I am constantly energized by the lightning-fast pace at which the predictive analytics industry is evolving. I am proud to be a part of a company that is making a difference for our users. However, my job is so much more difficult than it needs to be because of the wounds that the predictive analytics industry continues to inflict upon itself in the form of marketing messages that only serve to confuse audiences thirsty for knowledge.
Good companies in the predictive analytics space are all fundamentally founded on the premise of helping business leaders and/or consumers efficiently make better decisions. When successful, these better decisions will result in finding previously unseen value. By reducing the guesswork involved in decision making, the value for the end user should be savings of cash or time, waste reduction or revenue increases. All of these are the intended consequences for consumers of certain predictive analytics tools. That’s a pretty simple premise.
And yet as an industry, we do everything to complicate the premise by refusing to engage in a basic dialogue of how our tools work. Instead, we rely on buzzwords to describe our products. Sometimes these buzzwords have actual definitions (AI, Proprietary Machine Learning Algorithms, et al). Other times we use or clichés (black box, secret sauce, et al).
More often than not, use of these buzzwords seems intended to avoid a reasonable discussion about what happens to a dataset to arrive at the insights generated by the predictive tools. The message is basically, “trust us, we’re smart.”
As money continues to pour into companies throughout the nascent analytics industry from investors and consumers alike, there is not a natural impetus to change this messaging. Yet.
“Trust us, we’re smart” won’t be a message that sells forever. Even analytics companies that are successful today with good products will be burned if our markets develop a strong enough distaste for our industry. That distaste will be sharpened by buyers of ineffective products when they learn that the sauce isn’t so secret, the black box is empty, and that the A.I. is more artificial than intelligent.
Some of the analytics companies that continue to rely on the buzzwords actually have products that are powered by truly ingenious mathematical approaches. Other companies use the buzzwords to avoid discussions about mathematical engines that a decent high school math student could produce.
Without a high level description of how the “black boxes” work, the buzzwords subtly connote magic. People love the entertainment value of magic acts because they appreciate that they can be tricked. However, when people face a decision of any financial consequence and they instinctively feel that magic is involved in a decision making tool, they quickly discount – or totally ignore – the output. The mystery that my industry has shrouded itself in by its continued overreliance on these terms has caused suspicion. That suspicion has led to underutilization of analytics tools by the people and the companies that need the most help.
Those of us who believe in the decision making power that our industry can bring – producers and consumers of analytics alike – are responsible for changing this environment.
Good analytical products are built on a foundation of solid mathematics, but we insult the intellect of our purchasing populations when we over-rely on buzzwords and too-good-to-be-true ROI claims. Sellers of these products need to ensure that their users understand how the products work. This doesn’t mean that I encourage anyone to publish their algorithms for the world to see, but we can all discuss the inner workings of our mathematical engines in a way that is accessible to broad audiences. Allowing users to understand the basic inputs that drive our models is a step in the right direction. Deeper discussions of how changes to these inputs impact the outputs will bring us a lot closer to credibility.
Until competition or a tightening market compels changes in analytics providers’ messaging, it will ultimately require the buyers of these products to demand answers to basic questions. “How does it work” seems like a good place to start.
Jim Dries is the CEO of piLYTIX, a provider of predictive analytics solutions for unique sales organizations.
“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.
There was a time not too long ago when sales ops positions were viewed by executive leadership teams as a needed expense to keep the trains running on time. Too often, though, sales ops professionals lacked the organizational support to think in bigger terms of the wider strategic impact they could have on the rest of the organization – marketing, finance, and corporate strategy. The big data revolution which has been accelerated by advances in technology and business schools’ focus on “data driven decision making” has rapidly changed the profile of sales ops leaders. More often than not, we are seeing today’s sales ops leaders having a sharper quant focus. They are seen as having a more integral role in the C-suite.
Exactly as expected with this newly refined profile of sales ops leaders, a focus on better sales data has taken center stage. However, at company after company, we see a common mistake repeating itself. Too many sales ops leaders and their marketing counterparts are simply equating “more data” with “better data.” And, in plain fact, more data very often is better. Since most CRM systems are so easy to modify, we see incredibly elaborate customizations in which sales reps can enter sales data into several dozens of fields.
However, in seeking so much feedback from field reps, there are too many practical realities of 21st Century B2B sales that are being ignored. Ask yourself this, have you ever heard a good sales rep ask the following question: “What do you want me to do, close deals or enter data all day?” Whether the question is fair or not is irrelevant. Of course we want reps to close business AND comply with company CRM standards. But before we just brush off the combative rep, perhaps it is worth examining our CRM expectations.
