CRM - and restoring it by hand is nearly impossible at hundreds of leads a month.

The result is predictable: the company spends budget, earns revenue, and can’t say which part of the budget produced it. Decisions get made on approximations and intuition.

What It Means to Close the Loop

Closed-loop attribution is a methodology in which data about a closed deal is fed back to the source of the click. The loop closes when the system can trace the path from a specific ad to a specific line of revenue in the CRM.

Technically, this works through click IDs - unique identifiers that the ad platform appends to each click. Google adds gclid, Meta adds fbclid, LinkedIn adds li_fat_id, and so on. When the user lands on the site, this identifier is captured and stored together with the lead. Later, when the lead closes in the CRM, the system knows: this deal started with a click that had a specific gclid, which means it came from a specific campaign.

The key term here is first-party data. Closing the loop is built not on third-party cookies (which are dying off), but on the company’s own data: the click identifier captured on its own site, and the deal recorded in its own CRM. This makes attribution resilient to third-party blocking and privacy restrictions.

When the loop is closed, a metric appears that wasn’t there before: the real cost of a closed deal for each channel, campaign, and ad. Not an approximation, but a one-to-one link between spend and revenue.

What Prooflytics Does

Prooflytics connects the two halves and rebuilds the missing thread. The platform pulls data from ad accounts (Meta Ads, Google Ads, GA4, LinkedIn Ads) and at the same time connects to the CRM via OAuth - HubSpot, Salesforce, Pipedrive, Kommo, Close, and others are supported.

The Prooflytics pixel installs on your site and captures UTM tags and more than a dozen click IDs (gclid, fbclid, ttclid, msclkid, li_fat_id, and others) on every visit. That is the first half of the thread. The second half comes from the CRM: when a deal closes, Prooflytics matches it to the captured click ID and a time window.

The result is real CAC per channel. Not “CPL in Meta is $18,” but “cost per closed deal for the lookalike campaign is $180, for retargeting it’s $620.” The marketer finally sees the metric the decisions should have been based on from the start.

On top of that, AI modules run: a daily briefing that interprets the changes with concrete actions, a weekly PDF report by email, a HADI hypothesis tracker. But the core of the value is closing the loop: linking ad data with the CRM pipeline through first-party attribution.

For more on connecting to the CRM and pre-normalizing the data, see the section on custom integrations for Kommo CRM.

Where Prooflytics Is Not a Silver Bullet

Honestly: the tool doesn’t fix dirty data. If the foundation is a mess, the attribution will be a mess with a pretty interface.

You need clean CRM hygiene. If pipeline stages are named haphazardly, deals sit on old stages for months, and the “closed/won” field isn’t always set - Prooflytics won’t be able to reliably tell which deal actually closed. Attribution is only as accurate as the deal labeling in your CRM.

You need correct UTMs. If ad links are tagged inconsistently - different reps write utm_source differently, some links have no tags at all - some of the clicks won’t be tied to a campaign. Click IDs help, but they don’t cover every case (for example, an organic visit after an ad touch).

You need setup. The OAuth connection itself takes 10-15 minutes, but correctly mapping pipeline stages to conversion events and installing the pixel is 1-3 days of work depending on the state of the data. This is not “plug in the box and off you go.”

Long and non-linear cycles stay complex. If a deal closes through offline meetings, a tender, or several decision-makers, part of the path will still fall outside attribution. Prooflytics narrows the blind spot, but doesn’t eliminate it entirely.

That is exactly why Exceltic.dev puts the emphasis on data preparation before connecting anything - normalizing UTMs and cleaning up pipeline hygiene in the CRM. Without that step, the tool will show numbers you can’t trust.

