Enhanced Incrementally Testing: Beyond A/B Experiments for Deeper ROI Insights

Enhanced Incrementally Testing: Digital marketers have long relied on A/B tests and attribution models to gauge campaign success.

Yet, with the decay of third-party cookies and multi-device consumer journeys, traditional measurement is faltering.

Simple A/B tests can tell you which ad copy wins, but they often miss the bigger picture – such as whether your ads truly drove incremental sales or if those customers would have converted anyway. This is where enhanced incrementality testing comes in.

By isolating the causal impact of marketing efforts beyond what would happen naturally, incrementality testing provides a clearer lens on performance and return on investment (ROI). In fact, Google’s analysts note that incrementality experiments are becoming more accessible and popular for advertisers, thanks to new open-source tools and built-in platform experiments.

What You’ll Learn from This Article

• ✅ Understand the limitations of A/B testing and attribution models in today’s privacy-first, multi-device world

• ✅ Discover how enhanced incrementally testing works and why it’s more reliable for measuring true campaign impact

• ✅ Learn how to isolate the causal effect of your marketing spend so you can separate real results from background noise

• ✅ Explore practical testing methodologies, including geo-experiments and synthetic control groups

• ✅ See how AI and machine learning improve test accuracy and speed, delivering faster insights for better decisions

• ✅ Gain actionable steps to set up your own incrementality tests, even on modest budgets

• ✅ Improve your marketing ROI and budget allocation using results rooted in causality, not assumptions

• ✅ Equip your brand for the post-cookie future with privacy-safe, data-backed campaign measurement

In this article, we’ll explain what enhanced incrementality testing is, how it works beyond traditional A/B testing, and why it’s crucial now in our privacy-first, AI-driven marketing landscape. We’ll also explore practical methodologies – from geo-experiments to synthetic control groups – and offer actionable tips to help UK brands and marketing managers implement incrementality testing for smarter budget allocation and campaign optimisation.

What Is Enhanced Incrementally Testing?

Incrementality testing measures the true lift or additional impact of a marketing campaign by comparing outcomes of a treated group (exposed to ads) against a control group (with no exposure). In plain terms, it answers the critical question: “Would I have gotten those conversions if I hadn’t run the ads?”. Unlike multi-touch attribution that gives credit to every touchpoint (even ones that might have converted without ads), incrementality testing isolates only the net new value driven by your marketing. As one guide puts it, digital attribution often “claims credit for sales which would have happened anyway,” whereas incrementality testing focuses purely on sales caused by the advertising exposure.

Enhanced incrementality testing takes this concept further by leveraging advanced techniques and technologies to gain deeper insights than a basic A/B test could. Traditional A/B experiments (e.g. splitting an audience 50/50 to test an ad) are useful for tactical optimisations like choosing a better creative. However, enhanced incrementallity tests dive into strategic questions – for example, measuring the total ROI of a channel or finding the true effect of pausing spend in a region – and they do so with modern approaches like AI-driven analysis and privacy-friendly designs. The outcomes from incrementality testing can directly inform “where should I put my next pound of marketing budget to get the best return?”, a level of insight that simple attribution or A/B tests alone cannot provide.

Crucially, incrementality testing isn’t just about one-off experiments; it’s about building a culture of continuous, data-driven optimisation. Enhanced methods allow marketers to test more frequently and rigorously. For instance, some platforms now enable always-on incrementality measurement with minimal disruption, so you’re constantly learning which campaigns truly drive lift and which are wasting spend. In summary, enhanced incrementality testing combines the scientific rigour of controlled experiments with cutting-edge tools (AI and machine learning) to go beyond “did version A beat version B?” and instead answer “how much real value did my marketing generate?”

Why Incrementality Testing Matters Now (Beyond Traditional A/B)

In today’s environment, marketing teams face measurement challenges that older methods struggle to solve. One major factor is the shift to a privacy-first world: user-level tracking signals like third-party cookies are rapidly diminishing. Browser changes, GDPR and similar regulations, and mobile privacy features mean we have less granular data on individuals. Traditional attribution – especially last-click or multi-touch models – has become increasingly blind as it loses data, making it harder to trust what those models report. As these signals fade, first-party data (your own customer data) becomes more important, but even that has limits in reach and perspective. This is exactly why incrementality testing has surged in importance: it doesn’t rely on invasive tracking or cookies at all, but rather on controlled comparisons that are inherently privacy-friendly.

