Service

Marketing analytics consultant for apps, read on payment data

Marketing analytics for an app or subscription business means tying each ad dollar to a payment: tracking events from the payment itself, attribution across web, MMP and SKAN, dashboards for CAC, LTV, ROAS and payback, and forecasts that set budgets. Vlad Yasinski builds that layer, and he spent the budget it measures, with Facebook and TikTok spend of up to $1.5M per month.

Updated 6 November 2026

Track record

Built the tracking behind direct web funnels, from ad to landing page to payment, so campaigns were judged on revenue per click and payback.
Set up Meta Pixel and Conversions API at Facebook budgets of up to $1.5M per month.
Built BI, tracking and the end-to-end data flow from scratch.
Built dashboards and did cohort and forecast work in Tableau, Looker and Google Sheets.
Owned budgeting, forecasting and P&L reporting on CAC, revenue, ROAS and payback.

UsedConversions APIGoogle SheetsMeta PixelTableauLookerMetaGoogle

Product names and logos belong to their owners. They appear only to show tools used or typical in this work. No endorsement or partnership is implied. To request removal of a logo, use the contact form. Letter tiles are monograms drawn for this site and are not the owners' logos. Apple, App Store, iOS, SKAdNetwork, Google, Google Play and Android are trademarks of their respective owners.

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More detail

A marketing analytics consultant connects ad spend to payments, so you can see CAC, LTV, ROAS and payback by channel, campaign and creative without relying on platform-reported conversions. The work covers attribution, server-side tracking, dashboards, unit economics and forecasts.

Illustrative flow, simplified.

Lineage lensSpend to payback

Use the lineage lens to trace spend and payments through attribution, shared data and BI to a payback decision.

Ad spendCampaign cost
AttributionSource, campaign and attributed event
Shared dataSpend, payments and one cohort definition
BIPayback by cohort
Read paybackHuman decision

Sources

Cost and revenue enter as separate signals.

Data: campaign cost + payment amount

Attribution

Used attribution tools connect activity to revenue.

Data: source + campaign + attributed event

Shared data

Spend, payments and attribution meet here.

Join: one payment and cohort definition

BI

Used dashboards read the same joined data.

View: payback by cohort

Decisions

A person reads payback before changing spend.

Decision: keep, kill or scale

Set kill and scale logicPacing rules and alerts

Illustrative BI board. Not client results. KPI strip, then a cohort matrix.

Input$3,000Spend
Modeled$25Platform CPA
Payments$33.33Payment CPA
DecisionMonth 2Payback

Illustrative payback matrix. Rows are cohorts, columns are months since first payment. Empty cells are unobserved, not zero.
Cohort	Month 0	Month 1	Month 2
This campaign90 payers	0.90Short of spend	1.26Covers spend	n/aUnobserved
Newer cohortNot yet observed	n/aUnobserved	n/aUnobserved	n/aUnobserved

Illustrative payback matrix. Rows are cohorts, columns are months since first payment. Empty cells are unobserved, not zero.

Used
Typical stack

Read as text

Ad spend and actual payments enter the measurement pipeline.
AppsFlyer, Adjust, AppMetrica and SKAN connect source activity with attributed revenue.
Spend, payments and attribution meet in one shared data layer for unit economics and forecasting.
Tableau, Looker and Google Sheets surface the shared data in dashboards.
Payback, forecasting, pacing rules, alerts, and kill and scale logic turn dashboard reading into decisions.
Illustrative campaign: $3,000 spend, 120 platform purchases versus 90 payments, first-charge ROAS 0.90, cumulative ROAS 1.26 in month two on a 40% renewal assumption.

Fit

Who it is for

Subscription and app businesses that spend on Meta, TikTok, Google or Apple Search Ads and see platform numbers that disagree with payment numbers.
Teams running web funnels, where the purchase happens on a web checkout and has to reach the ad platform.
Growth leads who need CAC, LTV and payback by cohort in one dashboard.

Out of scope

Who it is not for

Businesses with no paid spend yet. Attribution needs volume to read.
Teams that want a data warehouse project for its own sake. The scope starts with the decisions the data has to support.
Anyone expecting a guaranteed lift in ROAS. The work improves measurement, and results depend on product and market.

What a marketing analytics consultant does

Cost per payerWhat each channel costs per paying customer.
Revenue over timeWhat that customer returns after the first charge.
Time to paybackHow fast cumulative revenue covers spend.

Tracking, dashboards and forecasts exist to answer those three questions with numbers you can defend in front of finance. Platform reports count conversions the platform attributed to itself. Attribution windows, modeled conversions, consent loss and iOS limits pull that figure away from payments, so the analytics layer reads the payment first.

Attribution and server-side tracking

Server-side tracking sends events from your backend or payment processor to the ad platform, instead of relying only on the browser. For a Stripe web funnel, a successful payment triggers a Conversions API event with the same event name and event ID as the browser pixel event. Meta deduplicates the pair within 48 hours and counts one purchase. Trial starts follow the same pattern when the browser also fires the event. Renewals have no browser event, so the server sends them alone, with action_source set to system_generated.