Who It’s a Fit For

Prooflytics makes sense if:

• The company drives traffic from 2+ ad channels and wants to compare them by real revenue return, not by CPL
• The data already exists in a CRM (HubSpot, Kommo, Salesforce, Pipedrive) and in ad accounts, but there is no unified picture
• The deal cycle is 2 to 8 weeks - that’s where first-click and last-click attribution lie, and a closed loop gives the real picture
• The marketer spends hours manually assembling a report from several tabs
• The monthly ad budget is large enough that a misallocation costs real money

Less relevant if:

• There’s a single ad channel and a simple funnel without a CRM
• Transactions are instant, with no sales cycle (part of e-commerce)
• The CRM data is in such a state that a full pipeline restructure is needed first - in which case start there

Term: Closed-loop attribution - a methodology in which data about a closed deal is returned to the source of the click, making it possible to calculate the real customer acquisition cost. CAC per channel (cost per closed deal by channel) - the money spent on ads in a channel divided by the number of closed deals from that channel. Unlike CPL, this metric accounts for the lead-to-customer conversion and shows the real economics of a channel.

A Typical Example With Numbers

A company runs two channels: Google Search and Meta Ads. In Meta there are three campaigns: retargeting, lookalike, and interest targeting. The budget is about $5,000/month.

What the marketer sees without a closed loop:

The CPL conclusion is obvious: scale retargeting, it’s the cheapest of all. The budget gets reallocated in its favor.

What Prooflytics shows after connecting the CRM and matching click IDs to deals:

The picture inverts. Retargeting delivers cheap leads, but 85% of them drop off at the first pipeline stage - this is an audience that has already been on the site and clicks out of curiosity. Lookalike brings in fewer leads, but the conversion to a deal is three times higher and the average deal is 40% larger. The real customer cost for lookalike is $180 versus $620 for retargeting.

The decision made on CPL was exactly the opposite of the right one. Closing the loop cost the company one day of setup and saved thousands of dollars of budget that was flowing into the channel with the highest cost per revenue.

This is not an exception, it’s a typical situation. Optimizing by CPL without funnel data systematically pushes budget toward cheap but non-converting leads.

Frequently Asked Questions

How is closed-loop attribution different from GA4?

GA4 sees on-site behavior and the conversions you pass to it, but it doesn’t know what happened to the deal in the CRM several weeks later. Slow-converting channels are often attributed by GA4 to “direct” or organic. Closed-loop attribution returns data about the closed deal to the source of the click - and shows the channel’s real contribution to revenue, not just to on-site conversions.

What if our UTMs aren’t tagged?

Then normalization comes first. Click IDs partially save you (they’re captured even without UTMs), but for the full picture you need consistent utm_source, utm_medium, utm_campaign tagging on all ad links. This is the first preparation step Exceltic.dev does before connecting Prooflytics.

Does this work with Kommo?

Yes. Kommo connects via OAuth alongside HubSpot, Salesforce, and Pipedrive. For Russian-speaking teams working in Western markets, this is a working combination. If you’re only just considering Kommo, there’s a separate Kommo CRM review.

How long does setup take?

The OAuth connection is 10-15 minutes. Correctly mapping pipeline stages, installing the pixel, and verifying attribution is 1-3 days depending on the state of the CRM data. If the pipeline and UTMs are in order - closer to a single day.

Does Prooflytics fully replace manual reports?

For standard channel and campaign reporting - yes, the daily briefing and weekly PDF cover that work. Non-standard analytics and custom cuts specific to your business may require a separate BI stack, but the tool does close the core “ads versus revenue” blind spot.

Bottom Line

• The blind spot between CRM and ads is not a lack of data but the absence of a connection: the ad account knows the clicks, the CRM knows the deals, nobody knows the link between them
• The marketer optimizes by CPL because they can’t see cost per closed deal by channel - and systematically funds cheap but non-converting leads
• Closing the loop is built on first-party data: capturing click IDs (gclid, fbclid, and others) on the site and matching them to deals in the CRM
• Prooflytics connects ad accounts with the CRM pipeline and shows real CAC per channel instead of CPL
• The tool is not a silver bullet: it needs clean CRM hygiene, correct UTMs, and 1-3 days of setup - dirty data will give pretty but unreliable attribution

If you have data in both the CRM and the ad accounts but no unified picture - describe your current UTM structure and the state of your pipeline. Exceltic.dev will assess what needs to be normalized before closing the loop through Prooflytics.