There are multiple ways to analyze sales performance data to improve results. One of the most popular approaches is to manage the current pipeline and the individual opportunities in it, because it gives you a chance to improve the outcomes.
If you can influence an opportunity in progress, you can increase the likelihood of a win or disqualify or de-emphasize an opportunity (meaning, deciding to nurture it, versus pursue it assertively), to free up time to work deals you can win. Managing leading indicators in this way is important, intelligent, and necessary. Your sales managers can do this as part of their sales management operating system and their established cadence for pipeline reviews and forecasting meetings.
In contrast, one of the least used methods in many sales forces is a review of historical data – not just for reporting outcomes – but to analyze the past with the intent of learning where to focus. Reviewing historical data helps you see where to focus organizationally or with individual sellers to improve skill levels and raise overall performance in the future.
This analysis of lagging indicators (results/outcomes) allows you to take advantage of a much larger data set and should be part of helping front-line sales managers determine where to spend their valuable sales coaching time to have a significant impact on performance.
Building a “Rearview Mirror” Pipeline Analysis
For this purpose of performance analysis, you need the ability to track the following:
- Sales process stages
- The number of opportunities in each stage, over a given historical period (usually a look-back period equal to one-to-three times your average sales cycle)
- Conversion ratios between stages
Take a close look at the above example. With the basics of stage and conversion tracking in place, you’ll want to enable reporting on a few specific buckets of performers such as:
- Top Performer Averages: They should remain anonymous, except to a select few leaders who selected them for inclusion and the report designer who pulls and massages their data. Based on where the natural dividing lines occur, I’ve used from the top 4 percent to the top 20 percent. These are the averages of the top performers in the examined timeframe, so they appear as one person’s results and can be compared to an individual performer’s results. (This post on the difference between top performers and circumstantial top producers may be helpful, as you identify your top performer segment.)
- Mid Performer Averages: As above, they should remain anonymous, except to a select few. Consider using from 10 percent above the “average” midpoint to 10 percent below it. Same as above, on averaging, so their results can be compared.
- Manager Team Averages: These are the aggregate results for the team that reports to a specific sales manager. Same as above, except there is no need for confidentiality.
- Individual Rep Actual Production: These are the named individual sales reps (Rep 1, Rep 2, etc.) on the team, with their actual production results for the timeframe, so they can be compared to the above performer segments.
This is a historical report. Look back over a select number of months to produce the opportunities per stage and the conversion ratios between stages. The approach should be contextual because there isn’t one approach that will work in every case. I usually recommend a backward look that is at least the length of the average sales cycle and often longer. The shorter the sales cycle, the further back you should look, to collect enough data. I’ve used anywhere from 2-6 times the average sales cycle to look back on, with 2-3 being the most common.
You might want to experiment with looking at the mean, median, and mode for average methods, remove distant outliers, or build a smart algorithm. If you can automate this as a rolling report, it saves time and can be incorporated into regular coaching analyses. In addition, you might benchmark and track other metrics over the same timeframe, such as sales velocity, sales productivity (revenue per producer), and win/loss ratios, to see how they trended in comparison to the pipeline data.
If your business is affected by seasonal trends, that’s another factor you may need to adjust for. Depending on the number of products you sell and how different the sales processes may be, you might also need to consider segmenting your report by solution.
As needed, seek support from your sales or revenue operations leader, a business or data analyst, a data scientist, or a consultant. The collaboration and added expertise can provide unexpected added value and will be helpful. Plus, there are some downsides to working with averages, and a well-versed data pro can help you minimize them.
With the report in place in a way that makes sense for your company and sales force, now you can begin your analysis.
Using The Data to Target Coaching Efforts to Improve Results
This is an excellent report to use for developmental coaching, specifically to identify opportunities for improvement that will raise performance as a whole. (For a definition of developmental coaching and a comparison to opportunistic coaching, see this post.)
Here’s how you can do that, using the report that appears above.
In scanning this report for Rep 1 (from the bottom, follow your eyes or finger up each column, looking for large discrepancies), you might notice that this rep is working a far lower number of opportunities in this timeframe, compared to his peers. It’s especially a stark contrast when compared to your Top Performer Averages.
The first question I’d ask this rep’s manager, is whether it’s likely that they’ll move this rep from where they are to the Top Performer numbers. Most will realize that it’s not realistic.
The second question would be to ask what they do think is reasonable. In this case the manager said (yes, it’s a real example, with some numbers rounded up/down for easier reading) that she should first try to move him up to the team averages. This would move the rep from working only 50 opportunities in the reporting timeframe to 70 and improve Stages 2 and 3 Conversion from 50 percent to 60 percent.
The rest is just math, right? Assuming the rep’s performance remains steady in other areas, if you improve lead generation and work more qualified opportunities and lift the stages 2-to-3 conversion ratio, this rep will move from 4 sales in the period to 7, for a 75 percent improvement!
When was the last time you helped someone achieve that type of lift? It is entirely possible, and it’s why we include this type of analysis in our Sales Coaching Excellence program.
This rep (and this manager’s team) tends toward stronger conversions in later stages, so improving early-stage metrics will significantly improve the team. For Rep 2, if the final closing percentage can be moved, both Rep 1 and Rep 2 will both deliver far improved results.
What to Do With the Analysis
Obviously, knowing where to target your field training and sales coaching efforts is the beginning of the performance improvement journey. It is, however, the first step. Far too many organizations practice “random acts of coaching” that are predicated on happenstance or luck. Instead, there should be purposeful, deliberate, targeted, behavioral-based developmental coaching that is designed to “raise the water level” for employee performance, rather than providing feedback about one thing in a specific opportunity, that may not have a universal impact.
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In the above example with Rep 1, following the ROAM model (Results vs. Objectives, Activities + Methodology), you’d need to continue to explore the activities (the A in ROAM) they perform for lead generation and qualifying, as well as their approach in Stage 2, and explore the quality of those activities. That includes how well they are executing your sales process, sales playbook(s), and using your chosen sales methodology (the M in ROAM). You can certainly explore through discussion, but observation (either reviewing call recordings and/or live observation) will likely be required to determine how to best close the performance gap and improve results.
For more on how to do those things, see our eBook that explores sales coaching excellence. And that is how you can analyze historical data to improve future sales performance.
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