Predictive modeling is essential to the success of marketing strategies and plans. The goal is to use one or more predictive modeling techniques to identify the target population likely to respond positively to a specific campaign or other marketing activity, as well as to understand the behavior of targeted groups.
Consider what happens when a telecommunications company does a normal random marketing mailing versus a mailing based on predictive modeling, which enables more strategic targeting. In this example, the company uses predictive modeling to generate the graph in Figure 1, which analyzes the first decile of customers (the top 10 percent by revenue) and shows that 30 percent of these customers have a high likelihood of attrition – a key group of customers for any company to focus its retention efforts on. The graph also helps the marketing department focus its retention activities on key target segments (and save the money to spend elsewhere). Failing to target funds in this way leads to diminishing marginal returns – but getting it right means the telecommunications company benefits from:
- Increased response rate by contacting the right customers.
- Reduced campaign cost by selecting the customers most likely to respond.
- Stronger customer relationships by understanding the target population and conveying messages that are highly relevant to them.
Applying predictive models to your marketing strategy
- Predictive modeling helps you execute your marketing strategy – and ultimately achieve your broader marketing objectives. As you think about the objectives you are trying to achieve, consider the following questions, which will guide you toward selecting the predictive modeling techniques used to drive those marketing programs – and ultimately to the treatment strategy used to execute it:
- Why will my customer attrite?
- When will my customer attrite?
- Who is saveable?
- Who will buy? What will they buy?
- Which product will the customer buy next?
- When will the customer buy?
For example, when marketing asks the question, “When will customers attrite?” you could answer this question using a survival model that identifies customers who will likely leave to go to a competitor within three months, six months or even one year. With ample notice like this, you can take proactive steps to prevent attrition. Other types of predictive models can help you identify which customers can be convinced to stay, as well as:
- Determine how to maximize revenue – for example, by understanding which customers will buy which products and designing campaigns accordingly.
- Calculate a customer’s propensity to buy specific products – insight needed to develop highly targeted campaigns and offers.
- Identify the sequential order of purchases by performing a market basket analysis.
- Identify when a purchase will likely be made by customer segments or individual customers by using a survival model.
After determining the appropriate modeling approach to meet your needs, the next step is to develop a treatment strategy. This involves using analytical models to determine customer value and define customer segments. When done correctly, you can create an individual view of the customer that – when combined with segmentation and customer value analytics – enables you to develop a specific treatment strategy that will optimize outcomes.
Stepping up to advanced predictive models
Once your company becomes proficient with a basic predictive modeling strategy, you can use more advanced models to realize even more performance improvements. Advanced models answer the same questions mentioned above, but with more precision or sophistication. For instance, you can determine the specific time horizon for the predicted attrition. And using an advanced survival model enables you to identify customers who will attrite or buy within certain windows of time (for instance, within three months). Using the analytic insight enabled by advanced predictive modeling, you gain an additional level of information to further improve returns on your marketing efforts.
The white paper, A marketer’s guide to analytics more clearly describes the benefits you can see with predictive modeling. It also includes examples and case studies that show improved outcomes and marketing ROI.