Moreover, standard A/B tests and attribution reports often fail to capture long-term or cross-channel effects. They might tell you which ad a user last clicked, but not how an entire campaign impacted sales over months, or how turning off a channel affects brand growth. Incrementality testing can illuminate both short-term and long-term media effects by observing what happens during and after experimental interventions. For example, a geo-experiment could show that it takes a couple of weeks for a TV campaign to produce a significant lift in sales (a delayed adstock effect), and that even after the campaign stops, its influence lingers for some time (a carryover effect). Insights like these are crucial for media planning – they help you understand how long to run campaigns and when diminishing returns kick in, so you can allocate budget more intelligently over time.

Another reason enhanced incrementallity testing is vital now is to separate real performance from noise or even fraud. In an era where platform-reported metrics can be inflated or misattributed, running your own clean experiments is the gold standard to verify performance. For instance, incrementality tests can expose whether a portion of conversions claimed by a channel would have happened organically (zero incremental lift) – in which case, you know to reallocate spend elsewhere. This is especially important for ROI-hungry brands and busy marketing managers who need confidence that every pound spent is truly driving growth. As Criteo notes, by using incrementality testing alongside tools like media mix modelling, marketers can make privacy-conscious, confident decisions and ensure their campaigns stay optimised even as traditional tracking fades.

In short, enhanced incrementality testing has moved from a “nice-to-have” experiment to a must-have measurement strategy in 2025. It directly addresses today’s challenges by providing a clearer picture of causality – cutting through attribution guesswork, adapting to privacy changes, and focusing on what genuinely boosts your bottom line.

How Enhanced Incrementally Testing Works

At its core, an incrementality test requires splitting your audience into a test group and a control group, then measuring the difference in outcomes. The test (or “treatment”) group sees the ads or campaign you’re evaluating, while the control group is deliberately kept free of those ads. By comparing results – such as conversions or revenue – between these groups, you can measure the lift caused solely by your marketing. A well-designed incrementality test shows not just the ROI of your ads, but also what would have happened if you hadn’t run the campaign at all (the control scenario). Modern platforms have made setting up such tests easier, with lower spend thresholds and built-in experiment tools so even smaller advertisers can test “what’s truly driving value” across campaigns.

Illustration: In a basic incrementality test, a portion of users (Group A) is exposed to your ads (test group) while a similar set (Group B) is not (control group). By measuring conversions or sales in each group, you isolate the incremental lift – the extra results driven by the ads. Enhanced methods build on this simple design with more sophisticated splits and analyses to ensure accuracy.

Geo-Experiments and Privacy-Friendly Design

One popular approach is using geo-experiments – testing incrementality by geographic region instead of by individual user. For example, you might turn off a campaign in Region A (test region) while keeping it running in Region B (control region), then compare sales lift between the regions. This method is inherently privacy-friendly because it doesn’t require individual user tracking; regions are used as large groups, avoiding personal data issues. Tech giants have embraced geo-based testing: Facebook (Meta) released a GeoLift tool for regional experiments, and Google Ads has offered geographic split tests for measuring campaign incrementality. Geo-experiments are especially handy for channels that affect offline sales or where user-level IDs are hard to use.

However, geo-testing introduces a challenge: no two regions are exactly alike. London is different from Manchester; even two towns can respond differently to ads due to demographics or competition. This is where the concept of synthetic control groups comes in. Instead of naively comparing one region against another, a synthetic control is an artificial composite of multiple control regions weighted to resemble the test region’s behaviour. For instance, if you stop ads in Region A (test), you could use B, C, and D as controls, assigning say 50% weight to B, 30% to C, 20% to D, such that their blended trends mimic Region A’s normal pattern. Any divergence in Region A (without ads) from this synthetic baseline indicates the impact of shutting off the ads. This technique greatly improves accuracy by accounting for inherent regional differences. In practice, analysts often use advanced statistical models (e.g. Bayesian structural time series) to generate synthetic controls and quantify uncertainty in the results. The end result is a far more robust measurement of incrementality at the macro level – you can be confident that observed lifts or drops are due to your campaign, not because your test city just had a big sports event or some unrelated factor.

