Today we published the white paper Unsubscribe Predictor. This research teaches brand, agency, and product professionals how to maximize and predict mobile marketing customer retention using knowledge of various factors such as industry, time of day and message content.

As marketers ourselves, we know that occasionally you need an academic perspective to mine for details. Other times, however, you just want something that resembles a Cliffs Notes version. For those in the latter camp, we wrote the “Marketer’s Guide to Unsubscribe Predictor”: a two-part layman’s terms walkthrough of our analysis and findings for maximizing customer retention.

In today’s first installment, we’re going to look at the challenge we set out to solve with Unsubscribe Predictor. Stay tuned for the second installment, which discusses our research approach and highlights the paper’s key findings.

Minimizing Unsubscribe Rate and Maximizing Customer Retention: The Challenge We Set Out To Solve With “Unsubscribe Predictor”

Just like any type of direct marketing, one of the most meaningful KPIs in mobile marketing is unsubscribe rate. Lower unsubscribe rate results in higher customer retention and, as a result, greater customer lifetime value. Those marketers with the lowest unsubscribe rate are most successful at earning maximum mobile ROI and customer retention.

Here’s a simple back of the envelope: ACME has one million customers in its mobile marketing database. Each time the marketing team sends out a mobile coupon, costing ACME 5% of revenue, 20% of customers redeem the coupon and spend $10. Notice how a 1% change in unsubscribe rate affects Gross Profit for one blast:


Savvy marketers know that the above tells just a fraction of the story. The ACME team has to factor in not only how unsubscribe rate affects the first outbound blast, but also subsequent messages sent to the same list for customer retention:


To be clear, even leaving everything else constant, a 1% increase in unsubscribe rate to 2% causes ACME marketers to lose ~$106K after sending five blasts – not even one month’s worth of content. In addition to these two issues, ACME marketers also have to consider re-marketing versus new acquisition costs. Given that re-marketing remains significantly less expensive than acquisition marketing (regardless of the industry), unsubscribe rate has an even more pronounced impact on bottom line profit.

With such dollars and cents at stake, we wanted “Unsubscribe Predictor” to address the challenge of unsubscribe rate minimization for marketers. From common sense and practical experience (with all marketing channels, not just mobile), marketers know that sending any outbound content results in some non-zero unsubscribe rate. It’s the nature of the business, as customers’ individual preferences and unique purchasing histories lead them them to opt out once a brand’s content is top of mind. That’s why minimization of unsubscribe rate holds paramount importance for maximum customer retention.

Now, Waterfall first started tracking mobile marketing outbound content in 2005. With almost 10 years of data, we knew that we had the ability to objectively determine the precise characteristics that impact unsubscribe rate, and to what exact degree. Running the data through a solid mathematical model, we could provide marketers with specific instructions about how to alter the various factors under their control to maximize customer retention. Plus, with new data coming in every minute, we can add to the model’s data set everyday and improve it constantly.

To download the “Unsubscribe Predictor” white paper free of charge, please click here. Stay tuned for the next installment of A Marketer’s Guide To “Unsubscribe Predictor,” as we take a look at our research approach and findings. 

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