Let’s say you have 100 people on a sales team. Can they all be great? Most sales leaders say no. Conventional wisdom will say something like ten of them can be exceptional. Ten of them will be dogs. And the other eighty will fall somewhere between “solid performers” and “good enough to not be fired.”
I hate conventional wisdom. Especially when it’s as destructive as this. And make no mistake, this is destructive logic with little more than a baseless rule of thumb at its core. Nonetheless, most sales leaders pretty much accept the 10-80-10 performance standard as an incontestable law of nature. In so doing, we are inherently setting an artificially low bar with which to measure our success. Provided you have a great offering and a large enough target market, the goal of a sales leader should be to ensure that every salesperson on the team performs spectacularly.
Ironically, the KPIs – key performance indicators – that many sales leaders rely on to measure, predict and ensure sales rep success often actually perpetuate tiered success levels in which too many salespeople fail to meet their potential.
The problems with KPIs stem from sales leaders’ perceptions of KPIs and the opportunity cost of not taking a more scientific approach to understanding a company’s actual predictors of sales success:
Unreasonable Expectations & Emphasis
Think about your own sales KPIs. Do you have salespeople who hit all the KPIs, but still land on the lower half of the leaderboard? Do you have people at the top of the leaderboard who haven’t hit all their KPIs? Of course you do! In itself, that’s not a problem, but simply illustrative that most KPIs aren’t the locked and loaded predictors that sales leaders might wish they were.
KPIs are usually created with the right intentions. In some cases, they are meant to get out ahead of problems before they become systemic. In other cases they might be intended to set minimally acceptable levels of effort or output required by every member of the sales team. In these senses, KPIs can be directionally helpful predictors of success and failure.
However, KPIs are usually created from team averages without a detailed understanding of individual rep strengths or nuanced attributes of individual deals. Too many sales leaders overlook these differences and instead see the KPIs as black and white predictors of success. This becomes management by numbers and these leaders have teed themselves up for disappointing surprises.
When generic KPIs are overemphasized, they incentivize the worst possible behaviors at all levels of the sales organization. Sandbagging, or hiding deals, is heightened in organizations that overemphasize sales cycles or uniform stage velocities – even though everyone will acknowledge that there may be legitimate and compelling reasons for certain deals to progress more slowly than others. When organizations overemphasize metrics for forecast calls, reps often feel compelled to “commit” deals that they know are a little more than a shot in the dark. Organizations that lean too heavily on activity metrics often find reps logging questionable calls or sending superfluous emails that don’t bring a buyer any closer to a decision. Companies that inflexibly demand a 3-to-1 or 4-to-1 pipeline ratio often see leads prematurely being categorized as opportunities, and sacrifice quality for quantity.
The result is a morass of bad sales data that only pushes the target of understanding the true drivers of success and failure farther away.
Ignoring Half the Story
When defining new KPIs, sales leaders often look at attributes of the deals that won or the sales people consistently at the top of the leaderboard and attempt to draw conclusions. That seems like a reasonable starting point, right?
As a starting point, dissecting attributes of the best salespeople or winning deals is fine. But if the process ends here, sales leaders are positioning themselves for disappointing surprises because they haven’t considered the traits of underperforming reps and deals that lose. Too often, we find that companies’ chosen KPIs would have varied only negligibly had they been based on attributes of losing deals or reps outside of the top 10%.
What worked in sales yesterday, doesn’t always work today nor is it guaranteed to work tomorrow. Yet too many sales organizations are built around KPIs that haven’t been re-evaluated in years or don’t more heavily weight recent events.
And versus Or
Think about your company. What is fundamentally different about the deals that your organization wins from the deals that you lose? Is it certain buyer attributes? Is it certain seller attributes? Price? Product? Activity levels? Does sales cycle or stage velocity mean anything? Competition?
Most sales leaders intuitively recognize that all of these variables (and more) simultaneously impact every deal in the sales pipeline. Success and failure are not decided by one variable or another. They are the product of lots of different elements all moving simultaneously. Yet most KPIs are derived from single variable data silos, without any regard for nuances that might predict individual deal outcomes and rep success levels.
