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

Discover how incrementality testing is reshaping mobile marketing strategies in Q1 FY25, helping marketers measure true campaign impact and optimize ROI. For more; visit here: https://apptrove.com/?utm_source=thirdparty&utm_medium=organic&utm_campaign=free_backlinks

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

2. Introduction
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 a
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 are
helping scale privacy-compliant testing at a time when marketers
have identified their need for clarity as never before.
Apptrove

3. Prioritising Incrementality Testing
Why Marketers Are Finally
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.

4. 1. Attribution Is No Longer Sufficient
2. Privacy Regulations Are
Redefining Measurement Frameworks
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.
Marketers have a great risk of misallocating large portions of their
budgets if they do not follow a methodology that separates
correlation from causation.
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.
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.

5. 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:
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. 
n Lack of access to user-level insighp
n Increased reliance on aggregate, anonymised reportinc
n Delayed postbacks and reduced granularity

6. 3. Economic Pressure Is

Driving a Focus on Verified ROI
4. From Measurement to Strategy:
A Shift in How Marketers Operate
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.
In this way, incrementality is not just a measurement method, it
becomes a strategic lever for performance optimisation.
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.
Incrementality testing enables:
DSmarter budget allocation: By identifying campaigns that drive
net-new users or purchases?
DCreative and channel testing at scale: Without dependence on
modeled data?
DConfidence in pause/scale decisions: With statistically valid
control groups validating outcomes.

7. * 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.
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.
in Mobile Marketing?
What Is Incrementality Testing

8. The Core Concept: Test vs. Control
How Incrementality Complements Attribution
Incrementality testing is an experiment at its core. It splits your
audience into two groups:
And by measuring the conversion rates of the two groups, you can
isolate the true lift the campaign has created.
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.
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.
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

9. Testing and When to Use Them
Different Types of Incrementality
By now, you've got the gist of what incrementality testing is, and why it
is important. But a bit of a catch so 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.
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
budgets. The reason why it works well is  because geos can be
naturally isolated and compared at scale. It is not necessary to have
user level data, but you only need regional KPIs.
1. Geo-Based Incrementality Testing
Instead of holding users out entirely, control group users see a non-
promotional message (like a public service ad). This ensures the ad
placement is still occupied but not pushing your product or offer. It is
ideal for environments where inventory must be filled, such as YouTube,
OTT, or programmatic channels.
2. PSA (Public Service Announcement) Testing

10. Users in both groups "win" the ad auction, but the control group’s ad is
suppressed at delivery, which means the platform logs the impression,
but the user doesn’t actually see the ad. This design perfectly isolates
the effect of seeing the ad from just being in the bid pool. It mimics the
test-control design without wasting spend on PSA creatives.
3. Ghost Ad Testing
A fixed percentage of your eligible users (e.g., 10%) are intentionally
excluded from the campaign. The rest receive normal exposure. After
a defined time window, you compare conversion rates. It is simple and
effective, and works especially in owned channels where you control
delivery. Ideal for retention, upsell, and re-engagement strategies.
4. Suppression Holdout Testing
The same campaign is turned off and on over alternating time periods.
You compare conversions across those time windows to measure lift. It
is useful when audiences can't be easily split, such as on limited reach
channels.
5. Time-Based Testing

11. Incrementality Test (Step-by-Step)
How to Set Up Your First
You believe incrementality testing is now the future, and you're ready
to get started. The good news is that you don't need to be a data
scientist or have a million-dollar backing to do a credible
incrementality test. However, you do need to approach it in a
structured way. Incrementality testing is inherently a structured
experiment, as with any great experiment, incrementality testing
involves intention, balance, and scientific rigor to set it up and
conduct the test properly.
Below is an actionable step-by-step playbook to help you get your
first test going, aimed at the mobile marketer who prefers clarity over
ambiguity.
All good incrementality tests start with a focused question of what you
are trying to prove. The best hypotheses are specific, measurable, and
tied to the objective of your campaign. Let's say you are running a
push notification based campaign to reactivate churned users. A
strong hypothesis would be, “Sending a time-sensitive discount
message to churned users will lead to a higher reactivation rate than
churned users who do not see the message.” Avoid ambiguous goals
like "increase engagement." Make sure you identify the conversion
event (e.g., app install, purchase, reactivation), identify the audience,
and describe what effect you expect.
Step 1: Start With a Simple, Actionable Hypothesis

12. Step 2: Choose a Testing Method
That Matches Your Use Case
Not all incrementality tests are created equal. Your testing setup
should take into consideration variables such as type of campaign,
tools available, media channels and level of targeting control.
The point is that test design should fit your environment, not the other
way around.
Once you’ve selected your test format, the next step is segmentation.
This is where many tests fall apart. If your test and control groups are
not comparable, your results will guarantee little value. You will want to
segment your audience in two statistically similar cohorts. The only
actual difference between them should be ad exposure.
? If you're testing broad acquisition in multiple markets, you could
attempt geo-based testing
? If you are using CTV ads or in-app banners, PSA-based tests would
be good for ownership of the creative, and ensure your control
group sees something neutral, without influence
? For retargeting or CRM calculating and creating bases for CRM
things like emails or pushes, suppression holdouts anywhere a
portion of your audience was excluded would be fine
? If using a platform like Meta or a DSP that allows ghost ad setups,
in which you can suppress delivery of your control group but allow
them to still take part in the auction
? When it is not possible to segment audiences, time-based tests
where you toggle your campaign off and on by defined intervals
could work for you, giving directional lift signals.
Step 3: Segment Your Audience Cleanly

