A mobile measurement partner (MMP) is a neutral third party that tells you which marketing actually drove your app installs and in-app revenue. Every ad network will happily claim the same install, so an MMP sits in the middle, applies one consistent set of rules, and decides who gets the credit. Without one you are reading marking-their-own-homework numbers from six different platforms and quietly paying for the same user more than once.
Mobile measurement partners have been doing that for a decade. What has changed, and changed hard in the last eighteen months, is the ground the whole category stands on. If your understanding of mobile attribution was formed around IDFA and SKAdNetwork, this guide is the update: what an MMP does now, how the major platforms actually differ, and how to choose one without buying an expensive dashboard nobody opens.
What a mobile measurement partner actually does
Strip away the marketing and an MMP has four jobs.
- Attribution. When someone installs your app, the MMP decides which click, view or campaign gets the credit, using one rulebook applied identically to every network.
- Deduplication. When Meta, Google and TikTok all claim the same install (and they will), the MMP awards it once. This alone is usually the business case.
- In-app event measurement. Installs are vanity. The MMP tracks what happens next, registration, trial, subscription, purchase, and ties that revenue back to the source.
- Cost and ROI aggregation. It pulls spend from each network and puts it next to the revenue, so you can see genuine ROAS per channel instead of six incompatible reports.
The neutrality is the point. An ad network measuring its own performance has an obvious incentive; an MMP charging you a flat fee does not care which channel wins.
What changed: the 2026 attribution landscape
Three shifts matter, and most guides still on page one of Google predate all of them.
Apple replaced SKAdNetwork with AdAttributionKit
Apple skipped the long-expected SKAdNetwork 5.0 entirely and replaced the framework with AdAttributionKit (AAK), which gained significant capability in iOS 18.4. AAK is broadly SKAN’s successor with two meaningful upgrades: it supports third-party app marketplaces, not just the App Store, and it allows configurable attribution windows rather than Apple’s fixed ones.
The practical caveat is adoption. The frameworks are interoperable, and the large ad platforms have moved at very different speeds, with plenty of spend still measured through older SKAN versions. So in practice you are running two attribution models side by side and reconciling them. Any MMP you consider must handle both without you thinking about it.
Google cancelled Privacy Sandbox on Android
This is the one that catches people out. In October 2025 Google shut down the entire Privacy Sandbox initiative, retiring the Attribution Reporting API, Topics, Protected Audience and nearly everything else it had spent six years building. If you attended a workshop about preparing your Android measurement for the Attribution Reporting API, that work is now moot.
The consequence: the Google Advertising ID (GAID) is still alive, with no announced deprecation date. Android attribution therefore remains materially easier and more deterministic than iOS. We would still not build a ten-year measurement strategy on an identifier whose future is entirely at one company’s discretion, but the panic deadline people were planning around has evaporated.
Consent-based measurement is now the norm, not the exception
ATT opt-in rates have settled at a low level globally, which means the deterministic, user-level iOS measurement many teams still quietly assume simply is not there. Modern MMP stacks compensate with aggregated and probabilistic methods, and increasingly by feeding incrementality testing and media mix modelling rather than pretending per-user attribution still works. Treat any vendor promising to restore full deterministic iOS attribution with deep scepticism.
The main mobile measurement partners, and what each is good at
The realistic shortlist for most companies is AppsFlyer, Adjust, Singular and Branch, with Kochava, Tenjin and Airbridge as credible alternatives depending on budget and region. They all do the four core jobs. They differ in emphasis.
- AppsFlyer leads on ecosystem breadth: the widest set of network integrations and partner connections, which matters when you run twenty channels rather than four. It is also the category’s financial heavyweight, having raised a Series E of more than a billion dollars in June 2026 from investors including Google, Meta, Moloco and Unity at a valuation around 2.7 billion.
- Adjust leads on pricing transparency and developer experience, with a strong reputation for real-time fraud prevention and analytics depth. If predictable cost and clean implementation matter more than sheer integration count, it is usually the shortlist favourite.
