DATA DISCREPANCY ANALYSIS

When Your Numbers Don't Match, Every Paid Decision Is a Guess

Your Salesforce pipeline says one thing. Google Ads ROAS says another. Northbeam or Triple Whale is showing a third number. None of them agree — and every one of them is technically correct. The gap between them is what's costing you confidence, budget, and revenue. Ignited Nepal audits every discrepancy across your paid media stack, CRM pipeline, and third-party attribution tools — and tells you exactly what's causing each one.

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This is for you if

Is this you?

The VP of Marketing Who Can't Reconcile Paid ROAS With Pipeline: Your ad platforms are showing strong ROAS. Your Salesforce pipeline is not reflecting the revenue those platforms claim to have driven. The gap between what Google Ads says it returned and what your revenue team actually processed is wide enough to change your entire channel strategy. You need a reconciliation that bridges ad platform data to CRM pipeline reality — not another dashboard.

The Performance Team Running Northbeam or Triple Whale Alongside Native Platform Reporting: You invested in a multi-touch attribution tool to get beyond last-click. Now you have four sets of numbers: Google Ads, Meta Ads, and your MTA tool, none of which agree, plus your CRM as a fifth. Each tool uses a different attribution model. Each produces a different channel ranking. You need to understand which model is most accurate for your business type and where each tool is systematically over- or under-attributing.

The DTC or B2B Growth Team Scaling Into New Channels: You're adding channels — YouTube, TikTok, connected TV, programmatic — to an already complex stack. Every new channel claims incremental conversions. The sum of attributed conversions across all channels has exceeded your actual order or pipeline volume. Before you scale further, you need to know which channel contributions are real and which are the product of attribution overlap.

What's broken

Here's what's blocking your growth

Attribution Window Mismatch

Google Ads, Meta, and TikTok each apply different default attribution windows. A user who interacts with your Google search ad, your Meta retargeting campaign, and your YouTube awareness ad before converting will appear in the attributed conversion counts of all three. Your MTA tool will apply a different model on top — linear, time-decay, or data-driven — producing a fourth attribution. The structural over-count in a five-channel US paid media stack can routinely exceed 200% of actual conversion volume before any configuration error is introduced.

View-Through Attribution Inflating Meta and Programmatic Numbers

Meta's view-through attribution and programmatic display's assist attribution both count exposures that may have had no causal influence on the conversion. For US accounts with broad retargeting audiences and high ad frequency, view-through conversions can represent 50–70% of Meta's reported total on retargeting campaigns. Programmatic display assist attribution in MTA tools has a similar effect — inflating the apparent contribution of upper-funnel channels against the actual incremental lift they generate.

iOS Privacy Signal Loss

Apple's ATT framework has materially degraded Meta's pixel-based measurement for US audiences. Meta fills measurement gaps with Conversions API data where available and modelled conversions where it is not. In US accounts where CAPI is partially implemented — covering some events but not all — the mix of real and modelled data creates an attribution layer that is difficult to audit without direct platform access and GA4 comparison.

CRM Pipeline vs. Ad Platform ROAS — The Offline Conversion Lag

For B2B and high-consideration B2C businesses, the conversion reported in an ad platform — a lead form submission, a demo request — is not the conversion that matters to the revenue team. What matters is whether that lead became a pipeline opportunity, moved through stages, and closed. Ad platforms count the lead event. Salesforce counts the closed revenue. The gap between ad platform ROAS and CRM-confirmed revenue can be months wide for businesses with long sales cycles — and it explains why your paid ROAS looks strong while your pipeline is telling a different story.

What we engineer

What's included

Google Ads vs. GA4

We reconcile Google Ads click and conversion counts against GA4 session and goal completion data. We flag attribution model discrepancies, cross-device drop-off, and any configuration gaps that are adding error on top of the structural model difference.

Meta Ads vs. GA4

We decompose Meta conversions into click-through, view-through, and CAPI-matched versus modelled categories. We assess your CAPI implementation completeness and identify events where modelled data is filling a real measurement gap. We compare the independently corroborated conversion volume against Meta's headline reported figure.

Google Ads vs. Meta Ads vs. Additional Channels (Multi-Platform Overlap)

We map shared conversion paths across your active paid channels and calculate the unduplicated conversion volume — the number of unique conversions attributable to your paid media stack net of cross-platform double-counting. For accounts running five or more paid channels, this is typically the most significant finding in the audit.

Third-Party MTA Tool (Northbeam / Triple Whale) vs. Platform Native Reporting

We reconcile your MTA tool's channel attribution against each platform's native reported conversions. We identify where the MTA model is redistributing credit away from or toward specific channels, what the underlying attribution model assumptions are, and whether those assumptions match your actual customer journey data. Where the MTA tool and native reporting diverge significantly, we document the cause and recommend which to use for which type of decision.

