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The case for pLTV in user acquisition

Noy Rotbart, Rohit Sharma

Illustration of pen ticking off a checkllist.

With the phasing out of third-party data, many companies use the various conversions APIs to share meaningful data to improve their ad spend. Presently, they target conversions for deterministic events such as subscription made. This way, all conversions are treated equally and are used as target for the ad platform. The more sophisticated companies would differentiate between these conversion events to differentiate between higher value subscriptions or even previous indicators for high-LTV users. ...

Predicting churn - or predicting what you can do about it?

Brian Brost

Illustration of people walking on a path

Roughly speaking, growth teams have two major responsibilities: (i) to acquire the best possible customers at the lowest possible costs, and (ii) to maximize the LTV of their existing customer base. In this blog post, we focus on the second problem, and why predicting churn is a useful starting point, but inadequate to solve the problem on its own. A study from the Harvard Business Review demonstrated that a 5% increase in retention can be expected to lead to a 25%-95% increase in profit. ...

Early LTV predictions - 5 pitfalls you want to avoid

Christian Hansen

Illustration of people walking on a square raised to different levels

The task of early LTV prediction can be phrased as: Get the best estimate of a user’s LTV, using as few hours of user activity as possible. The first part is easily understood; the better the estimates, the better your ad platform can focus the ads on high LTV customers. But for the second part, why is it essential to use as few hours as possible? By reducing the time between a user first appearing and you sending an optimization event back to the ad platform, the platform can learn to identify and target valuable customers earlier; in addition, if the event comes too late, the ad platform may be unable to use it all because of restrictions in their optimization algorithms and limitations on their ability to match the user correctly. ...

Computing a churn curve in your BI system

Noy Rotbart

Illustration of people on consecutively higher levels

We begin with the table in which each row contains the subscription ID, start, and end date. If the subscription is still ongoing, the end date is blank. For convenience, we bound each subscription ID with some properties such as frequency, age, inbound channel, country, and marketing allowed. The first column we need to define for each row is the number of payments made, a function of the current time, subscription start date, and end date. ...