Why we built AdShift: a measurement layer mobile teams can trust

In mobile marketing, the problem is rarely a lack of data.

The harder problem is knowing which numbers to trust.

Meta shows one result. Google shows another. TikTok shows a third. Your MMP may deduplicate some of it, your analytics dashboard may disagree with revenue, and your ad mediation dashboard may show value that is difficult to connect back to acquisition source.

Then someone has to make a budget decision.

Which campaign should be scaled? Which source should be paused? Which users are actually valuable? Which dashboard is close enough to reality to defend in a growth meeting?

That is the problem AdShift was built to solve.

We are not building another dashboard for mobile marketers to check once a week. We are building a measurement layer that helps app teams understand where users come from, which sources create value, where fraud appears, how cohorts behave, and how acquisition data connects to revenue.

What's inside?

• Why mobile measurement is harder now
• The gap we saw in the market
• Why our adtech background matters
• What AdShift is building
• Why we believe this can work
• What comes next

Why mobile measurement is harder now

The mobile app market has changed.

User acquisition is more expensive. Campaigns run across more channels. Privacy changes have reduced simple deterministic attribution. iOS, Android, paid social, search, ad networks, mediation partners and internal BI systems all create different versions of the same story.

At the same time, the pressure on marketing efficiency has increased. Growth teams are expected to prove that every marketing dollar is creating real value, not just attributed installs.

That is where many teams get stuck.

A dashboard that places Meta, Google and TikTok numbers next to each other feels useful, but it does not automatically solve the core problem. Each platform measures from its own perspective. Each has its own attribution windows, reporting logic and incentives.

The real question is not "which dashboard has the nicest interface?"

The real question is:

Which data can we trust when we decide where to spend the next dollar?

The gap we saw in the market

Large mobile companies have used MMP tools such as AppsFlyer, Adjust, Singular, Branch and Kochava for years. These platforms helped define the category and remain important parts of the mobile marketing ecosystem.

But for many smaller and mid-market app teams, adopting a full MMP stack can feel heavier than it should.

The cost can be hard to justify early. Implementation can require more technical time than the team has available. Some capabilities, such as fraud prevention, deep links, data export or advanced reporting, may sit behind add-ons or separate tools. And once the setup becomes complex, switching later feels risky.

Teams often end up with two imperfect options.

They either stay with basic analytics and manually reconcile numbers from ad platforms, Firebase, GA4, spreadsheets and revenue dashboards.

Or they adopt an enterprise-style stack before they have the budget, process maturity or technical capacity to use it well.

AdShift was created as a third path: MMP-grade measurement with a lighter adoption model, built for teams that want better decisions without unnecessary operational weight.

Why our adtech background matters

AdShift did not start as an abstract SaaS idea.

It came from people who have worked close to advertising technology, data infrastructure and systems where scale, latency, data quality and attribution logic are practical problems, not slide-deck concepts.

Part of our background comes from the RTB House ecosystem and the broader adtech market. We have worked around systems where large event volumes, fast processing, partner integrations and signal quality directly affect business outcomes.

That experience shapes how we think about mobile measurement.

An MMP is not just a reporting panel. Under the surface, it is a system that has to receive events from apps, understand advertising sources, process postbacks, filter fraud, map events, support deep links, move data into warehouses and translate all of that into decisions a marketing team can actually use.

It is an engineering problem, a product problem and a marketing problem at the same time.

That is why we are building AdShift as a technology product from day one, not as a services layer glued to a spreadsheet.

Who is building AdShift

AdShift is built by a team combining product, backend, infrastructure, SDK and adtech expertise.

Kamil Andziak leads backend architecture, attribution logic and the product's business direction. His role combines technical understanding of adtech systems with responsibility for making sure AdShift solves a real market problem, not just something that looks good in a demo.

Kacper Woźniak leads infrastructure, DevOps, scalability and the technical foundations of the platform. In a product like an MMP, reliability and infrastructure cost are not secondary details. They directly shape the customer experience and the economics of the product.

