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Core Concepts of Predictive Marketing: Predict Customer Value and Value-Based Marketing

We look at strategies to segment and target customers by their lifetime values, a practice called value-based marketing.

We believe that anyone can learn to do predictive marketing with the right foundation. In our series, “Core Concepts of Predictive Marketing”, Acquia’s Chief Science Officer, Omer Artun shares excerpts from his book: “Predictive Marketing: Easy Ways Every Marketer Can Use Customer Analytics and Big Data.” 

This series will be a guide to everything you need to know about relationship marketing and predictive analytics in marketing. Dive in to learn how to activate your customer data and tap into unlimited opportunity.   

The days of one-size-fits-all customer service are long gone. Not all customers are going to be as valuable to you as others. For instance, the costs incurred from customers who frequently return things they purchase could outweigh the revenue from those customers. In our previous blog post, we defined customer lifetime value in detail. Here, we look at strategies to segment and target customers by their lifetime values, a practice called value-based marketing.

Value-Based Marketing

Any business will have those low-value customers, as well as medium- and high-value customers. The trick is identifying which customers fit into which value buckets and crafting differentiated marketing and service strategies based on the value of each customer. That means keeping perks like unlimited free shipping and returns for high-value customers, rather than the low-value heavy returners.

Three key strategies are used to evaluate customer value:

  • High-value customers: Spend money to appreciate and retain these customers. Pay close attention to retention metrics here.
  • Medium-value customers: Upsell to migrate these customers to maximize their potential. 
  • Low-value customers: Reduce your costs of servicing unprofitable customers.

For simplicity purposes we broke down the customers into three segments, high, medium and low value. We usually designate the top 10 percent of customers as “VIP customers,” since there should be very few VIP customers to pay attention to, the next 60 percent of customers as “average” and the bottom 30 percent as “low profitability” customers. This is easily done by ordering customers from highest to lowest revenue or profitability and choosing the top 10 percent, the next 60 percent and the lowest 30 percent. Some of you might ask the reason why we don’t do this based on an absolute revenue or profitability break (for example, all customers who spent over $500 per year are in the high value bucket). 

The reason for this is to always have the same proportion of customers in the value segments, and track the average value of these segments. This way retention metrics can be calculated for the same portion of the population and this approach is proofed against change in segment averages.

 

Figure 1: Value-Based Marketing Strategies

For an apparel retailer we worked with, high value customers spent $600 on average, where medium value customers spent $120, and low value customers spent $30. This is not atypical, in many cases we’ve seen top 10 percent high value customers contribute close to 30-40 percent of all profits, medium value customers contribute 60-70 percent and low value customers contribute anywhere from 0-10 percent.

For any mix of customer value segments, you need to pay attention to how the mix changes over time, making sure that retention and acquisition for each of these segments trends favorably. The concept behind value-based marketing is to understand the mix of customer value over time. 

Figure 2 shows how this works by looking at the value breakdown of customers over the last 12 months (or if you’re using predictive metrics, you would use predicted next 12 month value against last 12 month value) and cross-tabulated this against the prior 12 month value status of the customer. For example, if a customer was a high value in the prior 12 months and hasn’t placed an order in the last 12 months, this is defined as a lapsed customer. However, there are three lapsed customer segments as shown, ranging from a high, medium to low value segment. High value customers lapsing is much worse than low value customers (which you might even be better off without in some cases where low value customers contribute negatively to profitability).

 Figure 2: Value Transition and Definition of Value Segments

The transition matrix shown in Figure 2 can be used to calculate many useful metrics. The seven segments depicted in the figure describe important patterns in your customer data. 

  1. Segment 1 describes customers who have been inactive for a long time (24 month or more in this example). These are not only lapsed customers, but customers you failed to reactivate. Most marketers have a growing number of these customers over the years and provide a pool of opportunity to reactivate from the past. 
  2. Segment 2 is customers who have existed in your customer database and were inactive and recently have been reactivated. The importance of reactivation is that it counterbalances lapsed customers. 
  3. Segment 3 is customers who stay within their segment of value over time. 
  4. Segment 4 is customers who lapsed in the recent period who used to be active.
  5. Segment 5 is customers who are migrating upward in value which shows they are increasing their loyalty and value. 
  6. Segment 6 is the opposite of 5, these are customers who are migrating downward in value and signal attrition risk.
  7. Segment 7 is customers who are recently acquired and which value they have or projected to have.

As we mentioned before, you can either use actual historical value or predicted future value when utilizing this framework.

Figure 3 shows an example of this framework. For example, we can see that 1,000 customers who used to be high value customers have lapsed. It also shows that of the 21,000 customers acquired, 3,000 of them were high value customers.

Figure 3: Example of Value Transition Framework

Retaining High-Value Customers

Until recently, many firms were unable to identify their high-value customers, let alone give them the white glove treatment. Although airlines, banks and casinos know it pays to make big investments in retention incentives for high-value customers, too many midsize organizations still ignore their best customers.

Spending to retain high-value customers pays off. Often a small percentage of customers make up the majority of revenues. A cosmetics retailer we work with found that 50 percent of revenue came from just 20 percent of customers. When analyzing its best customers, a popular flash sales site found that some of its best shoppers spent more than $100,000 a year with the retailer.

When a popular home improvement website first started to calculate lifetime value for their customers, the brand was surprised to find that some customers spent more than 20 times more than the average customer. These so-called whales were so important to the company that the CEO began picking up the phone to get to know these customers one by one. From those conversations, new ideas emerged on how to better serve and attract more high-value customers. Similarly, a flash sales site decided to send a box of chocolates for Christmas to their entire top 1% of customers. This was well worth the money as their top 1% made up 20% of their revenues. 

In the Harvard Business Review article, “Manage Marketing by the Customer Equity Test” authors Robert C. Blattberg and John Deighton recount the experiences of McDonald’s. The corporation’s managers noted that the value of what they call super-heavy users — typically males aged 18 to 34 who eat at McDonald’s an average of three to five times a week — account for a whopping 77 percent of its sales. Naturally, retaining these customers and getting them to eat at its restaurants more often is a priority. As a general rule it is much easier to get a current customer to use you more often than it is to get a new customer.

Another example of value-based marketing is airline loyalty rewards programs that are based on the dollars you spend with the airline, rather than the miles you have flown. This way, higher paying customers automatically get a greater reward than lower paying customers.

Marketers should use their retention budgets in order to proactively retain high-value customers. If you have an accurate projection of future customer lifetime value, you can experiment on what it takes to retain this customer. Some organizations might consider crafting separate marketing plans or even build separate marketing teams to focus on acquisition and retention efforts. 

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In the next installment of our Core Concepts of Predictive Marketing series, we’ll look at the concept of Predictive Lifetime Value and how to evaluate which prospects are likely to make a purchase.

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