In Data You Should Trust
Not too long ago, I became obsessed with Comedians in Cars Getting Coffee. If you haven’t seen it, you should stop reading this and get…
Not too long ago, I became obsessed with Comedians in Cars Getting Coffee. If you haven’t seen it, you should stop reading this and get thee to Crackle. Or just click on this. It’s Seinfeld continuing to pursue his dream of making shows about nothing. He literally picks up a comedian in an amazing car and they go grab coffee. So whatever he’d normally be doing, just on TV. Genius.
One of the episodes I’d encourage you to watch is the one with Aziz Ansari. Star of Parks and Rec, he also has some hilarious stand-up on Netflix. At one point in the episode, they are at a diner eating and, you guessed it, drinking coffee.
One particular part of their conversation at the 6:39 mark goes like this:
Jerry: I wanna stop lying at restaurants about how the food is to the waiter. You know how they always ask “how is everything?” And we say “great!” And then the place closes. And they say “I wonder why it closed? Was the food bad? Nobody said anything…”
And right after the above at the 9:32 mark, this happens:
Waitress: How is everything?
Jerry and Aziz: Good!
Waitress: Alright, good.
Aziz: (after the waitress leaves) Everything really good?
Jerry: Oh, I forgot to think, what’s the truth. So if I was really honest I would say that the blueberries are weird, there’s too much of some kind of liquid in the oatmeal, but a really good-sized coffee cup.
Aziz: And would it be worth it to bring up that stuff?
Jerry: (pauses…) No.
What people say and what they actually mean is extremely hard to decode. Oftentimes, the real problem doesn’t come to mind. Or they’re not truthfully representing their behavior. They feel like they are on stage, so they say what they need to for you to feel better. Assuming that you’re a wonderful human being that they want to be nice to. If you’re not, maybe ultimately you have the advantage.
The other problem with direct feedback is that, oftentimes, it feels extremely subjective without context. Take this example piece of feedback:
I love the app, but it doesn’t do everything I need it to.
On the surface, it sounds like a fairly innocuous comment. Oh, this user loves the app, great! But it’s just missing a couple of things that they need. Cool, I can fix that.
“What things are you missing that we can help you with?”
“I mean, it’s okay, but I really wish it did X.”
You hear that feature and put it in your backlog. Maybe you get to it, maybe you won’t. Either way, you’ll live on in the ignorant bliss of assuming that it’s one feature for one user. Your management will ask you about it, and you will say users love your app. They will assume everything is going great, and you’re on your merry way. Until your user numbers dwindle, and everyone wonders why you’re a failure.
If you dig into the numbers early though, they will paint you a very different story. Retention numbers tell you what percentage of users end up using your app after download. When you look at those, your retention percentage is a third of what it should be for apps of your category. Looking at this user in particular, it turns out that two days after they had that conversation with you, they uninstalled your app. Why? Well, looking at their usage patterns, it turns out that 80% of what they use on the desktop version is not supported in your mobile app today. Which means that you failed this user, hundreds of thousands of users like them, and you didn’t even know it. With that, you can chart a course out of the abyss before it’s too late.
Your users are lying to your face. Leverage your data to make your team’s hard work count.
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