Josh Chandley

You can't just run an A/B test and look at D360 ARPU. Whenever you try to improve LTV you have to take a particular lens, a proxy for your success.

WildCard Games looks at D30 ARPU growth from install as north star metric. From experience, when focusing on earlier metrics they've seen great initial impact in the short term but that didn't play out in the long run.

It is very different to focus on maximizing LTV for users that have not yet installed the game vs. maximizing LTV for players that have been in the game for 6 months. Older players already have a system in their heads that they are used to, but with new users you can try a lot of "weird and wonderful" things.

The downside of having D30 ARPU growth as a north star metric is that A/B tests require many weeks and tens of thousands of users. They currently have 6 or 7 A/B tests which means they're thinking about test ideas before even getting the results, which requires a lot of patience and discipline.

They have exposed their north star metric of D30 ARPU growth to the team and set quarterly D30 ARPU growth goals at the game level, so the whole team is accountable. Benefits: 1\. It gets buy-in on chasing LTV growth 2\. Once you get better at predicting that, the UA team can think of how much they can spend now vs. what they will be able to spend.

The second you add a new feature to your game, all your models and previous A/B tests are no longer totally valid. It's the same thing with your ARPU curve.

The high level definition of LTV as ARPDAU\*Retention has no application in the real world: changes in the game can have unforeseen side effects. You can improve ARPDAU but a few weeks into the game it goes horribly.

Showing more interstitials to users might not result in a higher net LTV long term but if it changes the shape of your ARPU curve and reduces your payback window it allows you to spend more aggressively.

A way to test creatives at a lower cost is to start in India with a small budget to filter them out first, then move to Canada and deploying the best-performing ones in the US.

LTV curve is important, modeling is important. But the underrated thing is the communication between the UA and product team. The ARPU/LTV curves are backward-looking. Since the UA sources you're scaling can bring players with different behaviors (e.g. VO on Facebook vs. incentivized traffic or pre-installs) your product team needs to be aware of it to build the right features (e.g. focusing on whales features vs. ad features).

For decision-making on features, they've been using the concept of LTV vs. effort: LTV impact for days of efforts required. The same way the dev team defines how many days of efforts are necessary for a feature, the product team is responsible for creating the same thing for LTV impacts.

It's really important to have the product team set clear ARPU and LTV growth goals and communicate them to the UA team. Example: interstitial ad frequency test → the UA team needs to know that D1 ARPU might be great and not go 10x on spend.