The Rise of Incrementality Testing in Mobile Marketing: A Practical Guide for Q1 FY25

Mobile marketing has never had a greater divide between what your data states and what your campaign is actually delivering. Attribution, which was once the bedrock of performance measurement, has become more unreliable than ever. With Apple rolling out App Tracking Transparency (ATT), the evolution of SKAdNetwork, and increasing limits on cross-app identifiers, marketers find themselves in a landscape where data loss is the rule, not the exception. 

The consequences of this are very real. You are potentially spending budget on channels or creatives that don’t create incremental growth. You are probably scaling campaigns that show your dashboards working but won’t drive net-new users. And, you are likely scrambling to justify ROI as you have nowhere near enough evidence to prove what is really working.

This is exactly why incrementality testing is being brought to the forefront of attention in Q1 FY25. Incrementality isn’t merely a measurement technique, but a strategic framework to help you separate the actual outcome, reduce fade from your performance metrics, and make confident decision-making in a privacy-first ecosystem.

In this guide, we’ll cover what incrementality testing is, how it works, and how you can apply to your iOS, Android, and cross-platform campaigns. You’ll discover how platforms such as Apptrove are helping scale privacy-compliant testing at a time when marketers have identified their need for clarity as never before.

Why Marketers Are Finally Prioritising Incrementality Testing

There’s a silent reckoning happening in mobile marketing. For years, attribution reports have been treated as holy scripture. Clicks received credit, impressions were weighted, conversion windows existed to dictate what works and what does not. The numbers were clean, until they weren’t anymore.

Incrementality testing has evolved not just into a necessity for mobile marketers, but a necessity that has a strategic priority. Given the increasingly privacy-restricted and signal-poor digital ecosystem, marketers are tasked with the increased demand for marketers to prove their campaign’s actual impact. 

While traditional forms of attribution exist, they offer marketers insufficient clarity, let alone comfort, in performance assessments. Incrementality testing provides clarity for many of the traditional digital marketing principles by identifying the true causal effect of marketing activities, hence allowing performance teams to isolate what campaigns generate net-new conversions and which do not. 

This increase is driven by three major contributors: the decline of deterministic attribution, the rise of privacy-first measurement protocol and an increased urgency to optimize ad spend in the wake of increased economic scrutiny. The resounding conclusion is that marketers require systems of measurement that convey precision, compliance, and business impact and incrementality testing ticks all three boxes.

1. Attribution Is No Longer Sufficient

Attribution models were originally established based on an ecosystem whose tracking was persistent and user-based. Within such a context, last-click, multi-touch, and even probabilistic models provided directional insight around users. With changes in operating systems like Apple’s App Tracking Transparency (ATT) and SKAdNetwork (SKAN), the assumptions about the models have been severely disrupted.

Currently, attribution has some major limitations:

• Signal degradation: Access to user identifiers is at an all time low, and attribution data is often incomplete or modeled.
• Channel overlap: Users exist in many different touchpoints across web, app, and CTV, making it tough to assign credit correctly.
• Over-attribution risk: Attribution models can attribute conversions to campaigns that did not meaningfully influence the outcome.

Marketers have a great risk of misallocating large portions of their budgets if they do not follow a methodology that separates correlation from causation.

2. Privacy Regulations Are Redefining Measurement Frameworks

Policies around data collection have fundamentally changed what it means to be able to act on data. With Apple launching the ATT framework, and the movement towards a global privacy-based data policy (GDPR, CCPA, etc.), marketers don’t have the complete picture anymore.

Even with SKAN workarounds or aggregated reporting, marketers have to now make decisions with less deterministic data, fewer identifiers, and shorter attribution windows.

While these changes will impact reporting, they will also impact how you plan your campaigns, set KPIs, and demonstrate value. Since then marketers have dealt with:

• Lack of access to user-level insight
• Increased reliance on aggregate, anonymised reporting
• Delayed postbacks and reduced granularity

Incrementality testing provides a compliant alternative. Utilising group-based experiments as opposed to measuring individuals it assesses performance based on statistical outcomes. This design will naturally conform to the privacy requirements providing not only a valid measurement tool but a measurement tool for the future. 

