Pini Yakuel is co-founder and CEO of Optimove. Optimove’s retention automation platform helps marketers maximize revenues and customer loyalty by automatically personalizing, executing, and evaluating a complete framework of customer-data-driven campaigns. The Web-based software’s unique customer modeling, predictive micro-segmentation, and campaign optimization technologies have already enabled dozens of iGaming and social gaming operators to become highly successful data-driven marketers. He provides a new way to look at marketing campaigns in this article.

As a marketer or retention expert at a social gaming operator, you are spending plenty of time and money on your customer retention campaigns. But did you know that there is a foolproof way to dramatically improve the effectiveness of your campaigns? The secret lies in treating every campaign as a scientific “marketing experiment.” In other words, focus on accurately measuring the uplift of your campaigns – in monetary terms – in order to optimize future campaigns and maximize the revenues they generate.

Many marketers use push notification response rates, in-app pop-up click rates, and email open rates as their primary means of measuring campaign effectiveness. These metrics may provide important insight into brand strength and customer engagement, but these metrics provide no indication of what players actually did in your games after they clicked on the campaign. Even more importantly, these direct response metrics tell you nothing about the monetary uplift generated by the campaign – and this should be the most important metric you’re looking at.

Control Groups

The key to determining the effectiveness of any customer marketing campaign is the proper use of control groups. A control group is a subset of the players you’re targeting with a particular campaign who you decide will not receive the campaign. The members of the control group are randomly selected to represent the entire target group of players. In other words, they should be similar to the members of the entire group and thus be exposed to the same set of conditions, except for the particular marketing campaign being tested.

It is very important that each control group is a representative sample of the overall campaign population, and that the size of the group is large enough to generate statistically significant results (typically 5-20 percent, depending on the overall size of the target group and the expected response rate).

Analyzing the Results of your Marketing Experiments

The central idea in treating every campaign as a marketing experiment is comparing the response rates and spending volumes between those players who received the actual campaign (the “test group”) and those who were in the control group throughout the duration of the campaign. It is not sufficient to look only at the purchases made by the test group, because a certain percentage of those players would have spent money during the measurement period anyway. Rather, this approach is about measuring the increase in spend observed among the campaign recipients versus the amount they would have been expected to spend in absence of the campaign – something that is only possible to determine by also examining the spending patterns of the control group during the same period.

One might think that by simply comparing the purchase rates and spend amounts of a similar group of players from a previous time period (when no campaign was run) to the current period (in which the campaign ran) can reveal how much additional revenue was generated by the campaign. The reason that this comparison is not valid is that there are always numerous other factors affecting customer behavior from one period to another. It is crucial that the comparison between the test and control groups is made during the same time period.

Running a Clean Experiment

To achieve high results integrity, it is important to ensure that no additional factors under your control are influencing player spending behavior. In other words, during the measurement period of a particular campaign (usually a number of days), the test and control groups should not be exposed to any other targeted offers or incentives. If customers are receiving multiple simultaneous campaigns, it becomes impossible to measure the effectiveness of any one campaign. This is called isolating your marketing experiments.

Thus, keeping track of which customers are within the duration period of other campaigns and excluding them from any additional concurrent campaigns is a crucial aspect of selecting target customer groups for any marketing campaign.

Optimizing your Campaigns Based on Uplift Results

It is important to note that for this approach to truly succeed, you will need to run all your campaigns as marketing experiments. However, selecting target groups for recurring campaigns, isolating valid control groups from each group, and analyzing all of the results is not an easy process to perform manually (e.g., using Excel). This is especially true for companies running dozens of marketing campaigns every month, including regularly recurring ones for particular target groups (new players, big winners, high spenders, most socially active, customers at risk of churn, etc.). The answer is to adopt a retention automation software system that automates this entire process.

Once you’ve made the switch to evaluating the financial uplift of every retention campaign, you are able to use the results to optimize the campaigns you send your players. By systematically evaluating every campaign and comparing the results of different campaigns sent to the same types of customers, you will know, with certainty, which campaigns generate the most revenues from each target group.

Over time, you will be able to fine-tune the timing and mix of your campaigns to maximize the revenues you can generate from your existing player base.

To hear more from Pini, you can follow him on Twitter or connect with him on LinkedIn.