A quick recap: in part 1 of this series, I outlined a couple of basic ways to define a customer acquisition funnel and explained how it falls short when measuring freemium products, in particularly free-to-play. In part 2, I continued to explain two alternative measurement models for free-to-play and focused on Lifetime Value (LTV) as a key metric. A core feature emerged: the spread of possible LTV through the audience is immense, ranging from 0 to, depending on product, hundreds, perhaps even thousands of euros.

This finding isn't limited to just money spending, but is seen over all kinds of behavior, and is well documented for social participation as the 90-9-1 rule. From a measurement point of view, one of the most overlooked aspects is how it destroys averages as a representative tool. At the risk of stating the obvious, an example below.

When 90% of a freemium product players choose the free version and 10% choose to pay, the average LTV is obviously just 1/10th of the average spending of the paid customers. However, when there's not just a variety of price points, but in fact a scale of spending connected to consumption (or if we're valuing something else than spending, such as time), the top 1% is likely to spend 10x or more than the next 9%. Simple math will show you that the top 1% would be more valuable than the rest of the audience in total, as illustrated here:

The average? 0.19. Now, can you identify the group that is represented by "average spending of 0.19" in the above example? Of course you can't - there is no such group. Averages work fine when what you're measuring follows some approximation of a standard distribution (e.g., heights of people), but they break down with other kinds of quantities. Very crucially, they break down on behavioral and social metrics. Philip Ball's book Critical Mass goes to some length on the history of these measurements, if you're interested in that.

Instead of measuring an average, you should identify your critical threshold levels. Those might be the actions or value separating the 90% and 9% value players, and equally, 9% and 1%. Alternatively, you might already have a good idea of your per-user costs and how much a customer needs to spend to be profitable. Measure how many of your audience are above that level. Identify and name them, if you can. Certainly try to remember them over time to address them individually. This goes deeper than simply "managing whales", to use the casino term. Yes, the top 1% are valuable and important to special case, but it's equally if not more important to determine what are the right strategies for developing more paying customers from the 90% majority.

This is why it's important to measure everything. If you only measure payment, the 90% majority will be invisible to your metrics, and it's usually very hard to identify ahead of time which other measurements are important for identifying the activities that lead to spending. Instrumenting your systems to collect events on all kinds of activities on a per-user basis (rather than just system-level aggregates) enables a data mining approach to the problem. Collect the events, aggregate them across time for each player (computing additional metrics, when appropriate), and then identify which pre-purchase activities separate those players who convert to paying from those in the same cohort who do not. There are several strategies for this, from decision trees to Bayesian filtering to all kinds of dimensionality reduction algorithms. The tools are already pretty approachable, even in open source, whether as GUIs like Weka, in R, or with Big Data solutions like Apache Mahout, which works on top of Hadoop.

Essentially, this approach will surface a customer acquisition funnel akin to what I described earlier, but using the raw measurement data. It will probably reveal things about your product and its audience you would not have identified otherwise, and allow you to optimize the product for higher conversions. The next step in this direction is to replace the "is a customer" criteria above with the measured per-player LTV value. Now, instead of a funnel, you will reveal a number of correlations between types of engagement and purchase behavior, and will be able to further optimize for high LTV. Good results depend on having a rich profile of players across their lifetimes. A daily summary of all the various activities in a wide table with a column for each activity, and a row per player per day is a great source for this analysis.