Predict Lifetime (pLT): Predictive user lifecycle analysis is a key-metric for any app marketer in forecasting future marketing strategies, making decisions in budget allocation, and maximizing campaign profits while ensuring compliance with user information security.
Predicted lifetime value or potential value of a customer by combining historical information and current metrics. This helps advertisers build and optimize marketing campaigns based on the audience's predicted consumption trends.
1. What is Predict Lifetime?
- Predict Liftetime (pLT) is a predictive analytics method based on AI, Machine Learning to measure and estimate the average number of days that users will return to a brand's application.
- Airbridge relies on a large amount of data from historical user data, multi-layer analysis on users with similar behavioral characteristics, fully informed users, and through security data on a variety of SDKs, etc. to train a model to accurately predict the user life cycle.
- Forecasting user lifetime also helps businesses estimate customer lifetime value to discover insights, balance user acquisition campaigns, and optimize CPA (cost per action) of advertising campaigns.
2. How does Predict Lifetime help businesses?
- With Apple's new changes from IOS 14.5, the introduction of App Tracking Transparency and SKAdnetwork causes many limitations in collecting user-level data, delays in sending back user information and attribution, leading to many data blind spots in optimizing advertising campaigns on IOS devices as well as measuring customer lifetime value to balance budgets. Knowing the predictive indicators of user lifetime at an early stage helps businesses identify high/low potential customer groups for UA and retargeting campaigns, helping advertisers to exploit insights to improve the effectiveness of IOS campaigns.
- Allocate advertising budgets appropriately to attract user groups with high customer lifetime value, increase Return on Ads Spend (ROAS) and make quick and timely decisions while ensuring compliance with user information security.
- Make decisions based on a data-driven mindset: Many marketers often make their own guesses about LTV based on benchmark revenue/ROAS metrics from other apps that have been running. However, in an era of scarce data and the characteristics of each app, the portrait of the target customer is different, leading to inaccurate predictions and inappropriate decisions. Taking advantage of pLT or pLTV will help businesses make more accurate forecasts when the AI and machine learning prediction models are completely based on the data of their own apps.
3. Airbridge predictive lifetime
- Airbridge with predict lifetime will be a useful tool for app marketers to predict the average time that users will be active on the app after installation. Taking advantage of pLT also helps businesses calculate the lifetime value of users, predict the quality of users, advertising channels.
- With this feature, marketers will be able to analyze the effectiveness of each cohort they are running and optimize for each group.
For example: Application A runs on 3 main channels by observing the pLT of these 3 channels, along with checking the number of installations, we can see that Google Adwords is the most effective channel with a high number of installs and a very good retention rate.
From there, the feature Predicted Lifetime (pLT) Can calculate the average number of days that users return to be active on the app in a given period of time
- pLT on Airbridge can make predictions up to 1000 days in advance based on the application's user data. Through a series of experiments on different datasets as well as applications on many customers, pLT shows an accuracy of up to 91% and often 96% when performing predictive analysis for the next 6 months. Airbridge is not only an MMP but also a multi-touch attribution (MTA), marketing mix modeling (MMM) partner of Meta, giving Airbridge a large amount of data for the machine learning engine to be trained and make the most accurate predictions.