Great salespeople are dreamers. They naturally “think big” and have an “eat what you kill” mentality. They are smart, creative and competitive. Being less than spectacular is unfathomable. They have an ego, but they’re not arrogant. They will learn from the best, but they need to be better.
This is the DNA that we all hire for and it’s the DNA that positions us all for success.
When we forcibly jam these forces of nature into a sales mold based on perceived success drivers for a collection of other reps, we are discounting each individual salesperson’s individual strengths, weaknesses and abilities to succeed outside of that precise mold. Instead of capitalizing on the attributes that position each salesperson and each opportunity in the pipeline for success, we are creating cadence-driven robots instead of thoughtful business leaders.
Jim Dries is the CEO of piLYTIX, a data science as a service company serving heads of sales and their teams.
“I know my salespeople.”
On the surface this simple four word sentence sounds pretty innocuous, doesn’t it? It certainly doesn’t sound like something that should cause alarm.
But that’s exactly what it does for us. When our account leaders hear this from a sales manager that they are tasked with serving, we know that we are in for a bumpy start to our relationship. When we hear this during a sales call with prospective clients, we instantly know that our own sales job just got a lot harder.
We recognize that the nature of our company likely causes knee-jerk reactions for some managers. Our company systemically dissects companies’ sales data to find the most impactful drivers of success and failure in their sales organizations. Individual rep skills, tendencies and biases always factor into our calculus. Absent a deeper understanding of our mathematical approach and our mission to truly complement their skills as managers, natural defense mechanisms kick in for a few managers. For these managers, “I know my sales reps” is just a more dignified way of saying “Bugger off eggheads! I have worked hard to get my team this far and your team of math geeks will never know more about my reps than I do.”
We take no offense to the characterization and some managers quickly drop their defenses. Others dig their heels in.
Our technical output allows sales managers to see very clearly all of the factors that make individual opportunities more likely or less likely to close. The factors might include things like the price of the deal, a buyer’s history, the product or products being sold, age of the opportunity, velocity at which the deal progressed through various stages, activities, or engagement data that has been captured by other technologies. For tenured reps, the output is heavily influenced by the individual rep’s historical performance in each of the areas. It also includes a recorded history of how the rep feels about a deal’s likelihood to close.
We have come to learn that this last point – the rep’s feelings – are usually what sales managers are attaching to when they say “I know my salespeople.” Tenured managers develop this sense over time on their periodic pipeline reviews or forecast calls. After these calls, the manager will speak with a Sales V.P. and say things like:
“Bob is perpetually optimistic, so I am highly doubtful that he is going to land this whale.”
“Diane is a sandbagger. All her deals are showing that they are in early stages, but she always comes through in the end, so I will just put her at quota.”
“Ed’s committed deals never seem to be on time, but they always come in, so I will put half of them this month and half next month”
As it turns out, many of these managers are often directionally correct. So what’s the big deal then?
These generalizations, based on solid observations over time, in all likelihood will help achieve marginally better sales forecasts. Sales forecasting is an important component of many sales managers’ jobs. However, it is never the primary role for which managers were hired. Sales managers’ first responsibility must be to help drive sales for their organizations. We find that most managers who continue to express some form of “I know my salespeople” inhibit their own ability to drive sales.
Let’s use the above example of “Bob the Optimist” to illustrate this point:
For argument’s sake, let’s say that we can quantifiably prove that Bob aggressively counts his chickens before they hatch. That doesn’t make him a bad sales rep. It makes him a bad forecaster. Presumably, he still wins contracts or we would be talking about an ex-employee named Bob instead of his forecast. So all we really know about Bob is that he is good enough to not get fired and that he regularly has disappointing surprise losses.
Bob doesn’t know why he is losing these deals. Bob is doing the sales equivalent of spiking the football on the 1-yard line when he embarrassingly commits deals that he doesn’t win. His manager, who presumably was hired to help Bob ensure Bob’s success, doesn’t know why these deals are losing or he would have saved Bob some embarrassment and ensured that Bob understood the obstacles that are reducing the likelihood of this deal closing.
