Well-designed incentive compensation plans – especially sales commission plans – are an incredibly powerful way to motivate great performance. But designing a great plan is both an art and a science.
Well-designed incentive compensation plans – especially sales commission plans – are an incredibly powerful way to motivate great performance. But designing a great plan is both an art and a science, and prone to design mistakes that are expensive and end up not motivating the desired performance. The commonest and most serious error plan designers make is to lay out the rules before deciding just what it is the enterprise is trying to accomplish. You can avoid that mistake with a simple, straightforward graph that I’ve drawn hundreds of times in my career. Follow these steps:
1. Draw your axes. “Performance” goes on the X-axis (i.e., the horizontal axis) and “Compensation” goes on the Y-axis (i.e., the vertical axis). For now, you don’t have to graph actual dollar amounts… just think in terms of percentage of target amounts:
2. Plot the obvious points. Those would be 0% of target comp at 0% of target performance and 100% of comp at 100% performance. You might also find it useful to draw a reference line through those two points. (That reference line happens to be the graph for a plan with a commission rate that is constant across all levels of performance.)
3. Plot some additional points and connect those points with straight lines or curves. Work with line managers and senior managers to get a sense of how much incentive comp they believe is appropriate at specific levels of performance. For example, here’s a graph for an “accelerated” sales commission plan – that is, one where above-quota performance is rewarded with increasing commission rates, and below-quota performance is penalized, where management has specifically suggested compensation of 40%, 165%, and 250% of target comp for performance at 50%, 150%, and 200% of target, respectively. Note that the compensation curve sits below the reference line for performance below 100% of target, and above the reference line for performance above 100% of target:
4. Plot the same graph, but this time with actual performance levels and comp amounts. Here’s what the graph might look like for the accelerated plan described above, where “Performance” is Sales, with a quota of $2,000,000, and target commission is $100,000:
You’re now ready to flesh out the comp plan with spreadsheets and other formal documentation.
Just for comparison, here’s another example, this time for a typical management MBO plan where no bonus is earned until a specific performance level, such as 60%, is met, and the maximum bonus is the target MBO amount:
In this way, you can visualize any approach to incentive comp, form conclusions about whether that’s the approach you want, and then fill in the blanks to design a comp plan that actually does what you intended.
Sometimes a picture IS worth 1,000 words.
On one point, there is no argument: You will be considered a great FP&A professional only if you can communicate clearly, effectively, and eloquently.
Regardless of what you’re communicating, your audience will form opinions about the content of your information – and about you personally – from what you present, how you present it, and how you behave while you’re presenting. They’ll form opinions about your intelligence, your professionalism, your grasp of the subject matter, your respect for your audience, your work ethic, your integrity, and your honesty. And those opinions are intertwined: the credibility of your content affects your credibility as a professional, and vice versa.
The fact that you’re communicating numbers doesn’t change anything – in fact, you face even more presentation choices than you do when presenting words. The most basic of those choices is whether a use a table or a graph. To make that choice intelligently, it’s critical that you answer each of these four questions, and in the following order:
- Which is the most effective way to impart your key information – a table or a graph?
- What is the best way to present the information graphically?
- What changes and additions to the graph will help your audience understand its central messages quickly and accurately? Will visual effects that “beautify” the graph help or hurt that objective?
- Is there anything about your graph that might cause your audience to question your agenda or even your honesty?
- Graphs can only make one or two points at a time. Graphs take up much more space than a well-laid-out table does to present the same number of data points, and in a less visually coherent way than a table. Don’t try to cram too much information into a single graph. Yes, a picture is worth a thousand words, but two pictures superimposed on each other won’t create something worth two thousand words – it’ll just be a jumbled mess. And dozens of little graphs, in an array that looks like the cockpit of a 747, are just as intimidating and hard to follow as a large table.
