Redefining the Player-Coach Relationship

Player-Coach discussions must focus less on unscientific "gut feels" or "commit forecasts" and focus more on pulling the levers that have been quantifiably identified to lead to a higher closing likelihood.

Four Dangerous Words

These Four Words are Dangerous When Said by a Sales Manager

“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.

Managing to Activity Metrics

Managing to Activity Metrics: Coming Soon to a Sales Strategy Landfill

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.”

Otherwise stated:

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:

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.

Jan 26, 2017 Read More

piLYTIX Talent Management Solutions

PX Talent Assessment:
Is a rep successful because he or she is genuinely talented? Or is it because he or she has been handed the best opportunities or territory? The PX Talent Assessment tool provides a continuously updated assessment of your reps’ talent in the context of the most impactful predictors of deal close for your company. Understanding your reps’ underlying strengths and weaknesses will position managers for more impactful coaching and training sessions.
PX Projected Performance:
Commit forecasts are based on biased opinions of reps and managers. Analytically identifying potentially missed goals early provides the best possibilities for managers and reps to change outcomes while there is still time.
PX Money at Risk:
It is not enough to know which opportunities are at risk. Reps and managers quickly revert to acting on their “gut feelings” about opportunities unless they understand specifically why opportunities have been identified as “at risk” and what they can do to influence the outcomes. Looking at a few generically predefined deal predictors isn’t enough. Every opportunity needs to be examined in the context of all the individual opportunity’s key attributes and the individual rep’s strengths and weaknesses. piLYTIX’ risk tools empower managers and reps to have meaningful opportunity reviews and coaching sessions.
PX Most Likely Wins:
Surprise losses can be devastating to a sales team’s psyche. Overcoming those losses is best accomplished by applying proper focus to those deals that have a confluence of factors that correlate to a high likelihood of success. The Most Likely Wins dashboard presents reps and managers with deals that show the greatest mathematical probability for success.