Refining The "Metrics Driven" Approach

This is a guest post by Bill Grosso, the Chief Technology Officer of Twofish.

As the social application space has started to mature, more and more application developers have adopted a “metrics driven” approach to game design. Driven partially by the availability of data gathering and analysis tools, partially by the distributed nature of the internet, and partially by the quick turnaround times that are possible if you analyze customer behaviors in real-time, it seems like almost every aspect of application design has gone under the analytical knife.

Which is why I looked forward to last week’s session on metrics at the Social Gaming Summit. The SGS is at the forefront of social game design, and the speakers, David King and Siqi Chen, are outspoken proponents of using metrics to guide every decision, and of making metrics a “core cultural value.” I was looking forward to learning about the latest and most up-to-date thinking from people who are on the forefront of metrics-driven design.

And make no mistake: Siqi and David gave a great talk.

But, somehow, it felt a little bit like a great talk about last year’s metrics. In a conference where every other talk or panel discussion prominently mentioned virtual currencies and virtual items and virtual gifting, it felt a bit odd that the metrics talk was focused on user acquisition, virality, and page views.

What makes it even odder is that virtual currencies and virtual items are natural candidates for quantitative analysis. At the end of the day, people are spending currency to buy things. As a culture, we’ve had 100 years of MBA’s thinking hard about sales reports: about what to measure, how to report on it, how to do drilldowns and user segmentation, and tactics for increasing sales once user behavior is known.

What I really wanted to see, and what I hope to see at the upcoming Virtual Goods Summit, starts with David King’s last slides. In those slides, he called out “Monetization by Gender” as a key piece of data, and split out age as a key bucketing device. This is a great start.

But to my way of thinking, it’s not even half the question. Once you know that a user group is spending money, the key question is what did those people spend money on? Are there items or types of items that sold well to particular demographics? Can you break down your sales by demographics, spot purchasing trends, and upsell effectively? Using a ‘transactional graph‘ doesn’t just lead to more effective monetization, it leads to a better user experience – or, at least, to a more engaged and invested user.

And for those users who have money but aren’t spending it, can you figure out why? Something as simple as “Once they’ve been using the application for 45 days, women over 30 spend 60% less money than during the first 15 days” is a powerful fact that can guide game design and item catalog revisions.

At Twofish, our recently released analytics framework focuses on exactly these sorts of questions. We think crossing user engagement and demographic data with sales data and information about user spending takes metrics to the next level for social application developers.