13. Maintain a balance of factors such as platform (iOS vs. Android), user
lifecycle stage, geography, and expected conversion intent. If you're
conducting a geo test, have a similar historical behavior and
seasonality for the test and control regions.
Randomization and scale is important here. A test with 200 users will
not provide the statistical power you need. You should be testing with
thousands, preferably tens of thousands, per group to provide useful
insight.
Now that you have your groups created, it's finally time to run the
actual campaign. Make sure that only your test groups are served your
campaign creatives. If you're running it in a holdout or ghost ad
method, ensure your control group is not served any impressions of
these ads.
If possible, run your campaign for long enough to collect meaningful
data in terms of behaviors. For actions such as installs or clicks, which
are typically more upper funnel, 7-10 days might be sufficient. For
actions like purchases of subscriptions, which are more lower funnel,
you might need to run the campaign for 14-21 days depending on how
long your user journey is.
During the campaign run, do not make any mid-test changes to
creative, targeting, or even your budget split, unless you are going to
restart that test. It is preferable to have as few changes as possible to
eliminate extraneous variables and to ensure that you are only
measuring the impact of the campaign creatives.
Your Campaign Carefully
Step 4: Launch and Monitor

14. Step 5: Measure Lift and Make It Meaningful
Don’t Just Chase Success
Step 6: Interpret, Iterate, and
Ultimately, the end of the campaign is time for analysis. You should see
how the conversion rates of your test and control groups compare. The
difference is your incremental lift, which is the value you created from
your campaign.
If your test group had a reactivation of 5.8% and your control had a
reactivation of 4.2%, you had a 1.6% lift. This is the 1.6% that the
campaign actually gave you, above the baseline, and that is your
evidence of impact. You could also calculate the Cost per Incremental
Outcome, if you divided total spend by the number of incremental
conversions. This also provides a way to compare the true ROI of
different campaigns, across channels and formats.
You could also calculate the Cost per Incremental Outcome, if you
divided total spend by the number of incremental conversions. This
also provides a way to compare the true ROI of different campaigns,
across channels and formats.
The last, and sometimes most disregarded, step is interpretation. Your
role is not only to confirm lift. Your role is to understand why lift
occurred and what your next move should be.
If the test did show lift, that's great. Is it possible to scale the
campaign or use the same creative thinking as it relates to another
campaign? If the test failed to show lift, that is still valuable. You have
saved future budget by confirming what does not drive results. Those
insights could be used to optimize creative, rethink targeting, and
adjust spend levels.

15. And very importantly, continue building a culture of testing.
Incrementality should not be viewed as a one-off diagnostic tool. The
most successful mobile marketers incrementality test consistently,
each quarter, each large campaign, and prior to any large shifts in
budget.

16. Optimizes Your Ad Spend
How Incrementality Testing
The way to make every dollar you have in a post-attribution world is a
harder thing to do.
Performance marketing used to be simple. You would go to your
attribution dashboard to see where conversions belong and spend
accordingly. Now, it's not that easy and ad spends are also harder.
Budgets are being scrutinized now more than ever. CPIs are
increasing, user acquisition is competitive, and privacy frameworks
have changed the way you measure success. Marketers are making a
move from the basic attribution of conversion counts to proof of
conversions and related behavior by using incrementality testing.
Marketers are using lift data to put more accurate spend allocations,
eliminate waste, and create strategies that are actually yielding return
on investment, and not just the appearance of return.
Often times typical metrics like impressions, clicks, or even attributed
installs don’t represent true impact. If a user was always going to
convert and your ad stepped in along the way, then you didn’t create
value, you simply took credit.
Incrementality turns this mindset upside down. You can see where true
growth is happening, where your campaigns are driving net-new
behavior, versus where you are just paying incrementally for that
organic or brand driven conversion.
This means you are now moving from optimizing for volume to volume
for value.
Shifting From Vanity Metrics to Value Metrics

17. Minimize Wasted Spend on Overlapping Channels
Justify Budget to Leadership with Confidence
You could easily be running user acquisition campaigns across five
paid channels. Attribution shows conversions across all five paid
channels, and consequently you assume each is driving results. But
after incrementality testing, you find two of them are cannibalizing
organic users, or even worse, are adding to overall performance with
little to no incremental impact.
That type of insight can save you thousands, and sometimes millions,
in spend. It helps you:
You are no longer guessing media overlap, but you are looking at how
each channel works independently, and how they work in
combination.
Marketing teams are being asked to substantiate they are not wasting
spend. Often times, attribution data alone is not appropriate for the
boardroom. Incrementality data is.
It arms you with:
? Proof that X% of conversions wouldn't have occurred without your
campaig?
? Understanding what tactics lead to real business impact, versus
attributed click?
? An accurate, privacy-compliant measurement model that is
consistent with where the industry is going
? Identify channels to prune that deliver minimal lift?
? Reinvest in high lift channels, especially if attribution is significantly
biased?
? Ensure you're not paying for the same user across two overlapping
platforms.