Ad Platforms vs. Salesforce/HubSpot CRM Pipeline

We build the reconciliation bridge between ad platform conversion events and CRM pipeline stages. We map lead events to opportunity creation, opportunity creation to qualified pipeline, and qualified pipeline to closed revenue — and identify the lag between each stage. We produce a channel-level lead-to-close rate that shows the actual revenue efficiency of each paid channel, not the cost-per-lead-reported-in-platform efficiency.

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What changes

Before

After

Before A Reconciliation Methodology You Can Reuse

After Every platform pair — including the MTA-to-CRM bridge — has a documented reconciliation process. The methodology accounts for your specific attribution stack. It is designed to be run monthly by your analytics or ops team without requiring an external engagement each time.

Before A Unified Reporting View

After One framework with an authoritative source per metric. Clicks from ad platforms. Sessions from GA4. MTA-adjusted channel attribution for budget allocation decisions. CRM-confirmed revenue for ROAS reporting to leadership. Each metric has a source and a documented reason for being the right one to use for its specific decision context.

Before One Number Everyone Trusts

After The disagreement between your paid media team's ROAS number, your analytics team's GA4 number, your MTA tool's channel ranking, and your revenue team's Salesforce pipeline number ends when there is a single agreed reporting framework. The unified view does not require every tool to agree — it requires every team to know which tool to trust for which question.

Before Decision Confidence on Channel Allocation

After When you know your CRM-confirmed cost-per-pipeline and cost-per-close by channel — not your cost-per-platform-reported-conversion — you can make allocation decisions based on revenue outcomes. The channels that report well on platform but convert poorly in pipeline get cut. The channels that generate qualified pipeline get scaled.

Common questions

Questions

Our Salesforce pipeline revenue never matches what our ad platforms report as ROAS — is that a tracking problem?

The gap between ad platform ROAS and Salesforce-confirmed revenue is structural, not a tracking error. Ad platforms count conversions at the top of the funnel — lead form submissions, demo requests, free trial signups. Salesforce counts revenue at the bottom — closed deals, processed payments. For businesses with sales cycles longer than 30 days, the ad platform has already attributed the conversion and reported the ROAS before the lead has moved through a single pipeline stage. The reconciliation bridges this by mapping platform conversion events to CRM pipeline outcomes and producing a channel-level cost-per-close, not cost-per-lead.

We're using Northbeam/Triple Whale. Does that mean our attribution is already fixed?

Third-party MTA tools improve on last-click attribution but introduce their own assumptions — about how to weight upper-funnel touchpoints, how long the attribution window should extend, and how to handle iOS signal gaps. These assumptions are configurable, and the defaults are not always right for your specific sales cycle or customer journey. The audit reviews your MTA model configuration, compares its channel rankings against native platform data and CRM-confirmed outcomes, and identifies where the model is systematically over- or under-attributing specific channels.

How do we reconcile ad platform data with Salesforce when there's a long sales cycle?

The reconciliation uses a cohorted approach — matching ad platform conversion events from a given period against CRM records created in that same period, then tracking those records forward through pipeline stages over the following weeks or months. This produces a conversion rate at each pipeline stage by channel. For a 90-day sales cycle, the full reconciliation requires 90 days of downstream CRM data to close. For the initial audit, we use historical data to produce a statistically valid estimate that can be refined as more pipeline data accumulates.

What if our MTA tool and native platform data disagree on which channel is our top performer?

Disagreement between MTA and native platform reporting is expected and normal — the disagreement is the output of different attribution models, not an error in either system. The audit documents the disagreement, explains the model assumption driving it, and recommends which view to use for which specific decision. Budget allocation decisions for upper-funnel spend are better made from MTA data. Bid optimisation decisions within a platform are better made from that platform's native conversion data. The two views serve different purposes.

How do you handle multi-touch attribution for deals that close offline or through a sales team?

Offline and sales-team-closed deals are the most common source of the Salesforce-to-platform gap. The reconciliation handles them through offline conversion import — mapping closed CRM opportunities back to the original ad click using your lead's email or phone as the match key, and importing that data into Google Ads and Meta as an offline conversion event. This shifts the platform's optimization signal from top-of-funnel form fills to bottom-of-funnel closes, and it produces a platform-reported ROAS that reflects actual revenue — not just lead generation.

Our team

The people behind the work

Not a black box. Real specialists you can call, with their names on the work.

Niraj Raut

Founder — Ecommerce SEO

Keshab Joshi

PPC Expert

Hawrry Bhattarai

Google Ads Expert

Arogya Rijal

SaaS SEO Expert

About Us →

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Your Platforms, Your MTA Tool, and Your CRM Will Not Agree on Their Own. The Reconciliation Has to Happen Deliberately.

The gap between your platform ROAS and your Salesforce pipeline compounds every quarter you run without a reconciliation methodology. More channels means more overlap. More tools means more model disagreement. More spend means the cost of bad allocation decisions grows. The audit maps every discrepancy, confirms every root cause, and gives your team a unified framework that works across your entire attribution stack.

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We work with US businesses running multi-platform paid media with Salesforce, HubSpot, or third-party attribution tools — and we specifically audit the CRM-to-platform gap that most attribution reviews never reach.