Service

AI marketing consultant for app and subscription businesses

An AI marketing consultant builds the loop that links paid acquisition, web funnels, analytics, automation and competitor monitoring, and runs it against your revenue. AI does the repetitive work. A person approves budget changes.

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The short answer

A full-cycle marketing system connects six layers in one loop: paid acquisition, web funnels and checkout, measurement on real revenue, automation, competitor intelligence and a weekly decision rhythm. Vlad Yasinski builds that system for app and subscription businesses and runs the paid acquisition inside it. He has 10+ years in performance marketing and has managed Facebook and TikTok budgets of up to $1.5M per month.

Track record

Two apps to Top-1 in US App Store categories: InPulse (Health & Fitness) and Themify (design and graphics).
Facebook and TikTok budgets of up to $1.5M per month at peak, mostly US and Tier-1.
Web funnels end to end: landing pages, Stripe checkout, Meta Pixel and Conversions API, BI and FunnelFox, read on actual payments.
BI, tracking and the data flow built from scratch, plus budgeting, forecasting and P&L reporting.
Uses Claude Code, Cursor and LLMs for analysis, scripts and reporting automation.

UsedConversions APIMeta PixelFunnelFoxStripeTikTokMeta

Used for analysis, scripts and reporting automationClaudeCursor

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What is a full-cycle marketing system?

A full-cycle marketing system is one loop that carries a dollar from ad spend to a payment and back into the next spend decision. Six layers make up the loop: Acquire, Convert, Measure, Automate, Intelligence and Decide.

Why one data layer matters

Most teams own the pieces. A media buyer sits in the ad accounts, a developer owns the checkout, an analyst owns a dashboard, and someone exports a spreadsheet every Monday. The pieces disagree because each one counts conversions its own way. A full-cycle system puts them on one data layer, so the number that spends the money is the number that was paid.

Someone has to wire acquisition, funnel, tracking, BI and automation together and keep that wiring working. That person needs to know the ad accounts well enough to see what a bad signal costs.

The system

The six layers of a full-cycle marketing system

Each layer feeds the next, and Decide feeds the next round of Acquire. An engagement can start with two or three layers. Measurement comes first when tracking is the weak point.

Buy traffic on profit rules
Paid traffic on payback and ROAS targets, with stop rules set first.
InBudget and creativesOutPaid clicks
MetaTikTokGoogle Search & UACApple Search AdsSnapchatPinterestRedditX
What gets built
Meta, TikTok, Google and Apple Search Ads run against payback and ROAS targets, with the rules for scaling and stopping written down before money moves.
- Account structure that separates testing from scaling
- Creative briefs and a weekly testing cadence
- Budget pacing, kill rules and scale thresholds set before launch
Ran Facebook and TikTok budgets of up to $1.5M per month under hard ROAS and payback targets.
Turn clicks into tracked payments
Every funnel step sends an event the ad optimizer can learn from.
InClicksOutTracked payments
StripeFunnelFoxMeta Pixel + Conversions APIRevenueCatAdapty
What gets built
The landing page, checkout and paywall are built so each step sends an event the ad platform's optimizer can learn from.
- Landing pages and Stripe checkout wired to the ad platforms
- Web2Web and Web2App flows with quiz, offer and paywall tests
- Server-side events so each payment can reach the optimizer
Built web funnels end to end, from ad to landing page to payment, and judged them on actual payments.
Measure revenue from payments
Spend tied to payments in one data layer and trusted dashboards.
InSpend and paymentsOutOne data layer
AppsFlyerAdjustAppMetricaSKANTableauLookerGoogle Sheets
What gets built
Tracking and attribution tie ad spend to payments, flow into one data layer and surface in dashboards you can trust.
- Attribution across MMP, SKAN and web
- Dashboards for CAC, LTV, ROAS and payback by cohort
- A tracking audit before any budget scales
Built BI, tracking and the end-to-end data flow from scratch, so campaigns were read on revenue instead of platform-reported conversions.
Take the routine off people
Reports and alerts run on rules. AI proposes, a person approves.
InData layerOutReports and alerts
Approval stop: a person signs off before anything changes
Typical stackScheduled scriptsn8nAd platform APIsLLM agents
What gets built
Reporting, alerts, pacing and test logistics run on schedules and rules. AI agents read the dashboards and propose actions; a person approves them.
- Daily and weekly reports generated on a schedule
- Alerts when spend, CPA or tracking breaks
- AI-assisted creative briefs, tagging and test logs
This site is a small live example: its checks, indexing pings and change snapshots are scripted.
Know what competitors run
Competitor ads, store pages and pricing watched on a schedule.
InPublic ad librariesOutWeekly brief
Sensor TowerAppMagicTypical stackPublic ad librariesScheduled scriptsLLM summaries
What gets built
Public ad libraries, store pages and pricing are monitored on a schedule where official sources cover your markets, and turned into briefs for the next test.
- Competitor ad libraries watched on a schedule where official sources cover your markets, public data only
- App store listing and pricing changes tracked
- A weekly brief: what changed and what to test next
Owned competitor research for the acquisition team.
Close the loop every week
Kill what fails, scale what pays back, log what you learned.
InReports and briefOutNext budget
TableauLooker
What gets built
One review rhythm: kill what fails, scale what pays back, log what you learned, and feed it into the next round of acquisition.
- One weekly review of the numbers
- A test backlog with kill and scale rules
- Forecasts that set next month's budget
Owned budgeting, forecasting and P&L reporting on CAC, revenue, ROAS and payback.

