- Introduction
- How Incrementality Works in Marketing
- The Benefits of Incrementality Analysis on User Acquisition
- Incrementality in Independent App Ecosystem and Walled Gardens
In the realm of marketing, coming up with effective strategies to increment user acquisition is vital. This article aims at analysing incrementality as a way to efficiently scale user acquisition. In fact, incrementality delivers critical, trustworthy insights alongside attribution, enabling complete and seamless measurement in one unified platform. User acquisition teams today face complex challenges, including fragmented user journeys, increasing privacy restrictions, and gaps in visibility across various channels. Incrementality for user acquisition helps you overcome these obstacles by revealing the actual business impact of your campaigns.
Incrementality refers to measuring the true impact of a marketing activity on a specific key performance indicator (KPI). The goal is to determine whether a campaign actually generates additional results (incremental lift), has no measurable effect, or even produces a negative outcome. By analysing incrementality, marketers can understand whether a campaign genuinely influenced a desired result—or if the same outcome would likely have occurred even without running the campaign.Incrementality analysis can be performed using several methodologies.
At its core, it relies on causal data science models and control groups to estimate what would have happened if the marketing action had not taken place, allowing marketers to move beyond speculation when evaluating scenarios such as increasing budget on a specific network or pausing certain campaigns. Although the terms are sometimes confused, incrementality and attribution are different concepts. Attribution focuses on identifying which touchpoint should receive credit for a conversion, while incrementality evaluates whether the conversion would have happened anyway, regardless of the marketing exposure.
Traditionally, incrementality testing involves splitting the target audience into two groups: a control group and a test group. The test group is exposed to the campaign, while the control group is not. By comparing the results between the two groups, marketers can isolate the campaign’s true effect. In practice, this approach is similar to A/B testing, where the difference in outcomes reveals the incremental impact of the marketing activity. App marketers (ASO or mobile growth marketers) frequently use Google to find out more about strategies (especially regarding user acquisition), tools and benchmarks. The most frequent keywords are divided into some main categories. For instance, the first category is ASO keywords (such as “app store optimization tips”, “ASO tools”, “app store keyword tracking”) which improve ranking on Apple App Store e Google Play, and User Acquisition keywords, to find new growth strategies and channels.
Another highly popular category is analytics and tracking keywords. This category aims at performance and attribution analysis in order to compare performance, and amongst the most frequent keywords there are “mobile attribution tools”, “app analytics platforms”, “mobile measurement partner comparison”, “app retention benchmarks”, while among the most searched tools emerge AppsFlyer, Adjust and Amplitude. Another precious and effective category is monetization keywords, which are very precious and effective to boost the app’s reward. Some typical keywords within this category can be “mobile app monetization strategies”, “in-app purchase optimization”, “subscription app pricing strategy” and “mobile app LTV calculation”.
As the marketing industry moves toward more aggregated methods of measuring performance, marketers are finding it increasingly difficult to gain real-time insights and fully understand the true impact of their campaigns. At the same time, marketers are asked to deliver greater precision, agility, efficiency, and measurable results, as well as ensuring a certain level of user acquisition. In this evolving landscape, incrementality analysis has become a powerful tool, providing several benefits especially on user acquisition. By identifying which campaigns truly drive performance, with statistical confidence levels as high as 95%, incrementality enables more agile budget allocation, allowing marketers to quickly shift investment toward the channels that generate real impact.
This approach is becoming increasingly important for user acquisition, as user attention spreads across a wider variety of apps and categories. Many users, particularly younger audiences, are actively reducing the time they spend on traditional social platforms due to both emotional and advertising fatigue, instead engaging with a broader mix of apps such as games, productivity tools, finance, and emerging categories like generative AI. As a result, mobile audiences are now distributed across millions of specialized apps rather than concentrated on a small number of social platforms. In this environment, relying exclusively on large walled gardens such as Meta or Google can limit reach and obscure the true incremental value of campaigns. By leveraging the broader independent app ecosystem, marketers can access more diverse audiences and identify the channels that deliver genuine incremental user growth.
Since it relies on privacy-centric and aggregated data rather than individual user tracking, incrementality is also resilient to ongoing global privacy changes and industry regulations. At the same time, it reduces operational complexity by eliminating the need for dedicated data science teams to continuously analyse campaign performance, enabling marketing teams to access valuable insights directly through analytics platforms. Incrementality also empowers marketers to experiment new channels, ad formats, markets, or seasonal strategies while clearly validating their effectiveness. Finally, by revealing potential organic cannibalization and the true contribution of different media sources, incrementality improves transparency and accountability, enabling more informed and constructive conversations with advertising partners about campaign optimization and long-term performance.
A walled garden is a closed digital ecosystem in which a platform provider controls access to hardware, applications, content, and user data. Within these environments, all advertising activities take place entirely within the platform’s infrastructure, meaning that advertisers must rely on the platform’s proprietary tools and technologies to run, optimize, and evaluate their campaigns. Companies such as Google, Meta, and Amazon generate enormous advertising revenues through these controlled ecosystems. However, walled gardens present several limitations for advertisers.
For instance, advertisers typically cannot export raw user-level data from these platforms, limiting their ability to conduct independent analyses or integrate performance data across multiple channels, resulting in marketers obtaining only a partial view of user behaviour and campaign effectiveness. Operational complexity is another challenge, as working with aggregated and anonymized data can make analysis more difficult and may require specialized tools and expertise. Managing campaigns across multiple closed ecosystems can also be resource-intensive, particularly for smaller advertisers. Moreover, advertisers often become dependent on the platform’s algorithms, measurement frameworks, and reporting systems.
Finally, walled gardens may limit competition and innovation by creating high barriers to entry for smaller or emerging advertising technology companies. In this context, incrementality plays a crucial role in evaluating the true effectiveness of user acquisition strategies. Most advertisers begin their user acquisition efforts on major platforms such as Google and Meta due to their scale and sophisticated targeting capabilities. However, attribution within these ecosystems does not always reveal whether conversions represent genuinely new users or individuals who would have installed the app regardless of advertising exposure. Incrementality analysis addresses this limitation by measuring the causal lift generated by marketing activities, allowing marketers to identify the campaigns that actually drive new user growth.
This perspective becomes particularly important when considering the Independent App Ecosystem, which consists of a wide network of publishers and advertising platforms operating outside the dominant walled gardens. Companies operating in this space, such as Mapendo, can reach audiences that may not be fully accessible through large platforms alone. As a result, campaigns delivered through the independent ecosystem can generate additional users and conversions that would not have been achieved through walled gardens, thereby creating incremental value for the advertiser’s overall marketing strategy. Rather than replacing campaigns on large platforms, these channels complement them by expanding audience reach and contributing to a more diversified and effective user acquisition strategy.