- Don’t let audience preferences drive your choice. We’ve all heard statements like, “I’m a visual person – just give me some graphs,” or “I love waterfall charts.” Yes, there are differences in cognitive abilities from one person to another, but those differences aren’t nearly as dramatic as you might think. Make presentation choices based on the most effective way to get your point across, not on what people in your audience think they want.
- There are alternatives to graphs. Graphs aren’t the only impactful way to get your point across. Key indicators – ratios that identify critical drivers, and that add meaning and context to the raw numbers – presented in a table can be just as effective. And “data visualization” can apply to tables as well: use text effects (e.g., boldface, italics, font size) and artwork (e.g., cell borders, changes to row height and column width, and Inserted Shapes) to emphasize the most important data. Or use Conditional Formatting to emphasize particularly large, small, or otherwise noteworthy numbers. And lastly, how about leaving space on your tables for some clarifying words?
The bottom line: If you’re an FP&A professional responsible for producing a regular periodic reporting package – such as monthly, quarterly, or yearly – consider using tables for the basic information that everyone is expecting. Use graphs sparingly, to make your most important points, and only when those points lend themselves to a visual presentation.
Obviously, the most important goal of almost all numbers presentations is to inform your audience. But every good FP&A professional should have another, more selfish goal: to showcase your critical thinking skills. If you keep both goals in mind, achieving each will make the other more likely.
The article was first published in prevero Blog
Let's take a look at some of the most messed-up, incomprehensible recent examples of quantation. Not surprisingly, all are graphs. But some come from sources that definitely should know better. With some, try to figure out what went wrong; with others, if you can figure out what the heck they’re trying to say, please let me know. Enjoy!
How much do you spend? We’ll start with a simple one, from the imgur.com website (beats me where they got it, but click here to see the original):
Do those percentages look like they match the length of the bars? And how about that strange scaling? And is that per day? Per year? What went wrong here?
I really hate to pick on a national institution like the Girl Scouts, but take a look at this graph that appeared in an article on CNN.com , about how the Girl Scouts are going online to sell cookies. We’re talking digital cookie sales here, not digital cookies:
Again, do the percentages match up to the size of the bars? What went wrong here? And how will this affect Girl Scout cookie sales?
The Hebrews do it backwards, which is absolutely frightening! (Google that whole line, if you’re under 50.) An Israeli friend, No’am Newman, sent me this, from a newpaper article about how people use the Internet:
In case you’re wondering, here’s the translation:
- Met partners on the internet – 32%
- Members of family groups in WhatsApp – 70%
- Visit a doctor after reading about their medical problem on internet – 56%
- Watch TV series and films via the internet – 63%
- Listen to music on the net – 80%
Again, do the bar sizes match the percentages? What happened here?
Yes, there’s always good old’ Fox News! No discussion of incompetently presented information would be complete without at least one pie chart. We have Fox News – OK, OK, the local Fox affiliate in Chicago – to thank for this one:
It doesn’t get much more incoherent and meaningless than this. If you want to see the reporter – uh, I mean the teleprompter reader – blithely whip through this one, click here. Again I ask: What went wrong here?
HUH??? Please, please tell me what the heck this graph trying to say, about the critically important subject of what blue-chip basketball players major in in college:
… and this is from Bloomberg BusinessWeek, for goodness sake. Warning: there will be a two-hour exam on this in tomorrow’s class, with both multiple choice and essay questions.
A breath of fresh air from a sitcom! At last – a coherent juxtaposition between bar graphs and pie charts, and surprisingly, we have Jason Segel of “How I Met Your Mother” to thank for it:
I might quibble about the repetitive use of colors, and I am not personally a fan of 3-D graph elements, but all in all I give kudos to the deep thought behind this elegant presentation. (Click here for the YouTube clip of this scene.)
Along these lines, I once again call your attention to the world’s most accurate pie chart. Click here to view it. In at least one sense, it might not be so accurate. Can you spot the problem?
In recent posts we’ve seen how tiny changes in the way we present numbers can have a huge impact on how well the information is understood. In this post, we look instead at how those little things can affect how your integrity or your ethics might be perceived.
