Customer lifetime value and pricing
Some customers are worth much more to a company than others. As a company, how do you take that into account in your pricing? A practical look at using customer lifetime value for pricing strategy.
The case for lifetime value-based pricing
One customer is not the other. Some customers are fun to work for, for others the discount is never enough and you never do it right anyway. You might consider doing something with that in your pricing, such as a fun-discount for fun customers. Less arbitrary is to consider customer value in your pricing strategy: lifetime value.
Lifetime value is one of four perspectives on pricing strategy. The others are:
- Product life cycle
Customer lifetime value (CLV or CLTV, see box underneath for method of calculation) is a customer value model for determining the value of a customer, based on the profit contribution that customer makes over the total period they are customers. Ideally, CLV is calculated at the level of individual customers, but when that is too complex, much insight can already be gained through analysis at the level of customer groups.
CLV helps identify the most attractive customer (groups) and better target marketing and sales efforts. Consider procurement of advertising.
The customer value is also input for the pricing strategy. For example by making concrete when discounts can be given, namely only to customers with whom you will earn back the acquisition costs. Or to determine in which cases it pays to – if necessary! – go deeper in discounting, namely to customers with the highest CLV.
CLV action plan: three steps
These are the three steps for those who want to get started with CLV:
- Develop customer segmentation based on understanding customer lifetime value
- Build model to predict CLV based on insight into customer behavior
- Integrate CLV into marketing and pricing strategy.
STEP 1 Create insight into CLV of current customer base
Start by visualizing the lifetime value of current customers. Start with sales figures, then refine based on product mix, margins and cost to serve.
Be careful with averages! An example from our consulting practice: We analysed new customer sales over the first 5 years. On average, sales from year 1 doubled during years 2 through 5. But a closer analysis showed that a large group of customers (>>25%) hardly generated any additional sales in years 2 through 5. However, the top 20% customers more than tripled sales from year 1. The key question: what are the characteristics of these groups and what explains the variation in CLTV?
We offer this analysis to every company with a sales force. Because we see with regularity, especially in B2B, that salespeople give high discounts to customers because of the customer’s supposed future potential … but no one checks whether that potential ever comes true.
STEP 2 Build model to predict CLV
In order to apply CLV, it is necessary to translate past and present insights (step1) into the future. This requires understanding the drivers of variation in CLV. Those who understand that can build a model that predicts what a customer’s CLV will be. That is necessary to make the concept of lifetime value actionable.
Companies that have large customer bases and data sets can use artifical intelligence to build a CLV-forecast model. But again, if you want to get started with it, start with the most obvious characteristics of the different customer segments based on CLV and then start refining. Example: we found for one of our clients that business customers were 3 times more profitable over their lifetime than residential customers. Within the B2B segment, we were able to further refine the prediction of CLV based on variables such as:
– expected growth rate of the customer organization (explanation: a customer who is growing rapidly is likely to buy more than a customer who is not growing);
– cultural fit (explanation: it was found that companies with a cultural fit stayed customers longer and purchased more)
– cost of serve (background: customers who had their own technical service came with fewer questions and thus had lower service costs).
Similar mechanisms can, of course, be found in the B2C market as well. Consider the customer who purchases his insurance online, or who calls to do so and wants to be helped by an employee. It seems likely that the difference in behavior during the buying process also says something about the customer’s preferences in the service process, and thus about the cost of service. After all, past behavior does not say everything but it does say something about behavior in the future. This is what the concept of “claim-free years” is based on (those who make claims are more likely to make claims again).
STEP 3 Integrate CLV into marketing and pricing strategy
Last step is to integrate the CLV forecasting model into marketing and pricing strategy. The applications are numerous. Well-known example: insurers differentiate their premium based on CLV. Drivers include: age, zip code, claim history (think claim-free years), behavior (smoking).
Some other examples:
Offer scarce products selectively: An e-warehouse preferably serves out the last available products to customers who bought on installment (because they make a much higher profit contribution). Marketing automation based on CLV.
Price differentiation based on prediction return rate: Zalando and peers are struggling with return rates of up to 50%. Extremely costly. Several companies are currently working on predicting return rates. A link to price is obvious.
Marketing in specific zip code areas: An energy company managed to use smart algorithms to predict which customers would stay longer once they became a customer, so they knew exactly where to go by the doors.
Price differentiation based on channel usage: Clothing store offers part of its assortment and deals only online.
Acquisition discount based on probability of repeat purchases: A company in the events industry provides deep discounts only when the CLTV forecasting model indicates a high score, with the prediction of repeat purchases being a particularly strong determinant.
CLV and the royal house
CLTV is a particularly rich concept with many possible applications. We certainly haven’t touched on all of them yet. For example, in this article we have not yet mentioned anything about loyalty programs, which can play an important role in increasing customer loyalty, prolonging the customer relationship and increasing sales. Nor have we said anything about the possibilities of defining customer value more broadly, such as by including the customer’s social status. After all, recommendations are worth money (that’s what the whole idea of the Net Promoter Score is based on). As a company, you might be willing to go the extra mile to bring in an influencer with a large network. It is not for nothing that some car brands go to great lengths to see the royal family driving their cars. For those who do not have that “problem,” the message is: start simple with the three steps we described above.
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