Retain your app users by focusing on their Lifetime Value
Posted 2 years ago
3 Minute(s) to read
The estimation of Customer Lifetime Value (LTV) is one of the cornerstones of marketing. LTV reveals the long-term relationship between a company and the value of a customer over time. As leading marketers have shifted their emphasis from Return on Investment (ROI) to the long-term growth of their company, estimates of customers’ lifetime value have become more important than ever before. Estimating LTV can give your company the ability to not only focus on the customers who matter most, but also to more accurately customise your messages to different customer groups.
Why apps should focus on LTV
We all know that in today's app market there is an overflow of new apps weekly. Every app available on the top app store is actually competing with more than 2 million others. The challenge for app developers is not only getting discovered and downloaded, but also retaining their users. Many apps fail to keep users interested for a long time and research is indicating that retention rates are only getting worse. Only between 29-44% of iOS users, and between 27-40% of Android users returned to an app after 24 hours. Furthering this, 80% of new app users stop using an app after just three months. Given these facts, estimating LTV is more crucial than ever so that focus can be placed on the customers that matter to ensure users stay interested and engaged for the long-term.
LTV is a powerful metric that can estimate the value of an app-user for a business. LTV looks at all the interactions of a customer and subsequently, can be used for identifying important customers and maintain engagement with them. A mobile app is more likely to satisfy customers, and its payoff is not only customer loyalty but dropped cost as well. While each call-agent or interaction cost $4, a mobile interaction cost is only 10 cents.
It should be noted that for estimating LTV, all touchpoints in the customers' journey need to be taken into account. For example, one of your users might use an app several times, and then make a purchase via your website. If you do not consider their interaction with both of these channels, you will undervalue the potential of this customer.
Use machine learning for LTV prediction
Machine learning is a sub-branch of artificial intelligence, that enables computers to self-learn without being explicitly programmed. Yes, it can sound a bit scary, but machine learning can actually be super useful as a tool for business decision making.
A machine can identify patterns in the customer information, and estimate LTV values faster and more accurately than humans. Machine learning not only can identify the most profitable users, but also can optimize the campaigns. This can be done through learning the attributes of high-value users, in order to identify and target more of these users. In other words, the model can self-improve as more data becomes available.
Lifetime value can be regarded as a straightforward classification problem, that labels users based on their loyalty level. Additionally, we can use machine learning techniques in combination with time series modelling.
One excellent example of usage of Machine learning for LTV, is the Instant messaging app Viber. Their marketing goal was simple - "Grow the number of loyal users". To complete their objective, they used the information about their current loyal users as the input and using Google's machine learning, found and targeted similar users that could be loyal to Viber's brand.
So what's the point?
In order to build a long-term relationship with your customers and keep them interested, you need to go beyond conventional marketing metrics and estimate customer lifetime value. This allows for the customisation of your messages for each different segment of your customers. We also suggest taking advantage of machine learning for more accurate clustering of your customers.
Here at Internetrix, we understand that analysis can be challenging and if you'd still like some extra help, our data science team is more then willing to help out. Leave a comment or get in touch with us to learn how you can measure your Customer Lifetime Value.