Geo-experiments powered by synthetic controls have been game-changers for budget planning. They allow brands to test scenarios like “What if we double our TV spend in the North vs South?” or “Does pausing Paid Search in one country hurt sales compared to similar markets where it’s on?” – all without violating user privacy. Do keep in mind that executing these tests well requires sufficient geographic markets and careful experimental design. But when done right, geo-incrementality tests yield insights that directly inform media mix decisions (e.g. which regions or channels truly deserve more investment based on causal results).

User-Level Holdouts and Ghost Ads

Beyond geography, incrementality testing can also be done at the individual user level – which is more akin to a classic A/B test, but with some twists. The simplest form is often called an “intent-to-treat” holdout: you randomly split your target audience into two groups, and withhold your ads from the control group entirely. The test group sees your normal ads, and later you compare conversion or revenue outcomes. This basic holdout method is straightforward and doesn’t depend on cookies (since the random split can be done by user ID or even probabilistically by ad servers). However, it can be “noisy” – some users in the test group might never actually see the ad due to low exposure probability, diluting the measured effect. Likewise, any bias in group selection can skew results. Enhanced testing methods aim to tighten this up.

One improvement is using public service announcements (PSAs) or placebo ads for the control group. In this setup, both groups technically get ads, but the control group’s ads are unrelated (e.g. charity messages or blank ads) that should have no impact on the desired conversion. This ensures both groups have a similar experience (reducing bias) while still only the test ads drive the incremental difference. The downside, of course, is the control group’s ads still cost money to serve (or at least impressions), and not every advertiser wants to run dummy ads – but it does yield cleaner data than a simple holdout where control sees nothing.

The most advanced user-level methods are known as “ghost ads” and “ghost bidding.” These techniques were pioneered to get accurate lift measurements without wasting spend on control group ads. In a ghost ads framework, whenever a user in the control group would have been shown your ad (had they been in test), the system instead logs a “ghost impression” – essentially a record that says if we weren’t holding this user out, they would’ve seen an ad now. No real ad is shown to the control user, but we now know they had an opportunity. Later, conversions of such users can be compared against similar users who actually saw ads in the test group. Ghost ads require a closed environment where the ad platform can determine who would have seen an ad (for example, Facebook can do this since it controls the whole auction and delivery logic). In open-programmatic environments, the equivalent is ghost bidding – the platform participates in ad auctions normally for the test group, but for control users it simulates the bids without delivering ads, tracking whether those bids would have won. If a bid “would have” won for a control user, that user is marked with a ghost impression. This yields a near-perfect apples-to-apples control: users who got ads versus users who would have gotten ads if not held out.

Ghost bidding is considered state-of-the-art for incrementality on the open web, as it significantly reduces noise and bias in lift measurement. By focusing analysis only on users who had a high probability of being exposed in the first place (and using those as the basis of control vs test), you eliminate the dilution from unreachable users. The result is a very precise lift calculation with less data required to reach significance. The trade-off is complexity – implementing ghost bids requires advanced integration with ad delivery systems and often depends on cookies or identifiers to track users across bidding decisions. That said, companies like Criteo have built this into their platforms, using their AI Engine to randomly split users and identify those most likely to be reached for inclusion in the test. The takeaway for marketers is that such enhanced methods can give incredibly reliable answers to incrementality questions (far beyond what a standard A/B test could achieve), but they may require partnering with tech providers or using specialised tools.

AI and Machine Learning Supercharging Testing

Artificial intelligence is playing a growing role in incrementality testing – hence the term “enhanced.” AI and machine learning algorithms can improve nearly every aspect of the experiment process:

• Smarter Experiment Design: Machine learning can analyse historical data to pick the optimal test timing and audience split. For example, AI might identify when to run a test (avoiding seasonal spikes or anomalies) and ensure the test vs control split is balanced across dozens of factors, reducing bias better than random sampling. AI can also help determine how long a test should run to reach statistical significance, so you’re not testing too short or unnecessarily long.

• Synthetic Control Optimization: Creating accurate synthetic controls is essentially a data science problem, and AI excels at it. Advanced algorithms (including Bayesian models) crunch through historical performance, seasonality, and even external factors (economic trends, weather, competitor activity) to build a synthetic control group that closely mirrors the treatment group. This approach means you don’t always have to hold out a large static control group for long periods; AI can help simulate the control scenario with high fidelity, which is invaluable for always-on measurement. By accounting for myriad variables, AI-driven synthetic controls help isolate the true incremental impact without needing to “turn off” campaigns entirely in some cases.

