Core Concepts of Predictive Marketing: What is Customer Lifetime Value

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How to calculate customer lifetime value using predictive marketing techniques.

Customer lifetime value is a term that describes how much revenue or profit you can expect from customers over their lifetime doing business with you. There are a couple of different ways to calculate and use lifetime value, depending on the marketing problem at hand. 

Historical Lifetime Value

Historical lifetime value (or LTV for short) is defined as the actual profits — gross margin minus direct costs — from customers over their lifetime so far, adjusted by subtracting the acquisition cost of those customers. Note that historical lifetime value only takes into account past purchases, not future purchases. The only time to use historical lifetime, rather than predicted customer value, is when you are trying to detect if the customer value of a specific customer or customer segment is trending up or down.

A customer might have spent $500 two years ago, but only $200 in the last year. It is this change in lifetime value that signals underlying trends, risks and opportunities. If a customer’s historical lifetime value is trending down, this is called value migration, and this can be an early warning signal of customers unsubscribing from your service or planning to stop buying from your website. Detecting value migration allows you to catch customers before they walk out the door and it is too late to win them back. Identifying a change in historical lifetime value allows you to implement a reactivation or proactive churn campaign to turn the tide.

Beyond value migration, customers may be changing their spending habits in other important ways. A certain customer may have made only one big purchase last year, but this year they are making smaller purchases more often. While the customer lifetime value of this person has not changed, your marketing approach and goals for the person should change.

To accurately calculate historical lifetime value, you need to be able to link all purchases made by the same person — even if that person used slightly different emails, names or addresses. The average American, for example, has three email addresses. If you are like most companies, you may have separate order databases for different channels. The orders from the web are often recorded separately from the bricks-and-mortar store purchases and those are separate from sales made through the call centre. Unless you can link purchases from these separate channels to the same physical person, you will not have an accurate picture of lifetime value. For some products, it might be important to also understand the total value of a household or an account. Perhaps I spend only very little with a brand myself, but by acquiring me, the brand has also acquired the revenues from my spouse and children. When comparing the costs to acquire me to the revenues of my entire household, I could be a very profitable investment. The only way to do this is to make sure you can associate family members of a household to each other. Similarly, in business marketing you need to be able to associate different buyers in the same company with the master account to understand the true value of a customer.

We recommend that you take the cost to service a customer into account whenever you can in order to calculate historical lifetime value. This includes returns and discounts, as well as product cost. On average, 9 percent of all retail sales in the United States are returned by consumers, so ignoring returns would skew the results. Some practitioners calculate lifetime value (LTV) without the acquisition cost. If LTV is being utilised to make acquisition decisions, acquisition cost should be taken into account. However, if it is for existing customers, acquisition cost is a sunk cost and should not be used.

Predicted Customer Value

Predicted customer value is the projected value, revenues and costs, adjusted for the time-value of money, of a customer looking forward several years. Your average retention rate will tell you how many years in the future, on average, you will retain a customer and how many years of future revenues to take into account. We typically look one to three years ahead when calculating predicted customer value.

Predicted customer value is very useful, especially when deciding how much money to invest in acquiring or retaining a specific customer. If you were to only look at historical lifetime value, you would significantly underestimate the potential of a customer and likely underinvest in the acquisition or retention of certain customers. It can also be used to identify high-value customers very early in the lifecycle. After her first purchase, a future high-value customer looks just like everybody else. If you could recognise the high-potential customer early in the lifecycle you could start differentiated treatment right then and there and increase the odds this high-value customer will stick with you.

One person might have just bought this expensive jacket, but he might have only been a customer for two months, but another customer might have been a customer for five years and bought the same jacket. If you were to look at historical lifetime value, you might draw the conclusion that one customer is more valuable than the other. However, these two might very well become equal value customers and should probably be treated in much the same way. If you look at historical lifetime value you look too much at old customers and will miss the opportunity to acquire or retain more recent, high-potential customers. With predictive analytics, you can estimate the future value of a customer by comparing a customer to the thousands or millions of others that have come before them. You can predict future lifetime value by finding customers that look just like them. From the example we used earlier, buying a certain type of jacket may very well be an early indicator of a well-known pattern of behaviour for a high-value customer. Even if predicted customer value is not accurate in absolute dollar terms, the rank order it provides gives the marketer focus on the right segment and trends.

