Using HR Tech to Track and Predict Sales Rep Longevity
Sales slumps are one of the most challenging aspects of managing a sales team. They were doing well, exceeding quota, and producing great results for the company. Then POW! They hit an invisible wall of sales death with only two potential outcomes: pull themselves out of the slump and return to their previous effectiveness, or continue in a productivity death spiral (until they quit, or you have to let them go).
After leading sales teams for more than twelve years, I have determined three truths about sales slumps:
- They will happen to every rep during their career—and not just once, but multiple times.
- The first slump’s timing is statistically predictable.
- Slumps have common causes no matter how or what is being sold.
As an HR professional, the more you can understand the sales slump’s when, why, and what, the more valuable you can be to your sales team and organization as a whole.
When Slumps Happen
The newly hired sales rep has been trained and is ready to start speaking with prospects. Most will have good results in the beginning (“beginner’s luck” is valid); next, they will plateau with closing deals at or above quota.
Then the slump happens. This pattern always happens for a sales rep who starts off doing well. For shorter business to consumer sales teams, it occurs over a five to six week time frame after graduating training. For longer business to business sales cycles, it happens during month two or three.
Why Slumps Happen
In the beginning, the new sales rep has a lot to try to manage, from reading a script and entering data into the CRM to asking questions, listening for answers, taking notes, and gathering prospect information. Typically, they over talk rather than rely on questions that lead the prospect to do most of the talking. Over time, they ask fewer questions and talk more—and don’t just talk, but over talk, assuming they know everything.
Here is where the slump happens. What they did in the beginning to be successful has been replaced with being a know-it-all “professional.” If the rep was given a script in the beginning to follow and close deals, and now they are failing to perform, I bet that script is shoved in a drawer, stained, and collecting dust. For an under-performing rep who was once successful, the first step is to dust off the ol’ script and have them get back to basics.
What Data Is Needed
To statistically predict when your organization’s salespeople will likely hit their slumps, you need to start with historical performance data. The first step is to export that performance data for each rep from the CRM, going back as far as possible. The one caveat is in most sales teams, a certain level of change is always occurring. It could be small changes to the script or sales process, or major overhauls to the pricing or the actual product/service being sold. Be careful to restrict your historical look back to a period when things were fairly similar to the current state.
Look for data on the number of closed sales during a given time period. If your company has a short sales cycle (most sales close within one week), then weekly stats are the key. For longer sales cycles, it might be more valid to look at monthly closed deal volume. While an HR Information System and CRM are powerful tools, the analysis is best done in a spreadsheet program.
Take your exported data for each rep, and in the spreadsheet, “line up” all the reps’ first weeks (or months). Typically, sales managers look at how everyone did this week or last month, but since all the reps started at different points in the past, you need to normalize the data by lining everyone up based on their first week after graduating training, then their second week, and so on. When you set the data up this way, and then graph it all together, you will see the up and down trends (and maybe even continued up and down patterns).
What to Do About It: HRIS as an Early Warning System
Once you have completed your data analysis and determined when the highest probability of a slump will occur for your teams, then what? While you might think you just invented a time machine that shows the future path of every new rep so you can prevent bad things from happening, you can’t stop slumps. Trust me, I have tried. All reps must go through it because they won’t believe you even if you try to warn them.
All you can do is know when to start intervening. The key is to create time-based triggers in your HRIS to take proactive steps when they enter that typical period. Use these triggers to alert the sales manager to what is about to occur and provide training, workshops, and coaching to the rep to help ease the slump. (Again, like with a tsunami, you can’t stop the flood; you can just put things in place to reduce the damage.) Also, put in performance-based triggers that will alert management when a salesperson’s sales volume begins to fall off, based on your predictive analytics. Like a tsunami early warning system for coastal towns, the sooner you can take action to keep your sales reps safe, the more likely they will make it.
The Bigger Picture Purpose: Proactive Staffing/Recruiting
In conclusion, you cannot stop slumps, and unfortunately, they will lead to the loss of some reps. There is always a certain level of turnover in a sales team, and now you have a statistical model for when some of it will occur. Taking this data, you can build a hiring timeline to proactively ensure the necessary staffing level in the sales department. For example, if you know a sales rep slump will occur around Week 5 or 6, and that 30 percent of your new reps won’t make it out of that slump, you can work backwards to ensure recruiting, hiring, and training of new reps is filling in the gaps those under-performing reps will leave.
Author Bio: With the title of Sales Success Architect, Jason Cutter’s consulting business is focused on helping organizations with their inside sales teams. From his sales-related podcast
to guest articles written about selling at tradeshows, his mission to help salespeople go from Order Taker to Quota Breaker.