AOV and Lifetime Value
Kevin Hillstrom wrote a great piece on the impact of purchase price on lifetime value, and I thought I’d pick up on that thread.
Direct marketers know that most sophisticated “scoring” systems for ranking the value of customers are based on “RFM” modeling. Studying data carefully for the last, oh 50 years, has taught the direct mailers that likelihood to purchase is best predicted by three variables:
- Recency: The more recent the customer’s last purchase the more likely they are to purchase again. This is a bit counter-intuitive at first blush; one might naturally assume that someone who just made a purchase is less likely to return than someone who hasn’t bought in a while, but the data says otherwise. The underlying reason likely relates to some combination of the following:
- the recent customer feels relatively flush with cash at that time or they wouldn’t have made the last purchase
- the recent customer just had a good experience with your brand (hopefully) so you are top of mind with them in that category at the moment; and
- the recent customer is less likely to have loss a job, their life, or found a competitor of yours that they prefer.
Whatever the case, recency is the strongest factor in predictive modeling.
- Frequency: the number of times the customer bought from you in the past. The more frequent, the more valuable.
- Money spent: how much did they spend on their most recent purchase? Customers who spend little on the first purchase tend to always spend less and vice versa. The big spenders are the most valuable.
Other factors play a role, but those are the big three.
While “Money spent” is the least predictive of the three, it is still quite meaningful and it gives us an interesting angle in paid search bidding.
When setting efficiency targets many smart marketers factor in some notion of lifetime value. That is, the value of the traffic isn’t simply the margin on the orders placed through a given keyword or cluster of keywords, some credit should be given for the value of future purchases from that customer as well as the likelihood that they’ll tell a friend about your company. Measuring the actual lifetime value by keyword may be theoretically ideal, but practically impossible.
What is NOT impossible is adjusting the lifetime value component of an efficiency target based on the average order value associated with a given keyword or cluster. Higher order values (by margin dollars) are more valuable in and of themselves, and a good bid management system will already take that into account, but it is also the case that those customers are likely to have a higher lifetime value — and wealthier friends!
How much of a factor is this will depend on the business. Likely if Pat’s order is twice the value of Taylor’s she is not twice as valuable as a customer, but perhaps a square-root of two factor may be a good rule of thumb.
This is not a game changing strategy, obviously, but when the core blocking and tackling is well under control, these are the types of tuning mechanisms that allow us to raise the bar.