The era of waiting months to evaluate Return on Sales (ROAS) and optimize mobile marketing campaigns even weeks after launch is over. AppMetrica’s new LTV (Customer Lifetime Value) and customer churn analysis feature, which is part of the Yandex Ads service portfolio along with the Yandex Advertising Network, increases user acquisition in the mobile application.
At the same time, it quickly informs product managers about customer lifetime value and the possibility of customer churn from the first day of the campaign and application installations.
LTV, a very important metric in the mobile app marketing industry, represents the revenue a user is expected to bring in throughout their entire relationship with the app. AppMetrica’s LTV analysis takes this concept to the next level by finding potential users with the highest LTV ratio through the use of artificial intelligence.
LTV analysis is built on Yandex Machine Learning technology, using anonymized data from tens of thousands of applications in various categories. This feature allows managers responsible for user acquisition to optimize their strategies by focusing on those that will deliver the highest return.
LTV analytics evaluates each user within 24 hours of joining the app and generates an LTV analysis for the following 28 days. Based on this analysis, managers can optimize campaigns for top LTV users in just a few clicks.
High-performance advertising campaigns
With LTV and customer churn analysis;
- On the first day of the advertising campaign, it can be seen which advertising channels will be increased or closed,
- With an accurate LTV analysis, high-value audiences can be reached with the mobile application.
- High-performing advertising campaigns can be optimized to increase revenue and return on investment.
- ROAS can be maximized and it can be understood which channels need to invest more,
- Users can analyze and compare based on LTV and churn probability,
- By identifying the user segment with a high probability of loss, the loss of these users is prevented.
Unlike classic optimization recommendations such as traditional metrics “time spent” and “engagement,” the new AI-based analytics model collects and analyzes vast amounts of data about each user’s potential LTV to find the highest quality leads for ad campaigns. Additionally, LTV analytics allows you to segment users into various LTV groups (such as top 5%, top 20%, top 50%, and bottom 50%) and compare them.
How does it work?
Customer churn is a common problem in the mobile app marketing industry. For long-term success, it is crucial to identify users who are likely to abandon the app and take proactive measures to retain them. AppMetrica’s Churn Insights allows app owners and marketing teams to identify new users who are most likely to churn over time once they install the app.
The artificial intelligence model analyzes all active users over a 3-week period and scores activities daily. Although the model does not require custom metrics, it accurately predicts users who are more likely to become inactive within 3 weeks of installation. The generated report divides all users into groups according to conversion probability: >95%, 75%-95%, 50%-75% and <50%. The analysis shows that when a user is assigned a “risk of churn” tag, that tag has a greater than 99% chance of being true.
Using analytics, this feature allows for targeted retention strategies to prevent users from leaving the app. For example, it can offer tools such as personalized push notifications and incentives.