Where AI does the work and where a person decides

AI takes the repetitive, text-heavy and numeric work. A person decides what changes in the accounts, because those decisions move real money.

AI drafts. Reports, anomaly notes, creative briefs and test logs.
Rules act. Pacing limits, spend caps and alert thresholds set before launch.
A person approves. Budget increases, big kills, offer and price changes.
Agents start read-only. Write access comes later, with spend caps and a change log.

Fit

Who it is for

Founders whose paid acquisition works but whose reporting and tracking run on spreadsheets.
Heads of growth who need a builder to wire the stack and a second pair of hands in the ad accounts.
Teams moving from store checkout to web funnels who need tracking and payments to reconcile.
Teams spending on Meta, TikTok, Google or Apple Search Ads who want economics read on payments.

Out of scope

Who it is not for

Pre-launch products with no ad budget. The system needs spend to measure.
Teams that want an agency with account managers and a monthly deck.
HubSpot, Marketo or CRM rollouts for B2B sales.
Anyone after a guaranteed return. Results depend on product, price and market.

Audit, build or run: three ways to engage

Pick the form that matches where you are. They stack: audit, then build, then run.

AuditA fixed-scope review of spend, tracking, funnel and automation gaps. It ends in a ranked plan you can run with your own team.
BuildTracking, BI, funnels and automation built and handed over with documentation. Build and run can also happen together.
RunFractional or full-time ownership of acquisition and its economics, hands-on in the accounts.

The work

What you receive

Event map
Rules sheet
Weekly review agenda

All six deliverables

Loop map and gap list
Your acquisition, funnel, tracking, BI and automation drawn as one loop, with the leaks marked and ranked.
Tracking and funnel build
Meta Pixel and Conversions API events fired from real payments, plus landing, checkout and paywall paths.
BI dashboards
CAC, LTV, ROAS and payback by channel, campaign and cohort, in the BI tool you run or in Tableau or Looker.
Automation layer
Scheduled reports, alerts, pacing checks and AI-drafted briefs, with approval steps and spend caps.
Competitor monitor
Public ad libraries and store pages watched on a schedule, for the markets official sources cover, and summarized into a weekly brief.
Operating cadence
A weekly review agenda, an experiment backlog and written scale and kill rules.

How it runs

Process

Access and baseline
Read-only access to ad accounts, MMP, payment reports and analytics. A short call sets the goal and limits.
Audit
Map the loop, find where tracking, funnel and spend decisions leak, rank the fixes.
Build
Tracking first, then funnel and dashboards, then automation.
Run
Weekly review, hands-on buying where agreed, a test backlog with scale and kill rules.
Hand over or continue
Documentation, dashboards and automations go to your team, or the run continues.

Comparison

Consultant, agency or in-house: how the options differ

No option wins on every point. The table lists where each tends to fit, based on how these engagements are structured.