3. Economic Pressure Is Driving a Focus on Verified ROI

With marketing budgets under scrutiny, especially in capital-sensitive verticals like fintech, gaming, and eCommerce, performance teams must now justify every dollar spent. The ability to demonstrate lift, rather than simply activity, is becoming the baseline for decision-making.

Incrementality testing enables:

• Smarter budget allocation: By identifying campaigns that drive net-new users or purchases.
• Creative and channel testing at scale: Without dependence on modeled data.
• Confidence in pause/scale decisions: With statistically valid control groups validating outcomes.

In this way, incrementality is not just a measurement method, it becomes a strategic lever for performance optimisation.

4. From Measurement to Strategy: A Shift in How Marketers Operate

The broader implication of incrementality testing lies in how it changes the role of measurement. Rather than serving as a retrospective report, incrementality enables:

• Forward-looking strategy: Insights that inform future investment decisions, not just evaluate past ones.
• Cross-functional alignment: Shared understanding of impact across marketing, product, and finance teams.
• Agility in experimentation: Rapid testing of creatives, offers, channels, and messaging, backed by validated results.

This aligns directly with how high-performing marketing organisations are evolving. Measurement is no longer a passive report. It is a decision-making engine.

What Is Incrementality Testing in Mobile Marketing, Really?

In essence, incrementality testing assesses whether your marketing efforts have added value or just captured the value that would have happened regardless.

Imagine you ran an ad campaign that resulted in 10,000 installs for your app. Attribution could report that your campaign drove all 10,000 installs, but what if 7,000 of those users would have installed the app without seeing your ad? That would mean that your actual impact, which is your incremental lift, was only 3,000 installs.

Incrementality establishes the difference between causation and coincidence. It measures the extra conversions your campaign created above the baseline of what would have happened organically. This is what makes it so powerful, especially as attribution continues to face headwinds and privacy concerns further reduce visibility.

When you start focusing on causation, you stop crediting ad spend for results it hasn’t actually driven. You stop scaling campaigns that were simply harvesting intent rather than creating intent. You start curating your media strategies on results that build your organization and not just those that build cool dashboards for reports.

The Core Concept: Test vs. Control

Incrementality testing is an experiment at its core. It splits your audience into two groups:

Test group: Users who are exposed to your ad

Control group: Similar users that are held out of the experiment and do not see your ad

And by measuring the conversion rates of the two groups, you can isolate the true lift the campaign has created.

Formula:

Incremental Lift = (Conversion Rate in Test Group) – (Conversion Rate in Control Group)

If the two groups convert at the same rate, your campaign did not create any incrementality. If the test group converts at a higher rate than the control group, you have proven your campaign created incrementality, and by exactly how much.

How Incrementality Complements Attribution

To be clear, incrementality testing is not a substitute for attribution. Attribution and incrementality represent two different uses.

While attribution demonstrates where a conversion actually came from, incrementality demonstrates whether that source actually caused the conversion.

When both are used in tandem, they provide a fuller picture of performance. Attribution helps you illuminate the journey; incrementality helps you understand what actually drove the outcome.

Different Types of Incrementality Testing and When to Use Them

By now, you’ve got the gist of what incrementality testing is, and why it is important. But a bit of a catch os that there isn’t one simple way to test for lift. 

Your campaign objective, budget size, access to data, and platform limitations (such as SKAN on iOS) will influence how you construct your test, and therefore, what kind of insights you get in return. 

Here are the most common forms of incrementality testing, as well as how each one works, their relative strengths and weaknesses, and when you should use them.

1. Geo-Based Incrementality Testing

Your campaign is set to launch in select geo regions (e.g., test markets) while further geo regions purposely remain only in control markets. Assuming you control for baseline trends, and compare performance across regions, you have an estimate for incremental lift. These are good for broad reach campaigns, paid social, big programmatic budget. The reason why it works well is  because geos can be naturally isolated and compare at scale. It is not necessary to have user level data, but you only need regional KPIs.

from Apptrove https://apptrove.com/incrementality-testing/
via Apptrove