The case for pLTV in user acquisition

Noy Rotbart, Rohit Sharma | November 8, 2022 · 3 min
Illustration of pen ticking off a checkllist

Here's how you can determine if your business case lends itself well for predictive LTV to improve your ad spend.

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. However, early signs for profitability can be quite misleading in the long run. To illustrate this situation, we made an info-viz tool for our clients to show the hidden potential in their audience.

We first order clients according to three parameters:

  1. Revenue from the first month

  2. Churney's 12-month pLTV predictions based on the first 48 hours activity

  3. Total 12-months revenue

We now take the top ½ of every such sorting of the population and plot their revenue below.

The marketer with a crystal ball would have chosen to focus on C and have to settle for determining based on what they know, which is A.

Setting the time bar to one month reveals that A starts off much more promising with an average 1-month LTV 35% greater than both B and C. As we increase the time passed, the truth of the gap in performance reveals itself, as all three roughly equate within three months and arrive at a pretty dramatic difference of +25% between C and A, and +10% between B and A.

As we onboard our clients, it is helpful for them to be aware of the comparison between A and C in their case to determine if there is a point in producing the pLTV signal to help win this performance gap. It also demonstrates that in the setting of recurring revenue, one should be careful not to optimize for the wrong metric, thinking it is a good neighbor to actual revenue, and that computing and using a more sophisticated metric is highly valuable.

  • LTV
  • Acquistion