User Lifetime Value (LTV) is an important indicator to measure the revenue effectiveness of an application. To accurately measure LTV requires a lot of human and material resources... and thanks to the development of AI, this process has become easier.
Mr. Anton Ogay, Product Owner of the App Campaigns department at Yandex Ads - one of the leading global advertising networks, talked about the potential of Lifetime Value (LTV):
Reporter: What role does lifetime value (LTV) play to help application developers compete globally?
Mr. Anton Ogay: LTV data allows developers to optimize revenue streams such as in-app purchases and in-app advertising by determining the value users can bring and the cost of collecting them. attract users. Thus, LTV helps determine the value users create for the application, allowing developers to focus on user files, creating the highest value to optimize application sales by setting out follow-up activities. Effective marketing targeting desired user files. LTV goes beyond surface metrics such as app downloads, app usage time... providing detailed information about global user behavior and preferences and is the basis for developers to make informed decisions. Effective campaigns bring lasting success.
How to measure LTV index? According to your observations, what difficulties have mobile game publishers encountered when their applications did not measure LTV?
LTV involves looking at a variety of factors such as average sales, purchase frequency, profit margins, and customer loyalty to determine the total revenue generated by customers over time. Therefore, developers face challenges in managing huge amounts of data that can be inaccurate or incomplete, hindering accurate insights into user behavior and creating revenue. To get the best measurement results, game developers will need a large amount of user data but this can be challenging for developers, especially small and medium-sized developers because they cannot afford it. pay. This increases the pressure on application developers. Furthermore, with the emergence of AI, LTV measurement support becomes more accurate, helping developers understand user behavior more deeply so they can optimize their marketing strategies effectively. .
So how to apply AI to measure LTV?
AI-powered models can analyze data from various sources, such as app usage frequency, user behavior, and market trends to predict future LTV for each person use or group. These models can identify future trends that may not be immediately apparent to humans, providing more accurate and comprehensive insights into user values. For example, on the AppMetrica app analytics platform, we incorporated a predictive LTV model built on Yandex Ads machine learning using anonymized data from tens of thousands of apps across multiple categories. different item. This allows app teams to make accurate predictions about monetization even without data from the app itself. So within 24 hours of installing the app, the model will analyze many parameters related to LTV and allocate users into groups based on the ability to generate income for the app, dividing them into 5%. users with the highest LTV, up to the top 20% or top 50% of users with the highest LTV.
Do you have any evidence of successful AI applications in measuring and forecasting LTV?
As I mentioned earlier, it is often difficult for small developers to access enough data sources needed to calculate and predict LTV. To solve this problem, we automated the process and mined data from the Yandex Direct platform, Yandex's own platform for advertisers. Yandex Direct has a very large data system source based on tens of thousands of applications and user files of up to hundreds of millions of people. These models enable mobile app advertisers to drive more post-install conversions and higher revenue, especially in pay-per-install campaigns. Once data is collected from Yandex Direct, AppMetrica's algorithm will begin calculating a score predicting the user's LTV. We used this score to train our models and incorporate the probabilities of post-set target actions into the predictions. Based on this score, the system will automatically adjust the advertising strategy.
By accumulating data, the model learns and adapts to object behavior in a specific application, increasing prediction accuracy to 99%. The reliability of these predictions comes from the vast and diverse amount of anonymized data we analyze, allowing us to identify patterns and trends that may not be immediately apparent to humans. This data is used to build predictive models that provide accurate and comprehensive insights into user value.
BINH LAM