Instead of patting himself on the back for his grasp of the obvious trend that Bob overcommits deals, the sales manager would better serve his company by striving to understand where Bob’s disconnect lies. Bob does win some of his committed deals. Both Bob and his manager need to understand the fundamental differences between the deals that win and the deals that end in surprise losses. Is there a price point where Bob begins to struggle? Are there certain products that Bob is less effective selling? Are there certain types of prospects that Bob can’t seem to close? Are there certain activity metrics that might foreshadow success or failure? Are there seasonal buying habits that Bob is oblivious to? Is Bob using the wrong content or the wrong communications tools for certain types of deals? What other stories are hidden in the sales data?
Bob’s manager – who says he knows his reps – rarely knows any of these answers. And he certainly can’t conceptualize that all of these elements (and more) come into play on every deal. Nor does he understand that the signals may have different levels of strength on each deal, since no two deals are identical and no two reps capture sales data identically.
In many cases, while Bob was celebrating the win that never happened, his manager could have helped him shore up weaknesses in the deal. Maybe this deal had three or four specific and addressable weaknesses. Perhaps there was data that would have shown that our prospect wasn’t as engaged as Bob might have thought. Bob’s manager could have asked pointed questions to truly assess the prospect’s position. Maybe Bob struggles selling to a certain industry. The manager could have paired Bob with a colleague to serve as an industry expert or ensure that proven industry-specific content was made available to Bob. Maybe Bob is our best sales rep for small deals, but simply can’t close a deal over a certain price threshold. His manager should insert himself into negotiations so Bob can learn how to ask for more money. Maybe Bob is not seeing as much benefit in a new sales technology as his peers. The company spent a lot of money on the technology and has every interest in ensuring reps understand best practices for extracting maximum value.
In other cases, poor Bob will spend months chasing an opportunity as his top priority that was virtually doomed from the beginning. He will work day and night to close a deal that has hidden roadblocks. He will give the manager enthusiastic reasons for exerting so much energy on these deals. Those reasons may be totally valid. Bob may even be leaning on mathematically provable deal predictors that have been present in other deals that he won. But Bob’s inability to understand all the predictors of win and loss prevented him from understanding the deal’s inherent weaknesses. The time that Bob wastes on these deals comes at the direct expense of other deals in his pipeline that were mathematically more likely to win.
Unfortunately for Bob and his manager, it’s almost never just one data point that predicts whether a deal will win or lose. Bob happens to be a human being who makes decisions partly based on emotion or gut feelings. We all do. We all have blinders and biases that prevent us from making the right decision. When this happens, we need someone to get us back on track. In sales organizations, that job belongs to sales managers.
When sales managers – who also happen to be a human beings– can’t acknowledge their own blind spots, it renders them totally incapable of helping their teams achieve the best possible results. By failing to understand what drives some of Bob’s deals to succeed and others to fail, Bob’s manager can’t give tailored coaching. The end result is that Bob’s will never achieve his full potential and our company will lose deals that it should have won. But Bob is only one rep. If a company has 10 or 100 or 1000 salespeople, the aggregate impact of arrogantly “knowing our sales people” is catastrophic.
Jim Dries is the CEO of piLYTIX.
A cold shiver went up my spine when I reviewed the call notes that an account manager had taken during a recent client onboarding meeting. Buried deep in the notes was a quote made by a well-respected head of sales operations at a large global software company.
“At the start of last year, we instituted a strict policy of managing to clearly defined activity metrics. As a result, we experienced a 15% sales increase.”
I took an action. The team had success. Therefore, this specific action caused success.
As a proud data geek this horrifies me because there is an implied lack of understanding of the meanings of correlation and causation by someone who should know better. (For a quick primer on the difference between the two, @KirkDBorne recently tweeted a brilliantly simple explanation: twitter.com/KirkDBorne.
More importantly, I have had a front row seat to this same movie more times than I can count. It always has a sad ending.
It’s not uncommon for business leaders to point to their innovations as “the” drivers for team success. A little self-promotion is needed in some organizations to navigate around sticky political situations or climb the corporate ladder.