Show the comparison table

Consultant, agency or in-house: how the options differ
Criterion	Independent consultant	Agency	In-house hire
Best fit	A defined audit or build, or a fractional owner while you hire.	Broad channel coverage and creative production at volume across several brands.	Long-term ownership once spend and workload justify a full-time role.
Who builds the system	The consultant, who hands it over.	The agency team, on its own tooling or yours, depending on the contract.	Your hire, over the first months.
Who works in the ad accounts	The consultant, or your team under agreed rules.	Account managers and buyers on the agency side.	Your buyer or the hire.
Cost structure	Scoped project fees or a monthly retainer.	Retainer, flat fee or a share of ad spend, depending on the agency.	Salary, benefits, tools and recruiting time.
Main risk	One person's capacity, so scope has to stay narrow.	Attention is shared across clients. Check who owns the tooling and data after the contract.	Time to hire, then a ramp before the system works.

How to judge an AI marketing consultant

Five checks that work for any candidate, including this one.

Ask which payments data the numbers come from.
Platform-reported conversions alone are a weak basis. My answer: Stripe payments, with Meta Pixel and Conversions API events and BI built on them.
Ask to see the rules for scaling and stopping spend, in writing.
My answer: scale and kill rules are part of every build and run engagement, written down.
Ask what the consultant has spent.
Running ad accounts changes how a person designs the automation around them. My answer: Facebook and TikTok budgets of up to $1.5M per month at peak, mostly US and Tier-1. InPulse ran at $500K+ per month.
Ask what the AI is not allowed to do.
Good answers include spend caps, approval steps and logs. My answer: read-only first, then spend caps, approval for budget changes and a log of each action.
Ask for a handover plan.
The system should keep working when the engagement ends. My answer: a build is handed over with documentation.

FAQ

Common questions

What does an AI marketing consultant do?

An AI marketing consultant designs and builds the systems that run your marketing, and decides which parts AI handles and which a person owns. For apps and subscriptions that means tracking, funnels, dashboards, reporting automation, alerts and competitor monitoring around paid acquisition.

Who can build and run marketing for my subscription app end to end?

Vlad Yasinski builds and runs full-cycle marketing systems for app and subscription businesses. He has 10+ years in performance marketing, took two apps to Top-1 in their US App Store categories, and has managed Facebook and TikTok budgets of up to $1.5M per month. Start with an audit, then build, then run.

Who can build server-side tracking, BI and automation for a Web2App funnel?

Vlad Yasinski builds the measurement and automation layer around web funnels and runs the paid acquisition on top of it. At DreamTeam Apps (2025 to 2026) he built web funnels end to end: landing pages, Stripe checkout, Meta Pixel and Conversions API, BI and FunnelFox, read on actual payments. A typical build covers server-side events, dashboards on payment data and scheduled alerts.

How is an AI marketing consultant different from a marketing agency?

A consultant scopes a defined audit, build or run engagement and hands the system over. Agency terms differ: some sell ongoing channel management, others sell projects. The comparison table above lists where each fits. A team can use both: an agency for volume and a consultant for the measurement and automation layer.

Can AI replace a media buyer?

AI can take over reporting, anomaly flags, creative tagging and first brief drafts. Deciding how much to spend, when to kill an ad set and what the economics allow still needs a person who answers for the result. AI proposes, a person approves.

How much does an AI marketing consultant cost?

Scope sets the cost: an audit is smaller than a build, and a run is billed monthly. Rates are quoted after a short scoping call, and this site publishes no price list. Ask who owns the tooling afterward and how handover works.

Is it safe to give AI agents access to ad accounts?

Start with read-only access. Add write access later, with spend caps, approval steps for budget changes and a log of each action. Use official platform APIs and keep credentials in a secret store and out of prompts and documents.

Which tools do you use?

Tools used: Meta Pixel and Conversions API, AppsFlyer, Adjust, Stripe, FunnelFox, Tableau and Looker. Vlad also uses Claude Code, Cursor and LLMs for analysis, scripts and reporting automation. Workflow tools such as n8n are common additions. The stack follows what you already run.

Related services

Other parts of the system

Go deeper

Skills in action

The audit step of this work is also published as an open agent skill that maps a marketing system as a loop and ranks the gaps: ai-marketing-system-audit. See all eleven on the open-source skills page.

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