Here are some examples of factors that can signal future lifetime value. Predictive marketing software typically looks at hundreds of factors like these but will only use those that actually correlate with future lifetime value in your particular company or situation: 

  • Recency of engagement: The recency of purchases, web visits, reviews and email clicks may all be important predictors of future purchases and thus future customer value.
  • Size of the first order: Customers who make a large first order are more likely to end up being valuable shoppers.
  • Discount on the first order: Customers who buy full price are more likely to become valuable over their lifetime.
  • Multiple types of products in the first order: Buying from different categories, such as shoes and electronics, in your first order is a signal of future customer value.
  • Time between orders: Most valuable shoppers make frequent purchases and thus a shopper who places a second order quickly is more likely to become a high-value customer.
  • Time spent on website: The more time prospects or customers spend on your website, the higher their likelihood to buy and the higher their predicted customer value.
  • Social and email engagement: Customer engagement of any kind, including email opens and clicks or social engagement, are great predictors for likelihood to buy and predicted customer value. Often it is not the amount of engagement that matters, but the consistency or frequency of engagement. Spending a little time every day is a more reliable indicator than spending hours sporadically.
  • Acquisition source: It turns out that certain channels drive higher value customers than others. The customer who came from a fashion blog may have a higher predictive value than the customer acquired through a banner ad.
  • Geography: Customers in certain post codes have a greater predicted customer value than others. Rural populations tend to be more stable, move less frequently and therefore have more loyal purchase behaviour. Post codes can sometimes predict what type of products people buy. For example, post codes with many apartment buildings have a low predicted customer value for certain products, such as lawn mowers.
  • Seasonality: Retail customers who are acquired during the holidays tend to be about 14 percent less valuable than those acquired during other times of the year.
  • Personal referrals: People who came to your brand through a personal referral tend to be more loyal than those who buy because of an advertisement.

Predictions about lifetime value are not destiny. Marketers can do much to change the course of history here. Take, for example, the fact that shoppers acquired during the holidays tend to be less valuable and less loyal than shoppers acquired at other times during the year. One skincare company decided to focus its retention efforts on this holiday cohort specifically. It set up an email marketing campaign to increase brand loyalty among new Cyber Monday customers, sending regular reminders for refills and recommending other products of interest. They were able to reverse the trend and lifetime value of these new holiday customers is now 5 percent higher than the company average. By focusing on specific outreach to underserved customer segments, the company was able to offer personalised promotions that ultimately drove greater brand loyalty. The important lesson is that once customers are acquired, the best strategy is to focus on engaging them to grow and retain them, ignoring the cost of acquisition.

Upside Lifetime Value

Upside lifetime value, which is also called size of wallet, calculates how much more money a customer still has to spend with you. This is money that the customer is already spending at your competition to buy the products you offer. Algorithms can figure out size of wallet by comparing a customer to other like-minded customers. It is important for marketers to focus on what size the wallet is, because it is always easier to grow a relationship with an existing customer than to acquire a new one. Unfortunately, most marketers have been taught to focus more on new customer acquisition than on engaging and retaining existing customers. Especially if the customers have high upside potential, marketers should focus on how to deepen their relationship by introducing them to new products or serving them in a differentiated way. Very few companies calculate and utilise the upside or share of wallet potential of a customer, yet it can be a very powerful way to identify customers to focus on.

The critical difference between future lifetime value and share of wallet is often in the types of products that are factored into the analysis. For future lifetime value, you tend to look at just those products that a customer is already buying from you. For example, I may be a hockey player and I may be buying my hockey tape from a specific outlet every couple of months. Based on this, the company can project that if they retain me, I will buy a lot more hockey tape in the future and perhaps have a predicted lifetime value of $300. However, because I am buying hockey tape, I am likely in the market for skate sharpening, hockey sticks and occasional gear upgrades. I am clearly buying those things elsewhere right now. If I were to buy all of my gear at the same place I buy my tape from, my future lifetime value is probably well over $1,000.

Let’s look at another example, this time in business marketing. An electronics company has quite a few customers who only buy inkjet cartridges for printers. They buy these cartridges regularly and spend $20,000 a year on average, leading you to think it’s a great customer. But the fact they are buying these high-end cartridges means that they probably also have a big office with servers, laptops and other products that could use services or add-on products that you are not selling to them. The fact that you are not selling those other products to them is a missed opportunity.

You can use share of wallet analysis to find upsell targets as one business software company did. It took all of its business customers and broke out those that were similar in size and industry. Out of 100,000 customers, they found 20,000 businesses in the insurance industry with 100 to 150 employees. They then divided these customers into value segments. The top 25 percent of these small insurance companies spent $30,000 a year, the next 25 percent spent $10,000 a year, the third 25 percent spent $5,000 a year and the bottom 25 percent spent $1,000 a year. All of these businesses are similar, and they all have a similar spending potential. Maybe not all of them will be large customers spending $30,000 a year, but all should at least be able to spend $10,000 a year, or as much as the second group. This means that for all the customers in the $1,000 bucket, you have an upside potential to sell more products and services of $9,000 and for the customers in the $5,000 bucket you have an upside potential of $5,000.

In our next Core Concepts of Predictive Marketing blog post, we’ll look more into the different types of value-based marketing strategies that can help brands retain their VIP customers.