Changing the numbers does not change the reality

I am a huge proponent of using analytics and other metrics to drive business decisions, but I repeatedly see people making a huge and avoidable mistake. Instead of using the data to determine the best strategy, they use data to justify their intuition. A good analyst can use data to draw virtually any conclusion and if the analyst is pushed in a certain direction by the business leader, all the data does is provide people with cover for the decision rather than leading you in the optimal direction.

The same situation applies to financial analysis. I have seen people frequently manipulate numbers, often with the approval or even encouragement of the target audience, to tell the story people want to hear. I have seen this manipulation in sales, in corp dev and in internal forecasting. In all situations, it is actually just a rationale to make a decision the person already wants to make.

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Data manipulation

The first part of the problem is manipulating the data. I am not talking Enron here, but more subtly and maybe not even intentionally. People will often select the data that supports their position while discounting the other information. If you want to greenlight a certain feature, you may look at the impact on retention while neglecting the impact on monetization and rationalize it by saying it is a retention feature. Regardless of whether it is a retention or monetization, your goal is to optimize lifetime value (LTV) so you need to look at the data holistically. Continue reading “Changing the numbers does not change the reality”

Lifetime Value Part 11: How to calculate LTV

Last year, I published a series of posts on the importance of knowing your users’ or players’ lifetime value, the key components and how to impact them and techniques to increase the accuracy of your customer lifetime value (LTV) predictions. I intentionally did not publish a formula for calculating LTV—while it is always a factor of retention, monetization and virality—as it is different by product and there are many alternative ways to get to an accurate customer lifetime value. Prompted by an infographic that I came across (see below) I did want to go into some details of the mechanics of calculating LTV.

The first step is to obtain your key variable metrics as averages across all users. The ones I prefer are ARPDAU (average revenue per daily active user), day 1 retention (how many people who use or install your website, app or game come back the next day), day 30 retention and k-score (how many free/organic users does a user bring in). Continue reading “Lifetime Value Part 11: How to calculate LTV”