• Rapid Data Processing & Attribution Fusion: Modern campaigns generate vast datasets – multiple channels, touchpoints, and conversion events. AI can rapidly integrate and analyse cross-channel data to attribute incremental effects properly. For instance, an AI model could consume your Google Ads, Facebook Ads, and SEO data and determine how each contributes to incremental sales when one channel is tested. This holistic view is something manual analysis might miss, especially in complex customer journeys. The benefit is a more complete understanding of where incremental ROI is coming from (paid search vs social vs display, etc.), which helps in multi-channel budget allocation.

• Real-Time Lift Optimization: Perhaps one of the most exciting developments is using AI for real-time optimisation based on incrementality. Instead of treating lift tests as one-off analyses, AI-powered systems can continuously adjust campaigns on the fly. For example, if early test data indicates a certain campaign is driving strong incremental conversions, an AI tool could automatically shift more budget into that campaign while the test is running, or conversely dial back spend on underperforming tactics. AI can also simulate different budget scenarios using the lift model – e.g. “what if we increased spend by 20% on Channel X?” – to predict incremental outcomes before you commit real budget. This kind of scenario planning and dynamic budget allocation ensures you capitalise on the findings of incrementality tests immediately, not months later. It’s a big leap from the days of static A/B results sitting in a slide deck.

All told, AI and machine learning make incrementality testing more efficient, accurate, and actionable. They help handle the complexity (multiple channels, external factors) in ways that human analysts would find time-consuming, and they enable an always-on learning approach. For savvy brands, this means incrementality testing isn’t a heavy research project reserved for big campaigns – it can become a routine part of campaign management, with AI doing the heavy lifting in the background. Busy marketing managers get the insights faster and can trust that optimisations (like budget reallocations) are backed by robust causal data, not just correlation or gut feel.

Practical Steps to Implement Incrementality Testing

Ready to move beyond basic A/B tests and start measuring true incrementality? Here are some practical steps and tips for implementing enhanced incrementality testing in your marketing plans:

1. Identify Key Questions and Metrics: Begin with a clear hypothesis. What do you want to learn? For example, “Does my paid search campaign drive incremental online sales, or would those orders happen via organic search anyway?” Define the primary KPI (sales lift, conversion lift, revenue per user, etc.) and make sure you have a way to measure it accurately. This will focus your test design.

2. Choose the Right Methodology: Decide which testing approach fits your scenario and budget. If you have the ability to segment by geography and enough regions, a geo-experiment might be ideal (no personal data needed, and good for measuring omni-channel impact). If geographic splits aren’t feasible, go for a user-level holdout. For large digital campaigns, consider advanced methods: many ad platforms offer lift test tools – for instance, Facebook’s Lift Test or Google’s Conversion Lift experiments – that can automate the holdout creation and analysis. These are essentially incrementality tests built into the platform. For the most sophisticated advertisers, explore partnerships or tools that use ghost ads/bidding or always-on lift measurement (partners like Measured, Nielsen, or Rockerbox specialise in this). UK-based brands should also ensure any user-level testing complies with GDPR – typically, this is fine as long as you’re not storing personal data beyond what your ads normally would, but get legal advice if unsure.

3. Ensure Randomisation and Similarity: In any experiment, make sure your test and control groups are as similar as possible except for the exposure to ads. Use random assignment where you can. If doing geo-tests, carefully select control regions that closely match your test region’s profile (consider factors like population, past sales, seasonality). The more similar the groups, the more confident you can be that differences in outcomes are due to the campaign and not some pre-existing disparity. You might use statistical techniques or even machine learning clustering to pick comparable regions or audiences.

4. Run the Test Sufficiently Long (but Not Forever): Determine how long your test should run to get a statistically significant read. This depends on traffic and conversion volumes – higher volume tests can run shorter. A common mistake is ending a test too early (before the lift signal emerges from the noise) or running it too long (wasting time and money). As a rule of thumb, run the test through any typical purchase cycle and account for any delayed effects – e.g. if your product has a 2-week conversion lag, ensure the test spans beyond that. Most platform lift tests will estimate the required sample size or duration for you. Keep an eye on external factors during the test window (holidays, sales, or market changes) and be prepared to adjust if something big could skew results.

