Up until now, our predictive modeling at Custora has focused on understanding the behavior of paying customers. We’ve traditionally analyzed customer purchase patterns over time – and helped clients answer questions like, which channels, ad networks, or affiliates should I be looking towards to attract more customers like my highest-value shoppers?
However, lots of companies don’t directly acquire new customers – they acquire new members and then convert them over time into paying customers. This business model, often called “free-to-paid,” has become an increasingly common fixture in the e-commerce landscape. The model is now the norm in the daily deal and flash sale industries, where companies tend to sign up lots of unpaid subscribers, hoping to convince them to shell out for goods or services at some later point.
For these companies, homing in on customer lifetime value is obviously still important. But free-to-paid firms tend to spend acquisition dollars on getting new members – who may or may not go on to become customers. The only way for them to maximize their return on acquisition is to be able to predict how much a member will be worth over time, even when her first purchase may be far down the road.
With this challenge in mind, we recently introduced a new feature in Custora called Member Lifetime Value (or MLV). Now our clients operating a free-to-paid business model can see the predicted value of a new member. And they can further break down expected member value on acquisition factors like channel or demographic variables like geography.
This is a big win for clients like LivingSocial. But we’re sharing this to highlight some of the interesting modeling questions that come with changing the frame of reference from customer to member. Predicting member CLV requires the joining together of two separate models – a conversion model, and then a customer behavior model conditional upon conversion. So what are some of the main hurdles?
1) Conversion is all about timing. Across free-to-paid businesses, a certain proportion of members (usually between 5 and 15%) generally make a purchase immediately upon signup. Retailers shouldn’t ignore these customers – they tend to be particularly valuable, and may justify special attention to keep them coming back. But what about the vast majority of members who don’t convert right away?
Consider two identical members who both sign up at time t=0. Once converting to a paying customer, each member will go on to make regular purchases every four months, with each purchase netting $50 in profit. If member A converts at the 8-month mark, his expected two-year profit (starting at time t=0) is $250. But if member B converts at the one-year mark, his expected two-year profit (still starting at time t=0) is only $200.
In other words, conversion isn’t just a binary, “yes/no” variable with a single probability estimate. In order to predict a member’s long-term value, we need to model the entire distribution of possible times when they’ll convert.
2) Members, like customers, are all different. Customers come in all shapes in sizes – some make small purchases once a week, others splurge on big-ticket items once in a while, and many are “one-and-done” shoppers unlikely to return for a second purchase. It’s precisely this diversity in customer behavior that allows us to effectively use segmentation and targeting tools to get more efficient with marketing dollars.
The same is true of members. Some types of members are likely to convert very soon after signup, whereas others may take much longer to convert – if they ever do. Take a look at the following graph of member conversion behavior by acquisition keyword campaign for an actual free-to-paid company:
For this company, members acquired through Keyword 3 were much more “conversion-prone” than those acquired through the Keyword 2 – by the end of one year, almost twice as many of them converted into paying customers. A robust model of conversion needs to factor in this underlying heterogeneity of conversion propensities across member segments.
3) Factoring in covariates. How do we predict at the moment we acquire a specific member how likely he or she is to convert into a paying customer – and when? We use covariates: the secondary data that a member record is “tagged” with at the time of registration. Variables like what channel or referral site a member came from can provide important clues about her underlying, unobserved likelihood of conversion.
4) Putting the pieces together. The conversion model is an essential component of understanding member value – but it’s only half the story. The other half is what the members actually do once they convert into paying customers. For example, a company might discover that members who sign up through a certain affiliate tend to be quick to convert – but then go on to make infrequent, low-value purchases over time. Both of these pieces need to come together to inform a prediction of the long-term value of members sourced from that affiliate.
Ultimately, introducing MLV is a step towards helping marketers at free-to-paid firms make smarter acquisition decisions. Any questions or thoughts on how we tackle MLV? We’d love to hear from you!