Optimizing User Acquisition(UA) efforts is a key challenge for gaming companies. Given the fast-changing and complex landscape, dedicated UA teams focus on crunching data and gathering insights to take critical business decisions. This article explores how Kausa can help you maximize user acquisition and LTV.

User acquisition (UA) is the continuous process of acquiring new users to download your game or app. It can be done through various methods, either paid or organically, aiming at getting more installs and attract profitable users.

On one side you have the cost per install (CPI) for each user installed through your campaigns. On the other side, you have the predicted lifetime value of acquired users (LTV), which is a combination of retention (how long people stay in your app) and monetization (how much they spend in your app, plus advertising). A positive ROI is achieved by having a lifetime value superior to the cost per install. Depending on the segment of mobile gaming, the lifespan of a user ranges from a few days to several months or even years.

UA challenges

Fast-changing landscape

One of the most critical challenges of user acquisition is not only to make a successful game but keep it running with a high number of DAU (daily active users) and revenue over an extended time period.

We often hear from companies hitting a plateau or decline with user acquisition: "It worked great in the beginning. We grew very quickly and our user acquisition was really profitable. Now we've hit a wall and profitability is going down". Once a game is launched, the signals are usually very obvious in the beginning. However, as it grows, it becomes harder and harder to understand what may move the needle and find the right insights.

One channel that worked very well in the past (i.e., very profitable) may not be the right one anymore. Thus UA requires regular assessment and analytics to optimize ROI.

Multi-dimensional complex analytics challenge

CPI changes based on different budget levels can be represented only if you look at it on a specific level of granularity, as overall CPI on a wide global UA scale depends on many factors, such as:

  • Country share: which countries do you spend the most and get the most installs
  • Platform share: Android vs iOS
  • Channels used: Facebook, Instagram, Google Ads, Apple Search Ads, Video Networks, etc
  • Optimization methods: optimizing towards the cheapest possible installs (app install optimization), chasing payers (app event optimization), or higher-value users (value optimization)
  • Scale: how much are you pushing with a daily scale of budget/installs on the country/platform/channel level
  • Seasonality: CPI varies over the year

UA teams analyze the available data to identify critical segments, testing every potentially relevant factor and combinations of factors. Variables such as country, campaign, device, channel, media source, and game type may play a critical role, just to mention a few. Therefore analysts spend a significant amount of time drilling down on data using Excel sheets and/or BI tools (e.g., Looker), which have significant shortcomings to analyze multi-dimensional data. You can read more into how Kausa is closing the gap between dashboards and data-driven decision making 🌍.

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Agile trial and error over time

UA optimization involves fast experimentation using regular interventions (e.g., launch in a specific country to assess the potential of a new game). These interventions can be segmented into acquisition efforts (e.g., marketing artifacts) and in-game interventions. It requires agile trial and error to smoke-test game versions, various visuals and ultimately increase revenue. Given the sheer frequency of interventions, it's hard to isolate effects and assess the impact of a specific intervention.

Moreover, the time lag between one intervention and the corresponding effect makes it harder to quantify its impact. Especially as there aren't appropriate tools for unsynchronized time-series analysis with noisy data.

Competitive interaction and other key factors

While continuous data analysis to derive actions is a key part of the UA process, there are other relevant factors and workstreams. Firstly, it's key to track competition and industry trends, as well as gather insights from key partners like Facebook, Google, peers, etc. This way you can take these factors into account when optimizing your budget allocation. Secondly, we know that you also spend a significant amount of time briefing and partnering with creative teams to design the next iteration of ads. Last but not least, activities like brand building are key for long-term growth.

How Kausa can help you maximize player acquisition and LTV

Rapidly diagnose the most effective player acquisition, engagement, and revenue strategies with Kausa. The challenge to drive player acquisition and monetization is to identify what works and what doesn't early enough to act upon and drive profitability growth. Kausa provides automatic insights at every stage of the funnel.

Kausa is a data exploration tool to understand KPI changes in a fast and comprehensive way. Its sweet spot is around fast-changing KPIs in multi-dimensional data challenges, like user acquisition. Kausa's AI-driven technology hypothesis tests all potential factors and combinations of factors in the data to identify the causes behind every change in a matter of seconds.

Kausa can play a key role in your UA analytics efforts by accelerating data exploration, delivering automatic insights, and enabling comprehensive root-cause analysis.

Automatic insights

Kausa delivers automated insights, empowering UA teams to make day-to-day decisions by having these insights at their fingertips. This shift from reactive to proactive analytics unlocks significant business value as actions can be taken before it's too late. This is especially critical given that UA is a very fast-changing landscape. In other words, what works this week may not work so well next week, so you need to constantly optimize campaigns.

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Comprehensive root-cause analysis

Kausa hypothesis tests all factors and combinations of factors in your data so that you don't miss out on any insights. It also conducts automatic feature engineering to make sure sensible features are tested. Thus it delivers granular insights (e.g. combination of country, channel, and platform) to really find out what's impacting your KPIs, like CPI. You can read more about the technology behind Kausa in this article 🌍 by our CTO Michael Klaput.

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Accelerate data exploration

Kausa delivers insights in seconds after a few mouse clicks, freeing up analysts to focus on applying their domain expertise and deriving actions, instead of spending hours per day on manual slicing and dicing on BI tools and running tests on spreadsheets. This way you can focus on deriving recommendations and actions, ultimately optimizing campaigns based on the insights delivered to you by Kausa.

Conclusion

In a nutshell, UA represents a significant analytics challenge given the fast-changing landscape, multi-dimensional complex data, and relevance of competitive interaction and other factors. That's why most gaming companies have dedicated UA analytics teams, whose critical mission is to continuously monitor and optimize UA budget ROI.

Kausa can accelerate data exploration, delivering insights in seconds and at the right granularity to derive action (e.g., a combination of country, channel, and platform). This way you know where to focus your domain expertise, instead of spending hours on data crunching and manual slicing and dicing. Given that you get automated insights in seconds, you'll never feel like you're acting too late. In addition, by hypothesis-testing all potential factors and combinations of factors in your data, you'll feel confident to derive actions and recommendations without being afraid of having missed out on a key insight.

About the authors and Kausa

Stefan DΓΆrfeltΒ is Co-Founder and CEO andΒ JoΓ£o SousaΒ is Customer Success Manager at Kausa.

Kausa accelerates data exploration, delivering actionable insights in seconds by testing all hypotheses comprehensively and continuously.