5. Analyze Incremental Lift and ROI: Once the test concludes, calculate the lift: e.g. +10% conversion rate in test vs control, or an extra £5 per user in revenue attributed to the campaign. Then translate that into ROI: was the lift worth the spend? For instance, if you spent an extra £10,000 on ads in the test regions and saw £15,000 incremental revenue compared to control, that’s a positive ROI and justifies scaling up. Make sure to account for statistical confidence – was the lift significant or possibly due to chance? Many tools will give you a confidence interval or a probability (e.g. “95% confidence the lift is between 5-15%”). If results are inconclusive, you may need to rerun with a larger sample or check for issues in setup.

6. Take Action – Calibrate and Optimise: This step is often overlooked – don’t just file away the results, act on them! Incrementality testing shines when its insights feed back into strategy. If you found that a channel is highly incremental, you might increase its budget or invest in similar campaigns, knowing they truly grow sales. If a test shows low or zero incrementality for a tactic, consider reallocating spend to higher-impact channels – or try to optimise that channel (better targeting, creative, etc.) and test again. Also, use the findings to calibrate your broader marketing mix models. Many brands now combine MMM (Marketing Mix Modelling) with incrementality experiments: the experiments provide ground-truth data points that help validate or adjust the MMM’s assumptions. This hybrid approach gives you the best of both worlds – strategic big-picture allocation from MMM with tactical, granular proof from lift tests.

7. Iterate and Expand: Enhanced incrementallity testing is not a one-and-done project. Build it into your ongoing workflow. Perhaps start with one channel or campaign as a pilot test. Then, as your team gains confidence, expand to other channels and even run multiple tests throughout the year. Some leading advertisers operate an “always-on” incrementality program, continuously rotating tests (e.g., one quarter focusing on Paid Search, next quarter on Paid Social, etc.). Over time, you’ll amass a rich understanding of what truly drives your marketing success. This culture of testing and learning can set you apart from competitors still relying on vanity metrics.

8. Leverage Tools and Expertise: Don’t be afraid to use technology and experts to help. There are open-source libraries (like Meta’s GeoLift in R, or Google’s CausalImpact for time-series analysis) if you have a data science team at hand. There are also commercial tools that specialize in incrementality – for example, Adjust and Singular (for mobile apps) offer incrementality testing features, and Criteo’s platform can run continuous lift tests via ghost bidding for its clients. If you work with an agency or a consultant, ask them about setting up incrementality studies for your campaigns. The investment in proper testing yields insights that can save or reallocate significant budget – it’s the epitome of working smarter, not just harder, in marketing.

By following these steps, even busy marketing teams can start unlocking the value of enhanced incrementality testing. The key is to start simple, learn the process, and progressively layer in more advanced techniques (like synthetic controls or AI analytics) as needed. Remember, the goal is to get a truer measure of marketing impact so you can optimise spend with confidence.

Conclusion: From Testing to True Optimisation

Enhanced incrementallity testing represents a powerful evolution beyond basic A/B tests – giving marketers in the UK and beyond a deeper understanding of campaign performance and ROI. By embracing methods like geo-experiments, holdout audiences, and AI-driven analysis, brands can finally see which dollars (or pounds) are actually driving results, and which are just along for the ride. In a time of tightening privacy rules and budget scrutiny, this kind of clarity is gold dust. You’ll be equipped to make data-backed decisions on where to invest your next advertising pound for maximum impact, armed with evidence of causality rather than assumptions.

For digital marketing managers and brand owners, the takeaway is clear: incrementality testing is no longer experimental – it’s essential. Those who go beyond the old A/B playbook and integrate these enhanced techniques into their strategy will enjoy more efficient marketing spend and stronger growth, backed by proof. As you plan your next campaigns, consider how you can incorporate an incrementality mindset – ask “how will I prove this works?” upfront and design a test to get that answer. The insights you gain will not only justify your budget to stakeholders (with hard numbers), but also uncover opportunities to optimise that you might never spot through conventional analytics.

If you’re looking to elevate your marketing measurement and implement robust incrementality testing, it can help to have experienced partners by your side. PPC Geeks has a team of experts ready to assist in planning and executing advanced testing strategies tailored to your business. From designing geo-tests to analysing lift results with AI tools, we can help turn data into actionable growth plans. Get in touch with us to supercharge your campaign insights – contact our PPC Geeks team today, and take the guesswork out of your digital marketing. By adopting enhanced incrementality testing, you’ll be well on your way to smarter budget allocation, higher ROI, and a marketing program that’s truly optimised for success.