For our example, consider a slide from an investor presentation I found online (click here for the original document). Just for the record, the company (Constant Contact – Nasdaq: CTCT) looks at first glance like a perfectly fine company; I’m only commenting on how they present some of their information. Here’s slide #13, showing the trend in Average Revenue Per User (ARPU):
A quick glance at this graph suggests impressive growth – the height of the rightmost bar appears to be nearly triple that of the leftmost bar. But that appearance was created by truncating the vertical axis scale, so that $34.00 is at the bottom. Here’s what the graph would look like if the vertical axis scale began at $0.00:
Not as visually impressive, is it? Raising the baseline of a graph is one of the oldest quantation tricks in the book; it features prominently in How to Lie with Statistics, the 1954 classic by Darrell Huff. So the question becomes: Was CTCT just making a naïve attempt to produce a more visually pleasing graph, or were they trying to make a trend look better than it really is? As far the audience is concerned:
- The most sophisticated consumers of quantitative information – that is, the ones whose good opinion is probably most critical – are most likely to draw the harshest conclusions about presenter intentions, and Those forming harsh conclusions are unlikely to share them with the presenter. The sad thing is that the $5.10, or 14%, improvement in ARPU over the time span shown really does seem impressive to me. CTCT asserts that ARPU is only one of several metrics affecting lifetime customer value, and the effect of each metric is multiplicative with the others. So if they all improved by 14%, that’s a big bump in total lifetime value. Moreover, the graph shows quarterly changes; the apparently modest growth rate of 1.1% per time period shown translates into 4.4% per year.
- The tactic of truncating a graph’s vertical axis is one we see all the time. But as you consider it, ask yourself: Is the potential benefit to how the content is interpreted worth the potential impact on how you are perceived as a presenter?
Stay tuned – we’re not quite finished with the CTCT investor presentation.
The next example from the CTCT presentation is the following pair of graphs, presented side-by-side on the same slide, showing paying customer additions. Note the main takeaways CTCT wants you to have, in the titles at the top of each graph. The color coding in the left-hand graph appears to be intended to enable you to make year-over-year comparisons of the four quarters of 2013 versus 2012:
Now look more closely. The left-hand graph does not show customer additions in each quarter – every value in that graph is 35,000 or higher, and every quarter-to-quarter change in total number of paying customers shown in the right-hand graph appears to be less than 25,000. Perhaps the left-hand graph is some measure of year-over-year change, but even if that’s the case, the left-hand and right-hand graphs do not tie to each other in any way that I have been able to figure out. (Perhaps labeling the axes might have helped.)
Also, note the comment below the left-hand graph: “Customer counts rounded to the nearest 5,000.” I can’t think of a valid, honest reason why CTCT would choose to round a bunch of numbers – none higher than about 50,000 – to the nearest 5,000, when it’s obvious that they have the exact numbers of new customers at their fingertips.
Lastly, note the heading above the left-hand graph – “Four consecutive quarters of year-over-year growth. . .” This statement could be the equivalent of saying, “One consecutive year of year-over-year growth.” That just doesn’t seem impressive. Why are they bothering?
Here’s the overall problem: Rather than simply giving investors the raw data – and letting the investors calculate their own derivative metrics – CTCT has chosen to cherry-pick its metrics, present the information imprecisely (i.e., rounded on the left and hard to read accurately on the right) and in a way that the reader can’t get back to the raw numbers, and put a hard-to-validate spin on the results. Even if CTCT’s reasons are completely innocent, their approach is guaranteed to provoke suspicion in the minds of some.
Again I ask: Is the potential benefit of spinning your information worth the potential impact on how you are perceived as a presenter?
Let's look at one last example from the CTCT presentation. But this time, we look at what's omitted.
