Online marketing: Calculating customer lifetime value
Via survival models
In our day-to-day life, we pay close attention to sustainability and pursue long-term goals without even really thinking about it. In the world of online marketing, however, the success of customer acquisition activities is often measured by the initial purchase. It doesn’t get much shorter-term than this. Frequently, the goods returned by customers are not even included in this analysis. This becomes especially problematic when such a limited analysis provides the basis for customer acquisition activities – a practice that often results in a focus on unprofitable customers or on channels that deliver such customers. In the worst case, this approach might yield a dubious affiliate partner that aggressively offers coupons and delivers customers who purchase high-priced products at slim margins in several different sizes and variations and who then return the goods and never buy anything again. A channel, however, that is actually more lucrative – because it delivers serious customers who shop less often but make targeted and repeat purchases – receives less budget.
The solution involves filtering out unprofitable customers using a customer lifetime value (CLV) analysis. Customer lifetime value measures how valuable a customer is to a company over the entire lifetime of the relationship. This takes into account not only customers’ current purchases but also their entire purchase history as well as future purchases. The word “lifetime” is somewhat misleading because this method takes a pragmatic approach that need not span the entire life of customers. To correct the imbalance and focus acquisition efforts on the “right” customers, it is often enough to expand the analysis to include not just initial purchases but all purchases made over a period of several weeks or months. Ironically, many companies shy away from using the CLV analysis because they consider the concept to be too academic. Yet the CLV analysis is a rock-solid method for identifying the best customers, as long as it is implemented pragmatically.
The calculation of customer lifetime value requires, at a minimum, data on customers’ purchase histories including prices and profit margins of the purchased products as well as information on discounts and returns. The scope of the analysis can be adjusted considerably by integrating additional specific customer data, such as tracking data, CRM data (incl. support cases), data about product/service usage, etc. It is recommended to start with a simple solution and then gradually upgrade it as needed.
Although there are general rules and best practices for customer lifetime value analysis, the existing models are as different as our clients’ business models. A subscription model (e.g., fixed-term contracts for mobile phone services) requires a different implementation than a transaction-oriented online shop. We evaluate the various approaches in order to find the optimal solution – either a single model or a combination of several models. Our goal is to devise a reliable model that can forecast profit margins early on in the customer relationship. If additional customer information is added (data on subsequent purchases, website visits, support contacts, usage, etc.), the model updates the forecast on a continuous basis.
The CLV analysis is a metric that enhances marketing efficiency by enabling you to swiftly identify profitable and unprofitable customers and concentrate your acquisition efforts on interesting customer groups with high CLVs. Our CLV solution is automated and updates itself regularly to take into account the latest information. In addition to short-term savings, you will also benefit in the long run from more repeat purchases. The possibilities of this analytical tool are still far from being exhausted. Customer lifetime value also offers numerous opportunities for use in CRM and also helps you employ customer retention measures in a targeted manner – or warns you when there is a high risk of losing valuable customers.