In other words, the existing best practice was to find great athletes with good batting averages and an ability to drive in runs. Oakland identified a different set of metrics, and then found players who were good at generating these metrics at a low cost. Oakland won a ton of games from 1999 - 2006, using this methodology.
In Online Marketing, we look to optimize conversion rate, and we have the best set of tools we've ever had to do this style of optimization.
But we're not making big strides in understanding how to increase customer spend over time. In other words, we work really hard to increase conversion rates, maybe from 4.1% to 4.5%. But we somehow aren't able to engage customers in a way that increases loyalty. E-commerce sales have largely grown from traffic, not from increases in repurchase rates, orders per buyer, items per order, or price per item. In the future, growth must come from increases in repurchase rate, orders per buyer, items per order, and price per item.
So if you are an online marketer, I'm going to encourage you to evolve your thinking. I'm not going to ask you to abandon all of the metrics and optimization strategies you've historically employed. I am going to ask you to think differently.
Let's start by defining a metric. The name of the metric is "Repurchase Rate". Simply put, this metric is defined as the percentage of customers who purchased last year, and then purchase again this year. Now if your business is not an e-commerce business, then go ahead an think about whatever the "action" is that you want to maximize --- if you are Twitter, you might look at a "Re-Use Rate", how many people use your service again today, given that they used your service yesterday.
Why is "Repurchase Rate" important? Let's look at two customers. Both customers purchased one time during 2007, on December 10, 2007:
- Customer #1: Visit 2/1/2008, Visit 2/8/2008, Visit and Buy 2/12/2008, Visit 7/1/2008, Visit and Buy 7/2/2008, Visit 9/10/2008, Visit 10/1/2008, Visit 12/1/2008.
- Customer #2: Visit and Buy 2/15/2008, Visit and Buy 7/2/2008, Visit 12/1/2008.
Both of these customers have a 100% "Repurchase Rate", and both customers ordered two times during 2008. Both customers last visited the website on 12/1/2008. In many ways, both customers yield the same outcome --- both customers purchased twice during 2008.
But from a "Conversion Rate" standpoint, these customers are very different. Customer #1 has a much lower conversion rate than does Customer #2. Our web analytics tools are often configured to favor Customer #2.
When we favor Customer #2, we favor the actions that cause Customer #2 to come to our website. And as a result, we will spend more money, via optimization, on the actions that generate a lot of customers who look like Customer #2.
So my thesis is this: Why not look for the actions that generate customers who have good Repurchase Rates? By optimizing "Repurchase Rate", a metric measured across a multi-month or multi-year period of time, we find customers who may look bad when measured via "Conversion Rate", but are equally or more valuable to the long-term health of the business.
In other words, there is a market inefficiency that exists when everybody focuses on "Conversion Rate". By instead focusing on "Repurchase Rate", we identify customers who appear to be poor converters, but spend the same amount in the future as do other customer who convert well. The secret is that we can grow our business faster than our competition, because we are optimizing on a different set of measures.
Next week, we'll begin to explore the math that allows us to optimize via "Repurchase Rate". The math will lead us to a simulation environment that helps us understand the long-term impact of short-term decisions.