How predictive analytics helps to understand and retain app users

Posted on June 23/06/18

Amidst various strategies and tactics to retain users, marketers with their advanced intellect touched the prediction analysis in the recent years. Prediction is compelling, predictive analysis to retain back users is more convincing. Let’s learn in-depth about prediction analytics marketing to retain users.

Why predictive analytics?

Predictive analysis is a subset of artificial intelligence, machine learning, and historical data, as it collects the elements of past data to predict the further happenings.

Being proactive about how to re-engage users or to avoid users churn is a step forward from being reactive after it happens. Prediction analysis will allow you to influence your user's further actions.

By knowing your user actions,you can analyze tactics to make them not to churn and win them back by sending personalized notification campaigns.

How to retain using predictive analysis?

Predictive analysis remained mystical realms of data scientists, and now it’s for any access and use data. Retention is always a big goal for many marketers, how will you use predictive analytics to retain users? It can be accomplished based on a hypothesis with the present data of the users.

With lots of data, it is hard to select one to start with. Predictive analysis with training(historical data) with mathematical algorithms predict the exact patterns to start with, the user patterns like users who are likely to churn and who got churned recently.

The deep insights about your user behavior, what the product was delivering to your user and the historical data, it relies on machine learning, which is an accurate learning to build data patterns.

With the following ways of predictive analytics, retain your users.

Understand your user preferences

Get the point why users stay as long-term and why users churn, understand their in-app behaviors and compare the cohorts of the different user. Predictive analytics will help you to know your user preferences by analyzing their previous data. So, over time it can help you to provide user needs and expectations, this will assure to make them happy users rather than disappointed users.

Example, let’s take an educational tutorial where they use data analytics and predictive model to increase their user engagement and developmentation. Here thepredictive model shows that some of your customers are more likely to churn and they didn't renew. With this information, you can provide incentives or offers for those users to make them renew. By doing this you ceased them from churning.

Segment your users

Segmentation with predictive analysis, you can get more details about your user's behaviors and preferences. The deep behavioral segmentation parts way for better campaigns to retain users.

Example, let’s assume a food delivery app say zomato which has the different set of users, the segment that wide range of users on basis of the food they order. With predictive analysis, you can predict the loyal users with high value, the users who are about to churn and some users who may or may not churn. By knowing this user data patterns, you can send personalized notifications for each set of users separately.

Target users who are likely to churn

Predictive analysis can help you to know the first-time user who is likely to churn, using segmentation you can segment the churned users or the users who are about to churn by using their previous engagement and data

If your users who are about to change are your first-time users then you can send incentives to boost them up like extending their trial period.

Example, in any gaming app, engagement and churning occur much faster than expected. With segmentation process segregate the users like who is using your app daily, who visited only once after app install, who completed certain levels in the app and didn’t revisit. Now, prediction analysis can help you to find which set of users are likely to churn with their previous data, it correlates. The resultant will help you to target users and bring them back.

Approach through campaigns

Segment your users with predictive analysis and send the personalized notification, fix your marketing campaign channels according to the segmented user patterns. Thus predictive analytic marketing will help you to create more effective campaigns for the different set of users.

Example, an online shopping app where you will have set of user patterns according to their actions, with predictive analysis target the users who are more likely to churn and churned users, then notify them individually. This will work more effectively than sending an end of season sale offer notification commonly to all users.

Send personalized notifications to each user sets, like for those who are about to churns provide them offers relating to their previous purchase. And for those users who got churned, send them the new arrivals, special offers and other features in the app to retain them back.

Understand your user activities and where they are more active, analyze and fix the marketing channel accordingly. Some users will be active in social media, some in social media and some through networks, also choose whether to email, push notify or in-app notify a user.

Experience Predictive Analytics

Predictive analysis is like an important tool for marketers in retaining users, with which you can analyze the causes for users to churn, accurate data on userswho are likely to churn and also to know about loyal users. With predictive analysis, you can create a personalized user experience and targeted notifications.Grow the app with predictive analysis and win back your users easily.

About the Author


Priyanka is based out in India, and she loves blogging, traveling and doing cool kinds of stuff in data understanding and visualization.

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