Lifetime Value, a Tool for App Developers to Conquer the Market

User Lifetime Value (LTV) is a crucial metric to measure the effectiveness of an app’s revenue. Measuring LTV accurately requires a lot of human and material resources… and thanks to the development of AI, this process becomes easier.

User lifetime value is a crucial metric to measure app revenue performance.

Mr. Anton Ogay, Product Owner of App Campaigns at Yandex Ads - one of the leading global advertising networks, talks about the potential of Lifetime Value (LTV):

Mr. Anton Ogay

PV: What role does Lifetime Value (LTV) play in helping app developers compete globally?

Mr. Anton Ogay: LTV data allows developers to optimize revenue streams such as in-app purchases and in-app advertisements by identifying the value that users can bring and the cost of acquiring users. Thus, LTV helps determine the value that users create for the app, allowing developers to focus on the user base, creating the highest value to optimize app sales by proposing effective marketing activities targeting the desired user base. LTV goes beyond surface metrics such as app downloads, time spent in the app, etc., providing insights into global user behavior and preferences and is the basis for developers to launch effective campaigns for long-term success.

How to measure LTV? What difficulties have mobile game publishers encountered when their apps fail to 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 a customer over time. As a result, developers face the challenge of managing large amounts of data that may be inaccurate or incomplete, hindering accurate insights into user behavior and revenue generation. For the best measurement, game developers will need a large amount of user data, but this can be a challenge for developers, especially small and medium-sized developers who cannot afford it. This adds to the pressure on app developers. Furthermore, with the advent of AI, LTV measurement becomes more accurate, helping developers gain a deeper understanding of user behavior so they can optimize their marketing strategies effectively.

So how to apply AI to measure LTV?

AI-powered models can analyze data from a variety of sources, such as app usage, user behavior, and market trends, to predict future LTV for individual users or groups. These models can identify future trends that may not be immediately apparent to humans, providing more accurate and comprehensive insights into user value. For example, on the AppMetrica app analytics platform, we have incorporated a predictive LTV model built on Yandex Ads’ machine learning technology using anonymized data from tens of thousands of apps across multiple categories. This allows app teams to make accurate monetization predictions even without data from the app itself. So within 24 hours of installing the app, the model analyzes multiple LTV-related metrics and assigns users to groups based on their ability to monetize the app, dividing them into the top 5% of users with the highest LTV, up to the top 20% or top 50% of users with the highest LTV.

Do you have any examples of successful AI applications in measuring and forecasting LTV?

As I mentioned earlier, small developers often have difficulty accessing the necessary data to calculate and predict LTV. To solve this problem, we automated the process and mined data from Yandex Direct, Yandex’s own platform for advertisers. Yandex Direct has a huge data system based on tens of thousands of apps and user files of hundreds of millions of people. These models allow advertisers to promote mobile apps to get more post-install conversions and higher revenue, especially in pay-per-install campaigns. Once the data is collected from Yandex Direct, AppMetrica’s algorithm starts calculating a score to predict the user’s LTV. We used this score to train our models and incorporate the probability of post-install goal actions into the prediction. Based on this score, the system automatically adjusts the advertising strategy.

By accumulating data, the model learns and adapts to the behavior of an object in a given 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

Source: https://www.sggp.org.vn/gia-tri-vong-doi-cong-cu-de-cac-nha-phat-trien-ung-dung-chinh-phuc-thi-truong-post732405.html