Event design: which events fire (view content, begin checkout, trial, purchase, renewal) and which parameters they carry.
Deduplication and match quality: shared event IDs between browser and server, and customer parameters hashed and sent within platform rules.
Consent: tracking follows the consent state collected on the page, in line with GDPR and ePrivacy rules for EU traffic.
Mobile attribution: AppsFlyer or Adjust configuration, SKAN conversion mapping and a view of the web-to-app handoff.

Dashboards for CAC, LTV, ROAS and payback

An LTV dashboard for a subscription app joins three sources: ad spend by campaign, attributed payments, and renewals by cohort. The output is a short set of views, each tied to one decision.

The build uses the BI tool you already run, or Tableau or Looker, and sits on a warehouse such as BigQuery when data volume calls for one.

Spend to paymentBy channel, campaign and creative, refreshed daily.
Payback curveThe day cumulative revenue passes cumulative spend.
LTV by cohortBy cohort and plan, with a stated churn assumption beyond the observed window.
Forecast viewTurns current cohorts into next month's revenue and budget.

Worked example: platform numbers versus payments

Illustrative

Illustrative example, not a client result. One campaign. Arithmetic is in the table.

Illustrative example, not a client result. Platform-reported purchases versus Stripe-matched payments for one $3,000 campaign.
Metric	Platform report	Payments	Read
Spend	$3,000	$3,000	Same input
Purchases	120	90	Platform counts more
CPA	$25	$33.33	$3,000 / count. Payments 33% higher
Month 0 revenue	n/a	$2,700	90 x $30. Short of spend
Month 0 ROAS	n/a	0.90	$2,700 / $3,000
Month 1 added revenue	n/a	$1,080	40% renew x 90 x $30
Cumulative revenue	n/a	$3,780	Pays back in month 2
Cumulative ROAS	n/a	1.26	$3,780 / $3,000

The scale decision rests on payment ROAS and the renewal assumption, not on platform CPA.

The stack

The list below holds the tools used. Options for a build depend on what you already run: a warehouse such as BigQuery, SQL models, and scheduled scripts for refresh and alerts.

Tracking and attribution: Meta Pixel, Conversions API, AppsFlyer, Adjust, AppMetrica, SKAN.
Funnels and billing: Stripe, FunnelFox, RevenueCat, Adapty.
BI: Tableau, Looker, Google Sheets.

UsedConversions APIGoogle SheetsMeta PixelRevenueCatAppMetricaFunnelFoxAppsFlyerTableauAdaptyLookerStripeAdjustSKANMetaGoogle

Typical stackScriptsTypical stack

Product names and logos belong to their owners. They appear only to show tools used or typical in this work. No endorsement or partnership is implied. To request removal of a logo, use the contact form. A dashed outline marks a typical stack tool: an example of what a system like this runs on, listed without a claim of past use. Letter tiles are monograms drawn for this site and are not the owners' logos. Apple, App Store, iOS, SKAdNetwork, Google, Google Play and Android are trademarks of their respective owners.

How it runs

Process

01
Tracking audit
Compare platform events with payments for a recent period and list what is missing, duplicated or late.
02
Event design
Define the events and parameters, then build server-side events from the payment processor.
03
Data layer
Join spend, attributed payments and renewals in one place.
04
Dashboards
Build the views above and agree the definitions of CAC, LTV and payback with finance.
05
Forecast and cadence
Add the forecast view and a weekly review of the numbers.

Scope

What an engagement sets up

01
Platform numbers and payment numbers reconciled, with the gap explained
02
CAC, LTV and payback by cohort in one place
03
A forecast that sets the next month's budget

The work

What you receive

Tracking audit and event map
A list of what fires, what is missing and what is duplicated, with a target event map.
Server-side events
Conversions API events fired from your payment processor, deduplicated against browser events.
Dashboards
Spend to payment, payback curves, LTV by cohort and a forecast view.
Metric definitions
A written definition of CAC, LTV, ROAS and payback, so marketing and finance read the same numbers.

FAQ

Common questions

When should I hire a marketing attribution consultant instead of buying software?

Hire a consultant when the software is in place but the numbers disagree, or when setup choices such as events, windows, consent and deduplication need an owner who understands the ad accounts. Software reports whatever it receives, so the consultant's job is to fix what goes in.

Is server-side tracking worth it?

For web funnels where the purchase happens on a web checkout, in most cases yes, because the payment is the strongest signal you have and browsers lose some events. The value depends on your spend and traffic mix. An audit compares platform events with payments first and shows the gap before you build anything.

What should an LTV dashboard for a subscription app show?

Spend to payment by channel and campaign, a payback curve by cohort, LTV by cohort and plan with a stated churn assumption, and a forecast view.

Do you build the data warehouse?

Only when volume and questions call for one. Many subscription businesses get far with the payment processor, an MMP export and a BI tool. If a warehouse such as BigQuery is justified, it is scoped as part of the build.

Does server-side tracking comply with GDPR?

GDPR applies to server-side events in the same way as to browser events. Hashed emails and phone numbers are pseudonymised, which keeps them personal data, so consent or another legal basis is still needed. Send server-side events only for users who consented where consent is required, and hash identifiers as the platform specifies. Your legal counsel decides the consent setup, and the build follows it.