At piLYTIX, we closely monitor CRM usage stats. Remarkably, we have found an inverse relationship between the number of added custom fields and the level of rep CRM usage. Hidden deals, surprise short term closes, and clear “sandbagging” indications tend to be highest at the organizations that have asked reps to enter the most fields of data.
While our clients benefit from specific recommendations for CRM adaptation, we encourage all senior sales ops leaders to consider the following when considering their data policies:
Take the time to educate sales reps how they will directly benefit from complying with your CRM standards. Hint: if you can’t convince sales reps of what’s in it for them, you will never solve your data collection problems.
Focus on those fields that directly speak to the most important priorities of stakeholders throughout the organization.
Learn which fields correlate with deal success (or failure) and ensure that there is focus on those fields. Don’t assume that these fields will be identical from one company to the next.
Recognize that while sales reps are tremendous sources of market intel, they are not professional market researchers. What information is better collected via full time professional market researchers?
If you use third party prospect engagement technologies, ensure that the quantitative outputs of those systems is integrated into your sales data. If your sales rep is doing the hard work of selling and you have paid a vendor to track open rates, phone meeting time, or web conferencing data, then it seems reasonable that you would want that technology seamlessly integrated.
This intervention is long overdue.
You know you have a data problem but haven’t fixed it. You know that data holds power and there are tremendous benefits getting this problem under control. You know that you are losing sales because of your inability to derive insights from your data and you know that your company needs to learn from its own sales data in order to stay competitive. But your data challenges run deep and it would take serious effort to get your data cleaned up. Given the perceived time commitments, you feel that you can’t make this a priority.
However, if we acknowledge the root causes of your data difficulties, the most efficient path forward will become easier to find:
You Inherited It
Perhaps you arrived at a company that is younger and hadn’t yet established rigorous data collection standards – or had made frequent changes to how it collected data. Maybe it’s an older company that fell into bad habits. Maybe your predecessor failed to see the benefit of basic CRM hygiene – or wasn’t able to convince a diverse sales team of its benefits. Maybe there have been changes because of new reps or expanding products or markets and data quality standards were put on the back burner.
Organizational Support Lacking / Unrealistic Expectations
You were hired to help grow sales immediately. Not next month, next quarter or next year. Any data project will take time and won’t be reflected by a revenue increase on this quarter’s income statement.
You have a few people or teams who don’t enter sales information until immediately before deals close. Or others who downplay the likelihood of deals closing.
Inferior Old CRM
You are planning a move to a new CRM system and will focus on quality then.
These root causes, however, are becoming excuses. You need to take ownership of the problem. Your revenue team has the most direct line of site to the market. Ensuring your company’s maximum growth requires constant interpretation of market feedback.
If you are going to finally fix your data problem, you need an airtight plan. As you build that plan, consider the following:
Consider all stakeholders’ needs – most importantly your sales reps and managers. Most sales professionals are competitive by nature and financially motivated. When they are sloppy with their data habits it’s because they don’t see the upside in doing it any other way. Be ready to answer the question of “what’s in it for me” early and often. Tap into the individual, selfish motivations that define each rep and each manager. Your data quality plan has to connect the dots for the reps in such a way that shows them a path to more sales. (For more thoughts on sales teams’ motivation, see: http://pilytix.com/blog/3-Reasons-Your-Sales-Team-Shuns-Sales-Technology)
Blaming sandbagging reps in your plan will get you nowhere. Sandbaggers exist for two reasons: 1) The failure to answer the aforementioned question “what’s in it for me” and 2) selective outrage depending on results. Reps who hit their targets are usually given a free pass while those who don’t hit their numbers are reminded of their data habits as an item on a laundry list of behaviors that need to be improved. Ask yourself, has inconsistent messaging ever resulted in universal adoption of…anything?
“Perfect data” should never be a goal. Many organizations who see the value in data collection have frequently ignored practical realities of selling in the 21st Century. They have built and modified their CRMs in such a way that requires sales reps to fill out too much data. This mission for perfect data ultimately prevents companies from getting any insightful data. (For more thoughts on this common misstep, see: http://pilytix.com/blog/more-vs-better-data).
