Behavior over time graphs and ways to influence

I was geeking out over behavior-over-time graphs (BOTG) this week and found this neat connection to bumpers, boosts, and tilts. My colleague Donald Martin first introduced me to BOTG and they fit right in, given my fondness for silly graphs.

The idea behind BOTG is simple: take some measure of value and draw a graph of its imagined behavior over time. Does the line go up? Does it go down? Does it tepidly mingle at about the same level? To make it even more useful, we can use BOTG for predictions: draw the “today” vertical line on the graph, splitting the space into “past” and “future.” Now, we can use it to convey our sense of how things were going before, and predict what happens next.

Now, let’s add another twist to this story: the goals. Usually, if we care about a value, we have some notion of a goal in relation to this value. Let’s draw this goal as a horizontal line at the appropriate level. If our goal is reaching a certain number of daily active users, and capture this notion by drawing our BOTG squiggly in a way that crosses the goal line in the “future” part of the graph.

It turns out that by considering how we’ve drawn this intersection of goal and BOTG lines, we can determine the type of the influence that might be effective to make our prediction come true. 

If our BOTG curve needs to touch and stick to — or asymptotically approach — the goal line, we are probably talking about a bumper. There is some force that needs to keep that curve at a certain level, and that’s what bumpers are for. For instance, if I want to keep the size of the binary of my app at a certain level, I will likely need to employ team processes that enforce some policy about only landing changes that keep us under that magic number.

If we picture the curve as temporarily crossing the goal line, we are probably looking at a boost. This is literally the “we just need to get it across the line” case. A good example here is the intense march toward a release that many software engineering teams experience. There are some criteria that are determined as requirements for shipping, and, spurred by the boost, the team works their hearts out trying to meet them. A common effect of a boost is the slide back across the line after the team ships, relaxing and resting ahead of another round of shipping.

Last but not least, the curve that permanently crosses the goal line and never looks back is likely a marker of a tilt. Here, the goal line is just a checkpoint. Did we reach N daily active users? Great. Now let’s go for N x 2. When such ambition is part of the prediction, we are likely looking for some constant source of compounding energy. A good question to ask here is — where will it come from?

One of the common mistakes that I’ve seen leads make is confusing the outcomes of boosts with those of tilts. Both offer gains. Boosts feel faster and provide that satisfying thrill of accomplishment, but they are at best temporary. Tilts are slower, but their advances are more lasting.  So when leaders employ a boost and expect the curve to just stay over the goal line, they are in for an unpleasant surprise. Early in my tenure at the Chrome team, I organized a task force to reduce the number of failing tests (shout out to my fellow LTTF-ers!), a small scrappy band of engineers dedicated to fixing failing tests. At one time, I reported that we brought the number of failures down from a couple of thousands to just 300! Trust me, that was an amazing feat. I am still in awe of us being able to get there. Unfortunately, my strategy — organizing a task force — was that of a boost. The spoils of the hard-won victory lasted a week after the task force disbanded. For a sobering reference, that list of failing tests is currently clocking at 7623 lines.

See if the BOTG with goals can help you puzzle out what might be the strategy for that difficult next endeavor you’re facing. Use them to clearly capture your predictions – and perhaps glimpse which method of influence might be needed to make them a reality.