- Singular leads on cross-channel cost aggregation. Its origin is marketing analytics rather than attribution alone, so if your hardest question is “what did we actually spend and what came back across every channel”, it tends to answer it with least assembly.
- Branch leads on deep linking and web-to-app journeys. If a large share of your users start on the mobile web and you care about routing them into the app without losing context, that is Branch’s home ground.
One 2026 development applies across all of them: every major MMP now ships some form of AI assistant over your data. Treat these as a convenience feature rather than a differentiator. The quality of the underlying attribution and the cleanliness of your event schema decide whether those answers are worth anything.
How to choose a mobile measurement partner
Most selection processes start with a feature matrix and end in a tie. These are the questions that actually separate the options, roughly in the order we work through them with clients.
- Which networks do you genuinely buy on? Integration breadth only matters for the channels you use. Take your actual media plan, including the two channels you are piloting next quarter, and check each against the shortlist. This eliminates more vendors than any other question.
- What does the pricing model do at your scale? Most MMPs price on attributed conversions or MAU. Model it at your current volume and at three times that, because a plan that looks cheap at launch can become the second-largest line in your martech budget after a good year.
- How does it handle iOS today? Ask specifically how it reconciles SKAN and AdAttributionKit, how it handles campaigns below the privacy thresholds, and what it does when the aggregated data is simply too thin to attribute. The honesty of that answer tells you a lot about the vendor.
- What is the fraud position? Install fraud, click spam and SDK spoofing still quietly consume real budget. Ask what is detected in real time before attribution, versus flagged retrospectively in a report you will never read.
- Where does the data go afterwards? This is the question most teams ask last and should ask second. Can you get raw, event-level data into your own warehouse, BigQuery, Snowflake or otherwise? An MMP that only exposes its own dashboards caps what you can ever learn.
- What does implementation actually cost you? Not the licence: the engineering. SDK integration, event schema design and QA are where MMP projects overrun. Budget for the schema conversation, because it is the part that determines whether the data is useful.
The mistakes we see most often
- Choosing on integration count. Two hundred integrations is irrelevant if the six you need are equally well supported everywhere.
- Designing the event schema last. A rushed schema is the single most common reason an expensive MMP produces numbers nobody trusts. Decide what a meaningful event is, name events consistently, and define revenue once, before implementation, not after.
- Expecting iOS numbers to reconcile. They will not match your network dashboards, and chasing exact parity wastes quarters. Choose one source of truth for decisions and use the rest directionally.
- Treating the MMP as the whole measurement strategy. Post-ATT, per-user attribution answers a shrinking share of the real questions. Incrementality testing and media mix modelling answer “did this spend cause anything”, which is usually the question the board is actually asking.
- Never revisiting the decision. The framework landscape has changed twice in eighteen months. A choice made against the 2023 rules deserves a review.
Do you even need a mobile measurement partner?
Honest answer: if you have an app and you spend meaningfully on user acquisition across more than one network, yes. The deduplication alone usually pays for it.
If you run a single channel, spend is small, or you are pre-launch, the free and low-cost tiers (Tenjin and Singular both have accessible entry points) are the sensible starting point. And if you have no app at all, this is not your problem: web measurement is a different stack, closer to the server-side tagging and consent-driven world we write about elsewhere.
Where this is heading
The direction of travel is away from counting individual users and towards proving incremental business impact. Deterministic identifiers keep shrinking on iOS, survive for now on Android, and the frameworks replacing them are aggregated by design. The teams getting this right treat the MMP as one input into a measurement stack that also includes clean first-party data, warehouse-level modelling and genuine experimentation, rather than as the single source of truth it could plausibly claim to be in 2019.
Which is a longer way of saying: pick the MMP that fits your channels, your scale and your data ambitions, get the event schema right, and then spend your energy on the harder question of what your marketing actually caused.
If you would like a second opinion on your mobile measurement setup, whether that is choosing an MMP, fixing an event schema that has drifted, or building the warehouse layer underneath it, get in touch. It is the kind of problem we enjoy.