Before you make any changes, start by learning which of the fields of data actually have the potential to provide useful insights about your reps or your sales opportunities. And don’t trick yourself into believing that all sales data is equally valuable.
If a major technical overhaul is required, assume that it will take longer than expected. Behavioral changes cannot wait for the completion of the new technical infrastructure. The idea that good habits can wait only ensures that the timeline to ultimate success will be indefinitely extended. When the plan is complete, its benefits need to be communicated throughout the organization immediately.
Measuring, Monitoring and Correcting:
As your data improves, sales leadership needs to stay vigilant or bad habits will creep back in. Part of this vigilance is measuring adoption of the plan. Every other major corporate expense is typically scrutinized and assessed for effectiveness, yet we rarely see sales leaders measuring the usage of their single largest technical expense – the CRM system. If your messaging has been effective and your expectations are realistic, sales reps and their managers should want to ensure quality data. There will always be exceptions, though. Sales leaders who measure individual rep CRM usage can take much more effective corrective actions with the outlier individuals and prevent individual habits from becoming systemic challenges.
Jim Dries is the CEO for piLYTIX. He is immediately suspicious of sales managers who claim to have excellent sales data.
Your company has just made a big investment in a new sales technology. Maybe you were the new product’s champion or you gave final approval for the expenditure. You know that this should have a major impact on sales. And now, no one seems to be using it. Your frustration and embarrassment grows with each passing day.
You’re not alone.
The sales world descended upon San Francisco last week for DreamForce, SalesForce’s annual product celebration for its throngs of loyal users, developers, employees and consultants.
Hundreds of companies that live in the SalesForce ecosystem were there too, and some of these companies were pitching truly innovative solutions to the myriad challenges that sales leaders face. And yet in discussion after discussion we ran into sales leaders who repeatedly lamented the fact that they can’t seem to coax their sales reps and managers to effectively use the technology that they already have.
We’ve been doing this for a while. If you are one of these leaders, here are a few of the reasons you’re struggling:
You haven’t addressed the only question that matters for most sales reps.
Let’s all stop with the nonsense that “reps should do what I tell them to do if they want to keep their jobs.” The best case scenario when you take this approach is that your reps will nod and smile and pretend to play along or do the bare minimum required to not get fired. They will not be inspired to extract value from your new sales tool. They won’t be inspired period.
Let’s try a different angle, no? Let’s acknowledge who we have hired. The best sales people tend to be smart, competitive, financially-driven and self-motivated. Whether you like it or not, the first question that they will ask is “What’s in it for me?” So tell them. Show them. Just be sure that the answer includes an obvious nod to the things that they care about: closing more business, making more money and climbing the leaderboard (or retaining their position at the top). If you can’t make these arguments to yourself, there is no chance you can get buy-in from your end users.
You haven’t proven that there is something in it for them.
Telling reps and managers how they will benefit is a good start, but it isn’t enough. You need to offer some proof and unfortunately, you alone are not the best positioned to convince the rank and file members of your sales team. Just like your sales prospects are more likely to buy based on the recommendation of a trusted confidant, your reps are more likely to follow the guidance of their colleagues who are in the field selling.
The smoothest technology implementations result when senior leadership enlists a handful of successful reps and mid-level managers to serve as internal “beta testers.” Ensure that they understand the cachet associated with being selected for this group. Take extra time with these reps to ensure that they understand their personal upside. Deputize them to help you sell it to their colleagues. They’re good sales people, after all. Prepare them to buy credibility with their colleagues by airing and addressing contrarian positions before the wider team launch. When you do roll it out to the wider team, avoid a monologue and instead guide a discussion amongst the beta testing group.
You actually bought a clunker.
Unlikely. You’re too smart for that. However, if the rep who sold the useless technology did a disservice to the noble profession of sales and snookered you into a bad deal, circle back with someone higher in the organization. You would fix this if this happened in your own sales organization. Most companies will.
Jim Dries is the sales rep in chief and head data geek for piLYTIX.
The world of predictive analytics has become a crowded field. To get an edge, I see players in this field making some fantastic promises about the “proven ROI” that their offerings provide. The claims often sound fairly compelling. An X% increase in sales! A Y% decrease in cycle times! Z% higher price points!
There was a time not too long ago when sales ops positions were viewed by executive leadership teams as a needed expense to keep the trains running on time. Too often, though, sales ops professionals lacked the organizational support to think in bigger terms of the wider strategic impact they could have on the rest of the organization – marketing, finance, and corporate strategy.