Przemysław Pramik brings analytics and SDK experience, which matters because integration often decides whether a measurement product works in a real app. SDK implementation, event taxonomy, deep links and data quality are often the biggest adoption barriers for mobile teams.

The broader team also brings engineering, mobile marketing and adtech execution experience, helping AdShift develop both the technical layer and the commercial use cases around it.

In this category, it is not enough to sell a strong promise. You have to deliver it: from SDKs, backend and infrastructure to dashboards, integrations and onboarding support.

What AdShift is building

AdShift is an MMP platform built in Poland for companies with mobile apps.

We help teams measure user acquisition campaigns, understand ROAS/LTV, analyze cohorts, detect fraud, manage postbacks, create deep links and connect marketing data with revenue data.

Put simply, AdShift helps answer the question every mobile growth team eventually asks:

Which campaigns are actually bringing valuable users, and which ones only look good in a dashboard?

We are building around five product principles.

Measurement should be neutral

Ad platforms are useful for campaign optimization, but each platform sees the world from its own perspective. A measurement layer should help deduplicate sources, organize events and create a more reliable view for budget decisions.

Installs are not enough

For many apps, value appears later: in retention, purchases, subscriptions or ad revenue. That is why AdShift focuses on source-level ROAS/LTV and cohorts, not just install counting.

Ad revenue needs to connect back to acquisition

Ad-monetized apps often have revenue in one dashboard and attribution in another. That makes it difficult to understand which sources are bringing users who actually generate value. AdShift is built to connect ad revenue back to acquisition source.

Integration should not be the blocker

We are developing AdShift Integrator and an AI-assisted workflow to help configure SDKs, events, partners, deep links and postbacks. The goal is to lower the adoption barrier, especially for teams without a large technical department.

Data should be portable

AdShift combines MMP functionality with CDP-like capabilities, including raw data export to tools such as BigQuery or Snowflake without adding more SDKs and integration layers.

Why we believe this can work

We believe AdShift can work because the problem is real, the category is mature, and there is still room for a product that is easier to adopt.

The first advantage is team experience. We are building in a category we understand from the inside: data, adtech, backend systems, infrastructure, SDKs, partner integrations and mobile marketing workflows.

The second is architecture. AdShift is being built from the ground up as a lightweight, modern platform based on technologies such as Go, microservices and cost-aware data processing. Infrastructure cost matters in an MMP because it affects pricing, scalability and long-term customer value.

The third is accessibility. We want advanced mobile measurement to be available beyond the largest companies with enterprise budgets. A team should be able to start with a focused pilot, prove value and expand from there.

The fourth is real product usage. AdShift is not only a prototype. The platform is already used in production scenarios, and customer and partner conversations confirm that the problem exists across multiple segments: ad-driven apps, marketplaces, e-commerce, subscriptions, utility apps and mobile gaming.

The fifth is local execution as an advantage, not a limitation. We are starting from Poland, but the problem is global. Mobile teams need support that understands implementation work, regulation, budgets and day-to-day operations, while still building for a global software category.

What we want to change

We want mobile teams to stop making budget decisions from incomplete or disconnected data.

We want smaller and mid-market companies to use MMP-grade measurement without feeling forced into a heavy enterprise stack from day one.

We want UA, monetization, product and leadership teams to look at the same decision layer instead of comparing dashboards that were never designed to tell exactly the same story.

And we want MMP implementation to feel less risky. Good analytics should help teams grow their apps, not become another source of technical debt.

What comes next

AdShift is evolving into a full MMP platform for mobile apps: from attribution, fraud prevention, deep links and postbacks to ad revenue attribution, cohorts, ROAS/LTV and warehouse data export.

The next stage is continued pilots, customer implementations, partner development and a safer path for teams to test AdShift without disrupting their existing stack.

If you run a mobile app and your dashboards do not answer the most important question - which campaigns are actually bringing valuable users - this is the problem we are building for.

We are not building another dashboard.

We are building a measurement layer mobile teams can trust.