At their best, graphics are instruments for reasoning about quantitative information. Often the most effective way to describe, explore, and summarize a set of numbers — even a very large set — is to look at pictures of those numbers. Furthermore, of all methods for analyzing and communicating statistical information, well-designed data graphics are usually the simplest and at the same time the most powerful.
The key qualifier in the above passage is well-designed. Most visualizations try to do too much and in doing so obfuscate the true meaning of the data, thus defeating their purpose. Tufte introduced a number of concepts to explain this, among them the data-ink ratio (how much of a visualization is data itself, versus ornamentation) and the lie factor (to what degree does a visualization skew interpretation of the data). He even coined a term, chartjunk, to refer to the worst parts of poorly-designed visualizations.
Visualizations abound in the world of SaaS. Most of them are terrible, which is a shame. Luckily, it isn’t all bad out there. Here are some of the SaaS visualizations that I find most compelling.
In a future post I’ll discuss the danger of focusing only on one or a small number of metrics to gauge the health of a SaaS business; suffice it to say for the purpose of this post that doing so is horribly flawed. With that caveat in mind, if I were forced to choose only one metric with which to assess a SaaS business I’d choose the quick ratio.
Introduced by Mamoon Hamid at SaaStr Annual 2015, the quick ratio measures a company’s growth in monthly recurring revenue (MRR) by dividing additions to the subscription base (new logo and expansion) by losses from the subscription base (contraction and cancellation). Watching the video of Hamid’s presentation is 17 minutes well spent. The standard visualization is the following stacked bar chart:
This does a good job of showing changes in MRR. One can see that more of the business’s growth is coming from the addition of new customers than from expansion of existing customers. And, customers shrinking over time is a larger problem than customers cancelling outright.
However, it doesn’t show the quick ratio itself! The reader needs to do mental gymnastics to calculate it and see how it’s changing. Direction is just as important as magnitude. And what about comparing one period to another? Most people aren’t good at spatial analysis. Would you believe that growth in January was two times as strong as in August?
Here’s my take:
This draws the reader’s attention to the quick ratio, which makes understanding magnitude, direction, and relativity easy. The drivers are de-emphasized but visible, supporting further analysis if desired.
Actual vs. Plan
Tufte favors high-density visualizations, i.e., ones that pack a lot of data into the allotted space. A corollary to striving for high data density is, “Don’t use a visualization when a sentence will do.” For example, you don’t need a chart with two bars, one showing planned retention and another showing progress to date, when you could instead write, “We’re one third of the way to our retention target for the current quarter.”
The calculus changes when you want to communicate more information about how you’re tracking against plan. A few years ago I created the following visualization for gross revenue retention (i.e., excluding expansion) and have included it in almost every Smartling board deck since:
You immediately see where you are (32%) against plan (92%). You also see that the outlook for achieving plan is pretty good: most of the remaining dollars are from customers in good health (green), whereas only a small percentage are from customers in average (yellow) or poor (red) health. Finally, you know that a small amount of revenue has been lost (the white space at the top of the right stacked bar).
The visualization works equally well using forecast category (commit, best case, pipeline) in lieu of customer health, which means it can also be used to show a sales team’s progress against plan. For this use case you’d modify the visualization to use absolute numbers instead of percentages.
Performance Against Quota
Tom Tunguz made widely known this visualization of a sales team’s performance against quota. His post on the topic does an excellent job of explaining all its benefits, so I won’t repeat them here. I will, however, offer two modest suggestions for improving the visualization:
Indicating inside versus field (or North America versus EMEA, or any other segmentation) distracts from the visualization. One could argue that it’s useful additional information, but if you want to illustrate the differences in performance across various segments, it would be more effective to re-create the visualization and have each row represent a segment rather than an individual contributor.
In addition, I don’t merge columns to illustrate a ramp because it confuses matters if the end of the ramp doesn’t coincide with the end of a quarter, or if reps have differing ramp durations, both of which are frequently the case. Because each column shows the rep’s performance against accrued quota in that period, the ramp is reflected automatically.
Achieving the right balance of talking and listening is one of the keys to having productive conversations. If you don’t have a conversation cloud, you almost certainly lack the data necessary to understand whether your reps are talking too much, too little, or just the right amount. Chorus provides an excellent visualization of our team’s talk/listen ratio:
(Unlike other examples in this post, this visualization uses real data, so I’ve shown only a partial view and have obfuscated names.)
Each dot represents a single conversation that’s been captured and analyzed by the software. The dot is placed along the horizontal axis according to the percentage of time that the rep spent talking. Research shows that 40% to 60% talk time is best (“on target”), so that area of the horizontal axis is shaded. Reps are then shown in descending order of the percentage of their conversations that were on target.
In addition to on-target rankings (which you could get from a simple table or bar chart), this visualization gives you:
- Density: How many conversations did each rep have during the period?
- Spread: Is each rep’s performance consistent, or is there significant variability from conversation to conversation?
- Direction: On-target percentage doesn’t tell you what behavioral change is needed, e.g., talk less or talk more; this visualization does.
The visualization would be even more powerful if the horizontal axis labels were replaced with “All Listen” and “All Talk” at the extremes — does one really need to know each conversation’s exact percentage? — but that’s a small gripe about an otherwise flawless visualization.
Visualizations play a critical role in helping people to better understand data. If you’re looking to up your visualization game, I hope this post is helpful, and strongly recommend that you read Tufte’s work.
Thanks to Rebecca Silverstein for inspiring me to write this post.
Sign up to receive new posts by email.