User Acquisition vs. Organic Growth

Imagine you’re driving traffic with your best-paid channels, yet your organic growth stagnates. It sounds counterintuitive but could be the key to unlocking more sustainable revenue.

As the UA landscape becomes more competitive and user ID tracking becomes more challenging due to privacy changes, marketing teams are under pressure to be more data-savvy than ever. The need for deeper analysis of UA has driven many to explore advanced models like Marketing Mix Modeling (MMM) and Incrementality in hopes of better understanding platform algorithms and optimizing performance.

MMM is a separate topic that requires its own discussion. In this post, I will focus on the basics of organic incrementality—how paid UA impacts organic growth and highlight key situations where reevaluating your testing strategy with a robust model could enhance your marketing efforts.

Incrementality in Marketing:

Incrementality measures the true impact of a marketing campaign or channel by assessing its additional value—something that attribution alone cannot fully capture. Users are typically exposed to multiple ads across different channels before deciding to convert—or, in our case, install a game.

This creates a dynamic relationship between touchpoints. However, most deterministic models don’t account for this dynamism in channel assessment. A traditional last-click attribution assigns 100% of the conversion value to the final touchpoint and disregards any prior interactions.

Incrementality helps bridge this gap by determining whether a channel contributes beyond just the last touch, offering a more holistic view of its true impact.

First look at UA & Organic Dynamics

There is always a dynamic between paid channels and organic efforts in marketing. The key is not just to acknowledge this relationship but to analyze it effectively and extract meaningful insights.

While many advanced incrementality tools are available for complex modeling, my goal here is to simplify the concept and show how marketers can start assessing the connection between paid and organic efforts.

I’ve titled this section “Incrementality,” but I will specifically focus on UA channels and their relationship with organic growth. Although incrementality also applies to interactions between multiple paid channels, that aspect will not be covered here.

The simple tests outlined below don’t account for external factors such as live ops, seasonal events, or community–driven efforts like influencer marketing—which ideally should be considered. Feel free to adapt these examples based on your unique product and market conditions.

Scenario A: Comparison

Without making any changes, you can begin by analyzing your historical user acquisition (UA) and organic trends. In the example chart below, you can see the conversion trends for different paid advertisers alongside organic conversions from July to September. Let’s assume this data is statistically significant.

Looking closely at the data, the first key insight is that organic conversions have increased significantly, even though the total number of paid conversions has remained mostly stable over 3 months. This suggests a dynamic between paid channels that is driving organic growth.

According to the chart, organic conversions doubled in September while showing a modest increase from August to July. Examining the paid channels, the decline in conversions from Paid Advertiser A raises the question of whether it was previously cannibalizing organic conversions in July. Meanwhile, with Paid Advertiser C remaining stable, Paid Advertiser B appears to have played a significant role in boosting organic growth. 

If Paid Advertiser B is already performing well based on last-click ROAS, it’s also generating additional value through organic acquisitions—which come at no extra cost. Based on the chart, Paid Advertiser B seems to have a positive incremental impact, while Paid Advertiser A appears to have a negative one. 

Ultimately, it’s up to the marketing team to evaluate last-click revenue + incremental revenue and decide whether continuing to invest in Advertiser A is justified. 

Scenario B: Black Out Test

In this scenario, you could consider pausing one of the paid channels that you suspect might be negatively impacting your incremental growth. A few weeks of pausing should provide enough data, depending on your current levels of organic and paid installs. 

In the example below, I paused paid channel A while keeping the other paid sources relatively stable. The goal is to observe if there’s any change in organic performance after pausing this channel. As you can see, there’s a slight uplift in organic conversions during Week 2 after pausing channel A. Interestingly, after relaunching the paid channel in Week 4, organic conversions dropped again.

While this chart alone doesn’t provide a definitive reason to stop using paid channel A, it does suggest that further analysis is needed to determine if this channel is cannibalizing organic traffic.  

Scenario C: Uplift Test

Since UA operations are never completely stable over long periods, I created Case B, focusing on a shorter timeframe and exaggerating the numbers slightly for clarity. 

In this scenario, we’re analyzing a single partner—Paid Advertiser B—while keeping all other marketing efforts constant. The goal is to determine whether changing spend levels for Advertiser B adds value to organic growth or causes organic to decline.

In Week 2, the marketing increased ad spending for Advertiser B, so it received more conversions than the previous week. There is a clear organic uplift. However, in the following week, Advertiser B was scaled again—but this time, organic conversions dropped. Here’s where it gets tricky: Advertiser B’s conversion trends don’t always move in sync with organic conversions every month. 

What does this mean? It highlights a key limitation: UA channels can’t drive unlimited organic uplift. There’s a point where incremental uplift reaches its peak, and overspending just because a channel previously showed positive incrementality doesn’t guarantee continued returns. 

That said, low incrementality isn’t necessarily a red flag that requires pausing a channel immediately. Instead, the key is to evaluate total return—last-click revenue + incremental revenue—against ad spend to make informed decisions. 

Looking at the examples above, if you suspect that a specific partner may be impacting your overall organic KPIs, it might be the right time to consider a more robust model for your marketing efforts. 

Discussion: 

The real value of studying organic incrementality becomes clear when you can scale marketing efforts and increase total revenue, all while maintaining the same last-click ROAS. As a UA manager, understanding each partner’s role within the broader conversion funnel and how they interact with other sources—especially organic—becomes essential. This insight allows you to maximize the potential of each channel. 

For instance, a partner might perform “just okay” based on last-click attribution but still deliver strong incremental value. On the other hand, a channel that seems effective on last click could actually be cannibalizing organic traffic. In such cases, you may decide not to scale further with the latter and instead focus on the first partner, whose incremental impact can drive more sustainable growth. 

However, it’s important to acknowledge that this is not an easy task. Accurately attributing incremental impact to organic traffic from multiple sources is a complex data topic. While I kept the paid channels to a minimum in the examples above (three), in reality, the number of channels can easily exceed 15.

Additionally, I didn’t consider critical factors such as live ops, feature launches, or community engagement—elements that greatly influence organic growth and overall product performance. These factors are essential when evaluating the contributions of specific events.

I also avoided diving into a publisher or campaign-level data. In reality, specific publishers, networks, or campaigns can drive substantial growth, but this won’t be true for every campaign. To optimize partner performance, a more granular approach is needed. 

Given these complexities, traditional marketing dashboards often fall short of providing the depth needed to explore incrementality. Once you’ve analyzed your sources and gained an overview of organic performance, you may begin to question whether a channel’s true value aligns with what last-click attribution suggests.

If so, it’s time to explore incrementality further. Using a robust incrementality framework—whether through a third-party tool or an in-house model—will help clarify the relationships between various factors, making it easier to interpret results and optimize your marketing strategy. 

Start exploring your own data and see if you can identify any patterns or contradictions similar to the examples described above. Feel free to reach out with any additional comments or questions!