In this case, however, the SVP of Sales had hired our company to help his team achieve greater success through the application of advanced analytics. This was not self-promotion. He believed his story and encouraged us to believe that this change was the only factor in the year-over-year improvement. Nothing else had changed, he claimed. Further, he pointed to an incredibly powerful statistic: His own data analysis showed that tracked activities (phone calls, opened opportunities, meetings, emails) in aggregate did increase substantially. Fifteen percent to be precise – the exact level that sales increased by.
This statistic is powerful, but ultimately misleading.
It took a data scientist on our team less than an hour to poke holes in the sales leader’s theory that the company’s new management style caused the increased sales levels. We have seen many companies fall into this trap of over-reliance on activity metrics before so we knew how to test the hypothesis that the 15% increase in activities directly led to the 15% uptick in sales.
As a first step we divided the sales reps into four equally sized quartiles based on sales. We also divided the sales reps into quartiles based on win rates. Not surprisingly, most reps landed in the same quartile in either trial (reps who with the highest gross sales tended to be the reps with the highest win rates).
When we look at activities and results in these more granular buckets it quickly becomes interesting: There was only a negligible increase in activities for the top two quartiles (less than 2%). The bulk of the activity increase came from the bottom half of sales performers. The bottom quartile witnessed a whopping 34% increase in activity levels. However, when we looked at actual results (gross sales and win rates), the results were flipped upside down: The top half of performers, whose activity levels barely changed, saw the greatest increase in win rates and gross sales.
Clearly, something other than the new activity based management approach was driving the sales increase.
In this fairly standard case, we see evidence that the bottom half of the sales organization is pumping the pipeline full of questionable opportunities and logging more calls and emails for these questionable opportunities in order to avoid negative consequences. The reps have done as instructed, but there is not a corresponding focus on the quality or validity of the opportunity or the interaction. Moreover, managers who are focusing on the activity tally for their reps (because they are being evaluated on their implementation of the strategy) are less likely to focus on the viability of the opportunity or the quality of the interaction.
This sales leader made a common mistake in interpreting data. More importantly, when companies focus their sales strategies exclusively on metrics associated with quantity of activities they are bound to be disappointed. Here’s why:
• We expect to see lower performing reps entering more unqualified opportunities and closing at lower win rates. More time is wasted on fudging bad data. Less time is focused on expanding skills sets.
• Higher performing sales reps tend to have a laser focus on achievement: hitting their sales targets, getting the next sale, achieving the next rung in the commission ladder. They understand how to sell and loathe being micromanaged. Focusing on activities fixes a problem that doesn’t exist.
• Even the best reps have weaknesses. If sales management has made the investment in coaching reps, management should include some efforts to identify and coach to those weaknesses. These weaknesses might only be microscopically visible when we see some combination of other predictive variables like: a certain type of account, size of the prospect, deal size, type of meeting, funnel stage, deals in which a certain competitor is involved, industry of the prospect, or product(s) being sold. ??]
• The data might show that a rep’s aggregate sales activities are high, but frequency, kind or quality of these interactions are predictive of the final outcome. Some combination of several of these individual weaknesses may be a blind spot for the rep and the manager. Coaching to activity metrics typically involves coaching to metrics designed from a whole team instead of coaching to the individual rep’s most impactful activities or behaviors.
• Coaching to average metrics takes significant management focus and prevents a search for all the other predictors of success to which managers can coach.
None of this should suggest that we don’t value activity metrics as an important part of the sales process. However, activities like anything else need to be considered in the context of all the other sales data when implementing sales strategies and interpreting results. They may sometime correlate to success, but do not confuse that with causing success alone. Organizations that measure the quality of interactions between reps and prospects, as opposed to just the quantity of those interactions, are much better positioned to understand the drivers of sales success and failure.
Postscript: After this article was first published in an industry blog several readers wanted to know what led to the 15% growth rate that the sales leader quoted. Despite the sales leader’s initial claims that nothing else had changed, there were more subtle moves that appear to have had some impact. These moves involved changes to sales territories and comp plans. This was also the company’s 4th consecutive year with a double digit growth rate.
Jim Dries is the sales rep in chief and head data geek for piLYTIX.
“This all sounds very interesting. Can you send me a few references?” Words that every sales rep loves to hear. After all, people only ask for references when they are getting ready to buy, right?