At the Fluxible UX conference last month, UX designer Steve Baty of Meld Studio gave a talk on innovation and sources of insight for customer research. The way he framed customer groups resonated with me as a marketer.
Normally, we think of people as being prospects or customers. In other words, once a person has crossed the point of purchase, they are a customer. From there many companies assume you have less work to do than when you were trying to get them to buy in the first place.
We all know that this isn't true, especially for customers of low-price-low-touch products, where you can cancel a subscription with the click of a button. Yet marketers tend to focus 90% of their efforts on getting new customers and 10% of their efforts on keeping them.
Steve's model for customer segments is closer to reality; I've sketched it below:
The first thing I like about this model is that it calls out that the customer may not in fact be the buyer or the user. This is often the case in the B2B space where there are multiple people involved in a buying decision, some who will never actually use the product. Think CRM, project management, marketing automation, etc...
The next thing I like is that it distinguishes customers from a group he calls near-customers. Near customers are people who have purchased your product, but aren't committed customers. These are people who may have chosen your product because the cost of trying it was low, or because it was the best option out there but not really what they were looking for. These people may technically be customers, but they are still looking for other options and are highly prone to switch to something else when they find it.
So how do you find the near-customers?
I'm doing this for a client right now and we are starting by interviewing people who have recently cancelled their subscription. We are using the Jobs to Be Done framework to understand why they are leaving, what they are switching to and what triggered their action. One of the things we learned was that there was a group of people who were leaving because they weren't in the habit of using the product. They were curious enough to try it at one point but they hadn't integrated it into their day to day life, largely because they were unaware of a feature that would make it easy to use this product while on the move and not just in their homes. When an opportunity came to tighten their budget or find something new to entertain them, this product was an easy candidate of something to do without.
In addition to the interviews we are doing some data mining to analyze what patterns we see in people who cancel versus renew, so that we can determine if there are specific fail points that we can flag and correct to hopefully prevent people from wanting to leave. For example, if someone only logs in through the website and not our app, or if they search for a product and can't find it and this happens more than twice in a 30 day period.
Putting both of these types of insights together, we can now start running tests to see what effect they have on customers' experience with the product, and whether our changes reduce the number of people leaving (churn).
I'll report back on how we did in a future post.