Some Quick Thoughts on Sales Performer Analysis

While there are multiple reasons to study the production metrics of your sales force, one of my favorites is to conduct a Performer Analysis. The goal of a Sales Performer Analysis (or perhaps I should say, ‘my’ Sales Performer Analyses) is to decide who to study to determine best practices, as well as the differentiating factors between ‘top vs. middle’ and ‘top vs. low’ producers (often answering the question, “What should the average sales person CONTINUE, STOP and START doing, to be more effective?”).

Who’s on First

To determine who to study, you must first determine:

  • Which metrics truly matter and define ‘top performance’ in your business
  • What categories or performer that you will define to study
  • How you will place performers into those categories, based on your metrics

Say This 5 Times Fast:  Which Metrics Matter

Every sales force has its defining production metrics. The metrics may vary by industry, company and/or product, so I can’t offer specific advice, just general.

Generally speaking, you can consider production metrics such as:

  • The number of sales (units, pieces, orders)
  • Dollar volume/gross revenue
  • Dollar volume/net revenue
  • Profit per sale
  • Price or discounted price per sale/unit/piece/order
  • Some quality measure – perhaps orders delivered, orders cancelled, or a similar measure (Sales should have some influence or bearing on such a measure, if used)

Gather Unto Thee Thy Numbers

When you gather the metrics:

  • Gather them for your entire sales force, over some reasonable time period
  • Use a time frame that is long enough to show consistency or trends. I usually look at the last twelve months, but also the last quarter and last month, to see how the average metrics change by slice. (Is the organization – or the data for a particular performer – trending up, down, or wavering?)
  • Place your performers, their appropriate demographic data, and their production metrics on a spreadsheet, in pivot tables, in a database or in statistical analysis software, so you can sort, filter, pivot or query to slice and dice your data by a variety of ways.
  • Consider how (or if) you will level out differences in tenure. For example, if you look at dollar volume over the course of a year, a sales rep who was working for the full year has an advantage over someone who was hired after the first quarter and only worked nine months. This also makes it difficult to uncover a 3-month rep who ramped up to the top 20% (or even top 40%) very quickly. You either need to do some things to level the playing field, or only study people who were employed and actively selling in your defined timeframe. I tend toward leveling, and include as many people as possible. And I often use averages or sales (units and/or dollar volume) per day worked to level the field. 

Ow, I Think I Pulled Something

Aside from the metrics above, once I settle on which I’ll use, I’ll either ask for (in the initial data pull) or calculate averages per rep month or per day worked, for each metric. When I do this, I prefer to start counting work days from the date of first sale, rather than hire date. (Looking at elapsed time between start date and first sale is another slice of data you might want to consider, depending on what you’re hoping to accomplish.) This doubles the amount of metrics you’re looking at, but provides so many different options for analysis. (And weighting, if you want to get into that.)

Performer Categories – aka, If You Could Be an Animal, Which…

Here are some categories that I’ve used:

  • Top Seasoned Producers (top 20% and top 4%)
  • Top New Reps
  • Fastest Ramp Up
  • Most Improved Over <Timeframe>
  • Middle Producers (I often grab the slice between Mean and Median performance)
  • Bottom decile (or 8th or 9th decile, to avoid the complete bottom-feeders)

Using Metrics to Put Performers into Categories – aka Who Let the Category Out of the Bag?

Once you have your metrics with a solid rationale, and have determined who you’re trying to find, you do your analysis. (Or, if you’re smart, you find someone to do your analysis for you.)

To find the top producers, in any tenure band, I like to by sort each metric in descending order, determine the top quartile for each of those metrics, and highlight them in some way. Then I look across metrics and rank reps by how many times they fall into the top quartile. After you define that subgroup, you can do the same again within the smaller group, and consider factors like most revenue, best profit percentage, largest number of sales, and rank the best of the best.

Smaller Slice, Twice the Calories

Interestingly, if I am trying to build a hiring profile (preferably by using psychometric assessments), I include the top 4%. If I am looking to develop training best practices, I will study the top 4% to some degree, but spend more time with the rest of the top 20%, just below the top 4% (the remaining 16%). I’ve found that the top 4% (or at least a good portion of them) are often selling through their own special brand of magic or the force of their larger-than-life personalities. In reality, this means that what they do is often not replicable but the average person. Conversely, the rest of the top 20% are often ‘normal humans’ who have simply ‘figured out the magic sauce.’ And what they are doing is very often replicable by others. This isn’t a statistically-proven fact (or not by me, anyway) – it’s my personal observation based on my experiences doing this work. You’ll have to form your own opinions on this one – but that’s mine.

If You See the Buddha on the Road, Kill Him

When I started doing performance work, I’d ask the sales leaders to identify the best performers to study. Today, I may still ask opinions or look for anecdotal evidence to support my data-driven findings after-the-fact, but I long ago stopped simply asking. Here’s why.

I Swear This is True

I was once given a performer named (err… we’ll call him) Bill. Bill worked in (we’ll pretend…) Indianapolis. At the time Bill’s name was given to me by his Regional Sales VP, Bill’s monthly performance was about 40% higher than average. Pretty cool, huh? A great fellow to study. Except, Bill took over the territory from… Hmm… Sandy… about 6 months ago and had steadily been declining it. Fast forward about six months, and Bill’s performance had significantly decreased over time… now well BELOW average, and he was terminated. In hindsight, he’s not looking like a such a good guy to get best practices from, right? Fortunately, I looked at the trends over the last six months and quietly jettisoned Bill from my study group for top performers. But I still studied him… and spoke with some of his customers about him (and funny, they all told me some great things about the differences between Bill and Sandy – unprompted by me, I might add – which was awesome for my study: differentiating factors).

Meet My Auntie Doe Tull

This is why, after getting a good sense of the business and what I should look at, I do my own analysis, then seek anecdotal evidence to support or weed out performers. For sales managers, for example, you can remove their personal results and look at team performance to determine the best managers. But you also have to look for balance in the team’s performance, as opposed to one top producer carrying the group. And what you can’t tell (easily) from data but can ask about and smoke out on your own, is whether this manager lucked out with a great team, turned them around, was handed them, or built them up from scratch, recruiting, training and coaching them all to higher levels of success. Make no mistake, you *can* get to much of this through data, but it’s so challenging that it’s a lot easier to have some internal conversations and call it a day. If I’m feeling dubious after the conversations, I might do some other data-digging.

Quick is Relative (Just Like Auntie)

Well, I’m not sure now whether those are “Quick” Thoughts on Performer Analysis, or not, but I do hope they’re helpful.

Thoughts? Comments? I’d enjoy hearing from you.

More soon… in the meantime, as always, be safe out there.


Mike Kunkle

Contact me:
mike_kunkle at mindspring dot-com
214.494.9950 Google Voice

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