In several places in the presentation, CTCT alludes to its past and potential profitability. Here’s an example:
CTCT presents “Adjusted EBITDA” and “Free Cash Flow” in several places in the presentation, but nowhere do they present Operating Income, Net Income, EPS or any other GAAP- standard profitability metrics. They do include the following footnote:
“Adjusted EBITDA margin is a non-GAAP financial measure; a reconciliation to the nearest GAAP financial measure can be found on investor.constantcontact.com.”
Put another way, “If you want to see the corresponding GAAP numbers, you’ll have to work for it.”
Both EBITDA and Free Cash Flow are metrics that look “better” than their closest GAAP equivalents because they are calculated by adding back certain expenses and expenditures. For that reason, even if CTCT asserts that these are the most appropriate and valid metrics – a highly arguable assertion, but let’s leave that aside for now – presenting those metrics to the complete exclusion of their GAAP equivalents is certain to raise questions in the minds of some in the audience – questions like, “Why don’t they want us to look at the GAAP numbers?” “Are they hiding something?”
Again I ask: Is the potential benefit of spinning your information worth the potential impact on how you are perceived as a presenter? Only this time, the spinning doesn’t lie in what you do present, but in what you don’t present.
As we leave this subject, let’s last consider the frequency of these perhaps unfortunate presentation choices. One such choice is probably innocent or at least not worth focusing on, but three is more likely to make us question the presenter’s intentions.
[We’ll also leave aside for now the facts that “Adjusted” EBITDA (%) isn’t defined and that they don’t state what it’s a percentage of, as well as the question of whether showing these two metrics on the same graph, with two vertical axes, is a valid and appropriate way to present this information. Perhaps some other time. . .]
Last week I led a workshop on management reporting at the IMA Northern Lights Council’s annual seminar in Minneapolis. While there, I had the opportunity to sit in on several excellent presentations. One of them was Toby Groves’s overview of big data, a powerful software tool that has rightfully gotten much attention but also has inherent limitations. It sometimes takes real wisdom and willpower to see when we should stop using big data.
In discussing the ideal applications for big data, Groves observed a distinction between a “puzzle” and a “mystery.” A puzzle is something that can be solved, and often the solution comes quickly with the discovery of just a little more data. A mystery has no definitive answer, and the outcome may depend on many different interacting factors.
The puzzle/mystery distinction was famously addressed by Malcolm Gladwell in a New Yorker article and by Gregory Treverton in Smithsonian. Treverton cites as examples questions like “Where is Osama bin Laden?” (a puzzle, which hadn’t yet been solved when he wrote the article) and “What will happen in Iraq after we invade?” (a mystery, at least when we’re not looking at it in retrospect). Gladwell’s article, centered around the trial of Enron COO Jeff Skilling, ponders whether truly understanding the Enron fiasco should be treated as a puzzle or as a mystery – to Gladwell, the answer wasn’t obvious.
To solve a puzzle, we just keep collecting data, and eventually, the right pieces will fall into place. But to solve a mystery, the right strategy may be to stop collecting more data. The additional data is not only just more noise in the system, the effort to collect and assimilate it interferes with our ability to think creatively and put ourselves in place of the protagonists in the mystery (that is, if the mystery is, say, about what people are going to do next).
Here’s where the distinction relates to big data: the availability of a powerful tool tempts us to collect and organize immense quantities of data, simply because we can. But that effort can be unproductive or even destructive because the underlying problem is a mystery and not a puzzle.
The trick, of course, is knowing the difference.
You will be considered a great FP&A professional only if you can communicate clearly, effectively, and eloquently. The most basic of those choices is whether a use a table or a graph. To make that choice intelligently, it’s critical that you answer four questions that are described in this article.
Let’s take a look at some of the most messed-up, incomprehensible recent examples of quantation. Not surprisingly, all are graphs. But some come from sources that definitely should know better.
There are so many ways to graph information, and many of them are not just labor-intensive, but cognitively ineffective. But even if you’ve chosen one of the more effective ways of graphing information, also remember that graphs work best when you’re trying to make a single, critically important point.