Increasing user LTV by 45% with automated loyalty and dynamic pricing | PGbiz

What we offer as a company is a cloud-based platform that uses machine learning to optimise in-app purchase revenue, retention and user lifetime value for mobile game apps.

Our products are Automated Loyalty & Retention (creating a native automated rewarding scheme that makes users come back to the game) and Dynamic 1-1 Pricing (finding the optimal price for the right user at the right time).

Large-scale user acquisition stems from the knowledge that only a small percentage of players will convert to paying users. How can further investing in the existing user base help cut these UA costs?

This question actually triggered two years ago what we are today. Back in 2015 our data showed that roughly only two per cent of players are payers and 2.5 per cent of that two per cent is responsible for more than 50 per cent of the total IAP mobile gaming revenue!

It’s time we focus on the existing audience of a game. The right pricing of IAPs, the proper retention tactics and a meaningful loyalty system can make all the difference.
It is an intensively condensed market and for some reason everybody keeps focusing on ads and new user acquisition.

We believe there are diminishing returns in terms of investing money, time and effort in advertising and acquisition, when you are about to lose 30, 50 or even 80 per cent of those users the next day or the day after that.

It’s time we focus on the existing audience of a game. The right pricing of IAPs, the proper retention tactics and a meaningful loyalty system are all aspects that matter to your existing users and are the ones that need to be optimised in order to make them come back to the game, and not just once.

Dynamic pricing is a controversial issue for some. How can this be used to work in the players’ favour?

This is a really hot topic lately, not only whether one should invest in it, but also what is the best technology out there and how it will work automagically with the game without harming its smooth – and fair for everybody – in-game economy.

For us, it’s pretty simple. You just can’t have a flat pricing across everything or everybody, it’s like the Big Mac. You need to adjust your pricing to each country and each user. But, there is a “but”.

It’s not easy to process millions of data, discover price elasticity curves and compute real-time the optimal prices for the optimal users at the optimal time, without affecting the sense of fairness among the global user base or mess-up the in-game economics.

We know it’s hard and that’s where we come in. Machine learning, predictive analytics and deep data pooling are the answer, the only way to make your pricing dynamic and personalised, while keeping your users happy and motivated.

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Increasing user LTV by 45% with automated loyalty and dynamic pricing | Pocket Gamer.biz | PGbiz.