18. This is especially important in Q1. FY25 budgets get rolled out and
finance teams will tighten the screws. Being able to speak about
causal ROI will put you in a stronger place to earn the right to defend
current expenditure or to secure a greater budget if needed.

19. How to Operationalize It in Q1
Making Incrementality Testing a Habit:
Most marketers have run an incrementality test once. They got a lift
number, filed a deck, may have moved a budget line, and nothing
>8<72e<*><+:
But the real opportunity to make incrementality a forever process at
scale across all campaigns, channels, and quarters is here. And this
time, not as a side project, but as the default one.
Here is how to operationalize incrementality testing to help make this
a Q1 FY25 growth engine, not just a lone experimentation activity.
Incrementality testing shouldn’t live in a silo or be saved for “special”
campaigns. It needs to show up at three core phases of your
marketing process:
? Define your goal (installs, subscriptions, reactivations?
? Build a hypothesis to tes?
? Allocate 5–10% of your audience or budget for holdout/control


? Run ongoing tests on creatives, messaging, and offer?
? Evaluate incremental lift for each cohor?
? Pause or scale based on causal performance, not just attributed 


results
Start by Embedding It in Your Campaign Lifecycle
oO are-Launch Planning
nO iid-Campaign Optimization


20. Review performance across both test and contro
Calculate true cost-per-incremental-actio
Feed insights into your next media plan
3. Post-Campaign Analysis
This loop turns incrementality into a feedback engine that powers
better decisions across quarters.
The biggest barrier to operationalizing testing is not technical but
organizational. The best-performing teams assign a Testing Owner,
who is someone who is responsible for:
It does not have to be a new hire. It can be a role within your
performance team. The key is clarity and accountability. When testing
is everyone's responsibility, it usually becomes no one’s responsibility.
Just like you plan campaigns for quarters or sprints, so should you plan
testing too. Create a Q1 testing roadmap that includes a cadence of
experiments mapped to key business priorities.
Setting quarterly testing prioritie?
Designing and managing test-control split?
Centralizing learnings into a playboo?
Working across teams to apply those learnings
Designate Ownership with Your Team
Build a Testing Calendar, Not Just One-Offs

21. For example:
Week 1–3: Test incrementality of brand video ads across geos
Week 4–6: Run holdout test on push campaigns for dormant users

Week 7–9: Compare creative A/B variants using ghost ad suppression
Week 10–12: Test incremental ROAS across SKAN-eligible networks
This gives you some wiggle room to budget testing ahead of time and
makes sure experiments aren't deprioritized during busy campaign
cycles.
Over time, as you run more tests, you’ll begin to create internal
benchmarks. You will understand what lift means for you, your brand,
your product, and audience. But more importantly, you will start to
identify trends.
These are useful numbers for when you're assessing new campaigns
????o???????o??????
? UA campaigns on Android generate 3–5% incremental lif?
? Winback offers via push drive 10–15% lift among users having been
inactive for 30+ day?
? CTV ads deliver lower attributed conversions, but 2x higher
incremental reactivation
For example:
Set Internal Benchmarks for Incrementality

22. Every test, successful or not, builds your knowledge base. Don’t let the
data die in a deck. Record each data and its outcome, and create a
living testing playbook for your team. Include in the playbook:
Mobile marketing has changed in a way that attribution is no longer
enough. With user-level signals declining, overlaps increasing and
budgets getting tighter, the question is no longer just where this user
came from. The more important question now is if the would have
converted without that spend. 
That is what incrementality testing answers, not by instinct or guesses
but with a structured and statistically sound experiment. It helps
clarify signals from noise, strategy from misdirection, and outcomes
from dashboards that just give convenient outcomes. 
The cherry on top is that you don't need to throw away your stack,
create an acquisition lab of PhDs and only run tests every once in a
while. What you really need is a repeatable, operational model, with a
culture that not only doesn't chase attribution, but actively challenges
it.
? The test hypothesis and structur?
? The creative/audience/channel teste?
? Results and lift observe?
? Next steps or recommended action


Make a Living Playbook of Learnings
Are You Measuring It Yet?
Lift Is the New Truth.

23. Whether you are scaling a new acquisition channel, leaning into CTV
or simply trying to justify your Q1 budgets, incrementality gives you the
data story that actually stacks up. It is not just proving value, but
learning how to create value, and doing more of that.
At Apptrove, we think incrementality testing should be as easy as
attribution once was. That’s why we are helping mobile marketers with
the right tools and more confidence, to embed incrementality across
their growth workflows.
Because in FY25, if you are marketing without incrementality, you are
simply spending. And with incrementality testing, It's performance,
verified. Get started today with Apptrove!