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Data Is Data, Not Laws Of Nature!

by David Brock on March 7th, 2022

Over time, I’ve noticed some striking things about data and how we misuse it. Too often, with the singular exception of Revenue/Quota attainment, we treat data as a goal, or a physical constant/law of nature.

We see this in our own individual and team performance data and in how we respond to research and market data. For example, we look at pipeline data like win rate, average deal value, sales cycle. We view these as constants, let’s just pull some numbers out of the air (they are actually real, but I don’t want to embarrass anyone.). Let’s imagine we have a win rate of 17%, average deal value of $50K, for the moment, let’s set sales cycle aside. We have a quota of $1M.

I see managers misusing data in several ways.

  1. The worst is, managers say, “You have to have a $3M pipeline! Get out and fill it up to that!” These are managers that don’t understand data at all. There is some mythology that pipelines have to be 3X. That’s built on a presumption of a 33% win rate. So we manage people to the wrong goal. Based on a 17% win rate, we need a $5.9M pipeline. So managers really are ignoring the data and what it means for the outcomes we are trying to produce. You may be reacting, “Dave, people can’t be that bad!” Shockingly, this is what at least 30% of the managers I encounter do. They don’t understand the data, they don’t use the data, they just manage by historic rules of thumb, then are astounded when they don’t achieve the desired outcomes.
  2. Another 50% of managers take this data, treating it like a physical constant. The boiling point of water is, theoretically, 100C/212F at sea level–it can be considered a physical constant (Yes, I know some of you will get back to me with the exception). But win rates, deal values and all of what we measure in selling are not physical constants. They are just representations of historical performance. But most managers (look at 95% of SaaS selling metrics) treat this data as physical constants. So we say, “You need to generate a $5.9MPipeline!” We never ask questions like, “What causes that win rate or average deal value? What might we do to increase that? For example, what do we have to about how we sell/who we sell to in order to get a 50% win rate. Or how might we increase the average deal value to $100K. If we started asking those questions, we can drive huge changes in performance (in this case, we only need a $2M pipeline and 20 deals.

Likewise, we misuse research data and results. Again, we treat them like physical constants. For example, I had a discussion with an individual who said, “Gartner says that customers don’t get sales involved until they are 57% through their buying journey!” He went on arguing that sales people should wait to engage customers much later in their buying process. Thank goodness, he didn’t read other research reports presenting numbers ranging from 70-90%.

Or people look at the research data stating, “54% of buying journeys end in no decision made?” They focus their strategies on winning more of the 46% that do make a decision.

Or another, that asked the question, “Since 60% of customers prefer a Rep-Free buying experience, how do we find the other 40%?”

What these people miss is, “What’s driving this behavior or these results? What would happen if we could change it? What would it take to change it.

We should be looking at the research data challenging ourselves with, “How do we change this to better help customers and to improve our own performance?”

For example, looking at those research data points, we might challenge ourselves:

  1. What would happen if we got involved much earlier in their buying process? Could we create more value in helping customers understand the need to change, could that drive a preference for us, could we drive much higher sales performance in identify more opportunities, could we help customers make better decisions?
  2. Or, we should be asking, “What causes the majority of buying journeys to fail? What if we helped more customers successfully navigate their buying journeys? What would happen if we could help more of them succeed and drive the business improvements they hoped to achieve?”
  3. Or, what is it that is driving customers to want these rep-free experiences? Does it enable them to make higher quality decisions? Or is there something sellers need to change, to create a higher value buying experience?

I’ll stop here, you get the point. Virtually all the data we look at is a result of looking in the rear view mirror. It looks at what has happened. Much of the research takes it further, with analysis of what may have caused these outcomes. But we need to challenge the data. We need to understand what underlies the data, and why it produces the results indicated. We need to think about, what might happen if we changed the outcomes? Would it improve the ability for the customer to achieve their goals? Would it improve our abilities to achieve our goals? What things do we have to change, what do we have to do, what might the customer do to improve outcomes, how do we help them?

Metrics are important. They help us understand what’s happening, alerting us to things that may cause us to miss our goals. But they don’t do anything more than that. They are like a red flag, or an alarm. They don’t help us understand the why. They don’t help us think about what we might change, or if we should change. They don’t help us understand what might be or how we can improve.

Too often, rather than thinking about what causes these numbers, we manage to them. As a result, we miss the point and the opportunity.

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2 Comments
  1. Hi Dave,

    I always a[p[preciate your thoughts and comments, perhaps because I always agree. Yet is always fresh and always wise. Keep the material coming!

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