Customer analytics tips for gaming companies

While social and mobile gaming companies are generally at the cutting edge of applying analytics, I recently took an online course from Wharton on Coursera that provided some additional insights in how to best use analytics in online gaming. These takeaways range from ways to improve your UI to how to calculate LTV more accurately.

Make your players wander

One of the most interesting takeaways from the course is that efficiency is not always the desired player behavior in the online world. In traditional retail, retailers found they enjoy much higher revenue when customers wander around the store rather than quickly find what they have come for. In the studies cited, about 75 percent of movement inside a store is not required. Sixty percent of purchases are items people had no intention of buying when they went into the store. Instead retailers optimize for “jiggliness,” as people with the most jiggliness buy the most.

There are uses of this concept for online gaming and iGaming companies. Rather than optimize your lobby and UI (user interface) to ensure your players find what they are looking for, take them on a journey around your game. If it is a social casino, rather than finding the slot they know and love, expose them to some other content, they may find something they prefer.

Higher customer satisfaction may not improve profitability

While customer satisfaction is positively correlated with profitability, the relationship is not linear. Companies with a low level of customer satisfaction, referred to as the Zone of Pain, experience a strong impact on revenue when making improvements. That is, the firms with awful customer service see big benefits just moving out of the Zone of Pain.

On the high end, companies that provide great customer service and differentiate themselves with it experience positive ROI by making the experience even better. These companies are what is referred to as in the Zone of Delight. Retailers like Nordstroms, which enjoy high margins due to their customer service, see a huge impact when they find even better ways to provide a WOW experience.

When customer satisfaction is only a small part of a company’s value proposition, improvements do not necessarily have a positive return. There is a large flat region where increasing satisfaction does not increase profitability. The key takeaway is that the relationship between customer satisfaction and profitability is not linear, but starts with a Zone of Pain, then hits a sizeable flat region, and then moves to a Zone of Delight.

Correlation does not equal causation

We should all know by now that just because two variables have related movement, you cannot assume one is causing the other. I see this mistake made frequently, including by BI experts. Correlation only shows a relationship between two variables. Causation, more critically, shows that one variable produces an effect on the other variable. It is crucial to remember there are three requirements for causality:

1. Correlation
2. Temporal Antecedence. X must happen before Y.
3. No third factor is driving both. Need to control for other possible factors.

Use analytics for pricing

I am surprised at how often pricing strategy in mobile games (the cost of in-app purchases) or in iGaming (RTP and bet levels) is driven by competitive analysis and intuition rather than analytics. Regression, however, can be used to set optimal pricing (including for virtual goods) at the level that boosts profits. Regression can predict demand at prices that have not been tried, thus you can determine profitability for different options. As predictions can be completed for different future prices, you can then determine optimal price. Effectively, you answer the question what you can charge to make the maximum profit (and with virtually zero marginal cost for online products, can be simplified to maximum revenue).

Preparing better surveys

While market research is a less than reliable way to understand customer intent, it still provides valuable insights into your players. Surveys are a good way to learn about potential customers and are relatively low cost. Some best practices include:

• • To improve reliability of surveys, test and then retest. If the results are consistent, it shows you are getting reliable results (people still may not know what they want though).
  • There are multiple ways to ask questions in a survey (comparative, rank, paired comparison, Likert, continuous, etc.) and you should understand your end goal when deciding which format to use. Advantages of open-ended questions allow for a general reaction that can help interpret closed end questions and may suggest follow up questions. Closed end requires a lot of pre-testing but is easier to administer.
  • Focus on drafting high quality questions. Use simple, conventional language and avoid ambiguity. Do not ask any questions more than 20 words. Most importantly, avoid leading and loaded questions (i.e. How bad a job is Lloyd doing?).
  • Pay attention to sequence and layout. Start with an easy and non-threatening question. Have a smooth and logical flow. Have the questions go from general to specific. Keep the sensitive or difficult question at the end.
  • The key to using surveys effectively is validity, how well it predicts variables you are interested in. If you find surveys effectively predict certain behavior, then they are an appropriate tool for predicting that variable.
  • Make sure your results are generalizable to an appropriate population. You need to define clearly the population, choose a representative sample, select respondents will to be interviewed and motivate them to provide information.
  • Pre-test your survey. Ensure respondents understand each question and the questions make sense.
  • Collect data on non-respondents as they may be systemically different. Try to convert them to responding.

Recency is incredibly important

When looking at the future value of a customer, the three keys are how recently they made a purchase (recency), how many purchases they have made (frequency) and monetization (size of the purchase) recency is by far the best predictor of future value. Frequency is then significantly more indicative than monetization. Thus, focusing on increasing the size of a purchase (up-selling) is the least valuable strategy you can pursue to increase your customer’s lifetime value.

Include clumpiness in your LTV analysis

I wrote several weeks ago about the important of clumpiness in determining a customer’s future value so will not go into too much detail again. Clumpiness refers to the fact that people buy in bursts and that those customers could be extremely valuable. When calculating customer value and segmentation, we focus on analysing recency, frequency and monetization of the customer, as I discussed above. This analysis is based on customers making purchases in a regular pattern, i.e. coffee, diapers or milk. For certain products (and I would classify social and casino games here), customers actually monetize in bursts. Thus, you need to add C for clumpiness to your modeling.

Key takeaways

• People who wander around a retail location spend more than ones who immediately find what they are looking for and retailers optimize to create this jiggliness. Online casinos and games can also build in jiggliness so players find new games and offerings rather than simply quickly go to the one they are looking to play.
• While satisfaction with customer service positively impacts profitability, the relationship is not linear. Improvements have a strong impact when players are highly dissatisfied (and that is corrected) or when customers with great service make further improvements, companies in the middle often do not see a positive ROI on CS improvements.
• A relationship between two variables does not show one is causing the other, to have causation there must be a relationship plus temporal antecedence plus the absence of a third variable driving both factors.