You work in a small company that has a program to help consumers manage their health. Your basic product involves a mobile app for tracking daily events and a personalized dashboard. For a monthly subscription users can also get access to coaching and other resources.
There’s a meeting with a potential investor on the calendar and you want to use data to support your story that things are going well. So, you open up Excel and start digging through the data you have.
Finding the Story
You got some nice local news coverage back in March and you signed your first partnership in June, both of which resulted in a spike of app downloads. So, you look at that.
Well, that’s something, but it doesn’t really communicate the excitement of the last few months. You remember that a lot of those downloads in the spring never turned into even free accounts. So, you decide to look at new accounts instead of downloads.
That looks more like what you were expecting. Whereas the app downloads spiked in March, the new accounts hit a peak in July. Comparing the two graphs, you become curious as to how many new accounts were linked to the news coverage and the partnership, so you draw another graph.
This view makes it clear that by the time the July peak hit, the effect of the news story had died. The big spike in July was just the partnership. You kind of knew this, but it’s the first time you’ve seen a picture of it, which is pretty cool.
You remember that your company has a 20% download-to-account conversion target, and you want to see how many of these months hit that. This seems like a good situation for a scatter plot:
Wow. Comparing against the diagonal line that represents the 20% target, you can see July and August blew it away, while March and April didn’t even come close.
You note another promising detail on the spreadsheet. Not only are accounts up, but the percentage of accounts that are paid subscriptions is rising as well. This is good for revenue, which investors obviously care about.
You wonder how many of the paid accounts come from the new partnership, so you look at that.
Clearly, the partnership has been a great thing for your company. Armed with these insights you put together a nice summary in dashboard form for your investor. You add a few other interesting tidbits (you know from your market researcher that about two-thirds of your paid account holders are women) to make it visually interesting.
When you walk a few of your colleagues through it you get some nice comments—this is the first time some of them have seen all this information together like this—but when you present it the following day, your potential investor squints at the wall and tries to figure out what’s going on
Visualize Situations, Not Data
When you start by looking at the data you have and concentrate on how to draw a picture of it, it’s easy to lose track of the message. Overwhelming your audience with data is an easy trap to fall into. The person crafting a dashboard (or an article, or a presentation, or a web page) knows the content backwards and forwards and can unconsciously assume that the audience is on the same page.
A graph is a picture of a situation. The trick to creating a good one is to start by identifying a situation that your audience cares about. In some cases, you may know. Your investors probably care more about revenue (and projected growth) than they do about specific conversion rates.
This graph describes a situation that investors will understand: Revenue is going up due to a partnership, and more partnerships and more revenue are on the way.
Often you won’t know what situations your audience cares about, even when you think you do. A clinician who is monitoring a heart failure population may not need to know about her patient’s every movement but does care if he has become less active over the past few days. A credit card customer looks at a breakdown of his purchases out of idle curiosity, but what he really wants to know is how he can maximize the frequent flyer miles he earns by using his card. A patient doesn’t understand what her deductible is, but she does want to know which insurance plan is going to cost her less over the coming year.
It’s not fair to throw data at people and expect them to decode it. Just as with any design, effective data visualization requires you to understand the situations that are significant to your audience. By starting there, you can use data to describe something they will care about.