Most of the media and e-commerce Chief Data Officers I talk to on a daily basis dream of an active collaboration between their Marketing and Data Science teams. To realize this goal, we suggest setting a use case that promotes cooperation between colleagues with different skill-sets. One of our favorite cross-team approaches is to practice a use case involving Churn Analytics.
According to Wikipedia, the definition of churn is:
"Churn rate (sometimes called attrition rate), in its broadest sense, is a measure of the number of individuals or items moving out of a collective group over a specific period of time."
Keep Your Fishes Home
The first step in a churn-based data science project is to define the model used by an organization; typically, there are two varieties: subscription and non-subscription. Some examples of subscription types can be found in our customers:
Determining whether or a not a customer will become a churner (i.e., no longer remain a customer) is fairly straightforward in subscription models, but a bit more challenging in non-subscription models. In subscription models, a customer churns when they request cancellation of their subscription. In non-subscription models, however, you need to analyze your customer’s behavioral tendencies in order to identify potential churn (e.g., the amount of time since he last used the company’s services/products). The goal is to then determine the specific point when your customer will no longer use your services or products.
Churn projects are typically launched when the customer acquisition rate diminishes. For most companies, the customer acquisition cost (cost of acquiring a new customer) is higher than the cost of retaining an existing customer… sometimes by as much as 15 times more expensive (Winning New Business in Construction by Terry Gillen, 2005). Therefore, the challenge of implementing a successful churn project is to increase customer loyalty and, consequently, increase company revenue.
There are two complementary modeling approaches used to predict churn:
In both cases, it is crucial to connect your models to marketing-driven actions in order to attain churn reduction. Some examples of short and long-term actions include:
Only a combined approach of mixing short-term actions (in order to retain potential churners) with longer-term approaches will have an effective and sustainable impact on churn reduction.
Churn analytics projects can be addressed by Data Science and Marketing teams thanks to Machine Learning modeling (classification) with a defined target. The target is known in subscription business models while it needs to be defined in non-subscription scenarios.
Segmentation: Segment your customers based on their behavior and address the question, “Which customers do we care about?” Only the best? The most valuable? Regardless of the answer, a churn reduction campaign should be targeted toward a well-defined customer segment;
Compare to Control Population: By understanding the extremes of churners, new customer classes can be created and refined. On one extreme there are customers who interacted with the product at least once, but no longer visited afterwards. The other extreme includes customers who make frequent uses or purchases and are heavily engaged with the product. In this context, the definition of a “new customer” can be formulated along with an understanding of customer groupings;
What Makes your Churner Different?: Data collected from the above analysis, when subjected to Machine Learning modelling, enables your company to discover differential patterns among churners and identify what makes your churners different from others.
Implementing a churn scoring mechanism relies on a pair of processes:
Yes, the way you do marketing is about to change.
The process of churner identification and behavioral analysis involves expertise from both Data Scientists and Marketing Specialists: one party understands the customers whilst the other can measure & analyze behavior. At the end of this process, it is time to apply the information learned to the company’s loyalty program. The output of a churn project is a dataset (Excel or CSV file, or a table stored in your customer database) that contains the customer ID and an associated churn score. This churn score indicates the probability of the customer abandoning your product or service. With this score, the Data Science and Marketing teams can build business rules that define customer segments.
Churn scoring is used to assign a score to customers that conveys the potential loyalty of the customer. Churn scores enable Data Science and Marketing to build business rules together in order to define customer segments. For example, a churn scoring mechanism would enable your company to creatively segment customers — sample types and potential actions include:
In order to achieve optimal results, the actions need to be customized based on your business requirements and your knowledge of customer behavior & expectations. Actual deployment of your churn scoring methods can be done by feeding e-mail marketing automation tools and push engines for in-app notifications. Want to fight against customer churn, but don’t know how to begin your project? Have a look at our Whitepaper “The Modern Marketer’s guide” to find out how to deploy your marketing project.
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