FAQs: Enhanced Incrementality Testing

1. What is enhanced incrementallity testing?

Enhanced incrementality testing is a method used to measure the true impact of marketing campaigns by isolating the conversions or outcomes directly caused by advertising efforts. It goes beyond simple A/B tests or attribution models by using controlled experiments and advanced analysis (often powered by AI) to determine what would have happened without the ads.

2. How is incrementality testing different from A/B testing?

A/B testing compares different versions of an ad or landing page to see which performs better, usually focusing on creative or UX. Incrementality testing, on the other hand, compares results between exposed and non-exposed audiences to understand whether the marketing itself caused an outcome – focusing on effectiveness and ROI rather than creative variation.

3. Why is incrementality testing important in 2025?

With third-party cookies disappearing, privacy regulations tightening, and customer journeys becoming more complex, traditional attribution models are less reliable. Incrementality testing offers a privacy-friendly, data-driven alternative to prove which campaigns genuinely drive value.

4. What types of businesses can benefit from enhanced incrementallity testing?

Any business that invests in digital marketing – from eCommerce and retail to SaaS and financial services – can benefit. It’s especially useful for brands with multi-channel campaigns, significant marketing spend, or long sales cycles who want to optimise their budget allocation and campaign strategies.

5. What are geo-experiments in incrementality testing?

Geo-experiments are a type of incrementality test that uses geographic regions as control and test groups. By running or pausing campaigns in selected locations, marketers can measure the lift generated in treated areas while using others as a baseline – all without tracking individual users.

6. What is a synthetic control group?

A synthetic control group is an artificial benchmark created by combining data from multiple control regions or audiences. It is used when natural control groups aren’t available or are not perfectly matched. This method improves accuracy by modelling what would have happened if no advertising had occurred.

7. Does incrementality testing require personal data?

Not necessarily. Many modern incrementality methods, like geo-based testing or ghost bidding, can operate without personal identifiers. This makes them more compliant with data protection laws like GDPR and aligns with a privacy-first marketing approach.

8. How long should an incrementality test run?

The duration depends on the volume of traffic and conversions. Most tests should run long enough to capture a full customer journey and any delayed effects. For many brands, this means at least two to four weeks, though high-volume campaigns may achieve results sooner.

9. What role does AI play in incrementality testing?

AI and machine learning enhance incrementality testing by automating experiment design, generating more accurate synthetic control groups, analysing large datasets, and delivering real-time insights. AI also helps adjust spend mid-test, model uncertainty, and simulate budget scenarios.

10. Can I use incrementality testing alongside attribution models?

Yes. Incrementality testing complements attribution by providing causal proof to validate or correct attribution assumptions. Many brands use both: attribution for day-to-day optimisation, and incrementality for deeper performance audits and strategic decision-making.

11. What tools or platforms support incrementality testing?

Platforms like Google Ads, Meta (Facebook), Criteo, Nielsen, Measured, Rockerbox, and LiftLab offer built-in or integrated incrementality testing features. Open-source tools like GeoLift and CausalImpact are also available for brands with in-house analytics teams.

12. How much budget do I need to run an incrementality test?

Some platforms support incrementality tests with as little as £4,000–£5,000 per campaign. However, larger or more complex tests (e.g., involving multiple channels or geographies) may require higher budgets. It’s important to balance test scale with expected decision-making value.

13. What if my test shows no lift?

No lift means your campaign likely didn’t drive additional conversions beyond what would’ve happened naturally. This is valuable insight – it tells you that budget may be better spent elsewhere, or that the creative, targeting, or channel needs to be re-evaluated.

14. Is incrementality testing suitable for always-on campaigns?

Yes. Many brands implement always-on incrementality testing through rotating experiments, holdout groups, or AI-powered analysis. This approach enables continuous learning and optimisation without stopping campaigns entirely.

15. Can PPC Geeks help with incrementality testing?

Absolutely. At PPC Geeks, we help brands design, run and analyse incrementality tests – whether you’re exploring geo-experiments, synthetic control models, or platform-specific lift studies. Our experts use best-in-class tools to ensure every campaign delivers measurable, meaningful results.