How To Better Understand Your Users

**David Lieb** (0:09)
One of the biggest mistakes I see founders make is relying on aggregate user metrics instead of understanding how any individual users use their product. In my last video, I talked about cohort retention curves and how you can use those to separate groups of users and track what they do over time throughout using your product. And I think that's the best tool that you've got to figure out if people keep using your product. But what you don't know is how are they using your product? How are they interacting? What features are they using? What's the frequency of use? What's the pacing of how they use the product? And most founders just like ignore this. But I think it's the most important signal to figure out if you've built something that people want. So you want to be able to look at what individual users are doing. But that's a lot, right? If you even have like 10 or 20 users, it's pretty challenging to just tail the logs and watch every event that every user is doing. So with aggregate data, the graphs that we're all used to talking about, things like DAUs or MAUs, these lump all of your users together, and you can't really get a sense of what any individual user is doing. And if you have any amount of growth, those graphs tend to be going up and to the right, even if users aren't actually enjoying using your product. So today, I want to tell you about a tool that we came to in my startup that allows you to understand what's going on with individual users, while also giving you a big picture view of how your entire product is performing.
And we call it the dot plot. So let me show you what a dot plot looks like. Based on the name, you can figure out it probably involves dots. What you basically do is just make a two-dimensional grid, like a spreadsheet, where there are a bunch of columns and a bunch of rows. Each row represents one individual user. If I'm one of the users, I'll write my name here, Dave. I'm one of the users.
And every other user of your product gets their own row.
And then every column represents a time period. I think days are usually the right thing to use for your product, but it probably depends a bit on the nature of your product. So let's just draw in the days. I'll just do Monday, Tuesday, Wednesday, Thursday, Friday. And you can make this as big or as small as you want. For the sake of this example, I'll just do like a week or two of days, just to show you what's going on here. And then the idea, it's called a dot plot, is you put some dots in each of the cells. You want to pick an event that your user does in the process of using your product that you think represents value in the product. Maybe it's sharing a photo if you're building a photo app, or listening to a song if you're building a music app, or processing an invoice if you're building a B2B invoice processing product. And you can just put a dot for each day that each user uses the product. Let's say we're Spotify and we're building a music streaming app, and we want to see how our users are using it. Let's pick the event that we're going to chart here being listen to a song. So anytime a user listens to a song during a day, we're going to put a dot.
So for me, let's say I listen to Spotify song on Monday and Tuesday, and not on Wednesday, but Thursday and Friday again, and then maybe again on Monday and Wednesday.
Another thing you can do to make a record of the first day that a user used the product, the day that they onboarded, you can put another symbol, like let's say on a user's first day, we'll just draw a little ring around the dot like that, just to give us a little bit more signal. And what you'll eventually start seeing is a pretty high density visualization of individual users and their usage over time. What's really cool about this is it lets you figure out patterns that you probably would not have seen with your human brain just looking at aggregate charts or looking at individual user logs. Okay, so let's look at this example I've just drawn.
For our Spotify app, what do we see? What patterns have emerged now that we can see individual users and their own behavior? Well, one thing I see is it seems like there's a set of people who use the product on weekdays, right? We've got myself, we've got user number three here, user four used it on a Monday, user six used it during the week. And there's a couple of users who seem to kind of only use it on the weekends. That's an interesting observation that might help me redesign my product in a different way, or target different users, maybe understand which user is the most valuable ones to me. Do I want the weekday work time listeners, or do I want the weekend users? We would have no idea about this if we didn't have a dot plot visualization like this. Another thing I can see is a measure of retention. Like do we see a lot of users like user four that try the app on one day and then never come back? If we see that on a bunch of our rows, we have an idea of a potential problem that we've got in our onboarding or other things. As you get more sophisticated with dot plots, you can make them as intricate as you want. At Bump, we had different symbols that we would put into these cells. So we knew whether you shared your contact information using Bump, or if you shared a photo, and it gives you a lot more granularity and you can go as deep as you want on this. This idea of dot plots might be familiar to some of you. You've probably seen it at the top of GitHub pages.