Many Ecommerce challenges come down to two things: knowing who your customers truly are and how to efficiently acquire new ones. Most companies have a mix of customers from the extremely loyal to one-time buyers. Yet they struggle to understand their customers on an individual level, and are unable to use more targeted advertising.
One model that has proven its efficiency with some of our Ecommerce clients is lifetime value (LTV). To illustrate this model, we’ll take a look at one of our clients, an Ecommerce retailer for planners, stationery, and other accessories. Their varied mix of customers, from loyal devotees to one-time buyers, requires advanced targeting techniques, but for this reason they are also an ideal candidate for LTV modeling.
Another approach is to try to add rules of thumb like, “if a customer hasn’t
purchased in the last 12 months, that person is considered churned.” While these
types of rules are an improvement, they are not ideal since they don’t consider
the statistical properties of customer transaction patterns. To address this
issue, researchers develop statistical models to describe the transaction
patterns of customers.
The model that has proven to be most efficient in our work is the customer lifetime value (LTV) model.
Expected # of Transactions
Probability of Being Active
These groups were built based on the predicted spend for the next 12 months. Customers were
placed into 3 different groups: high value, good value, and less valuable.
As the names suggest, the high value group contains the customers with the highest predicted spend, which was also the smallest group in number.
The less valuable group contains the customers with the lowest predicted spend. Customers in this group are predicted to have churned already, and this was the largest group in terms of number of customers.
Finally, the good value group contains the customer that have predicted spend lower than the top performers, but unlike the less valuable group they are predicted to spend money in the next 12 months.
At this point, we have 3 distinct client lists to share with the paid media teams. They can match the customer IDs with customer emails and created audiences in AdWords and Facebook. Here are just a few of the strategies the media teams can implement using our outcome from the LTV model:
High Value Group: create lookalike audiences based on this list, and use these lookalike
audiences for prospecting campaigns. Our main goal is to find new customers
that look similar to the current best customers.
Good Value Group: use this list for remarketing campaigns. We can use tailored campaigns to influence these customers to spend more. We want to move them to the top tier and avoid them moving to the bottom tier.
Less Valuable Group: remove them from campaigns’ targeting or reduce the bids. We want to save marketing dollars on customers that are predicted not to spend in the future.