In our last two minute short, we talked about how you can orient your Customer Success efforts around two key customer-focused objectives:
- More logins (boosting daily activity)
- More revenue
I mentioned that there were a few edge-case exceptions. Well, a few of you wrote in to ask what those edge cases are, so thought I’d cover them today.
So, in what cases should you be concerned about higher rates of user activity? We’ve come across four edge cases. Of course, the nature of edge cases is that there are always more of them… way out on the long tail. So our list isn’t exhaustive, but it’s pretty good:
Edge case #1:
User is very busy extracting data from your tool so that they can quit. We see this case most commonly with online tools that have a reporting or archiving feature. Here, the user is preparing to pull out of the tool, getting everything they can get out before they quit or stop using it.
At first glance, this user looks like a great customer. They’re very very busy, so the tendency is to think: great, this user is getting a lot of value. But if you look at their usage pattern, you’ll find that they’re too busy. Their level of activity is way above their average over the past few months. This is a case where an early-warning system is a really good idea. You want to catch this high risk user before they start to extract and leave.
Edge case #2:
User is very confused – and clicking around a lot in frustration. We see this case where the product has a compicated user interface. Typically this kind of user won’t stick around for long though. They’ll have trouble getting to value and will quit pretty fast.
Edge case #3:
User is looking at your pricing or account management page. This is a very common pattern in advance of a quit. They’re checking out the pricing options and wondering why they’re paying so much for something they aren’t liking much.
Edge case #4:
Your product doesn’t lend itself to daily usage. There are certain products that just don’t lend themselves to a daily, weekly, or even monthly usage pattern. These are apps with high seasonality or peak usage patterns around certain times, like birthdays or holidays. For these apps, daily usage would be a peculiar pattern.
And there you have it, four edge case exceptions – when driving daily usage doesn’t make sense or isn’t a good thing.