What you measure is what you value. But what if you don’t know what is valuable – how will you know to measure it?

Data is relative. Numbers alone don’t mean anything. 2/10? that means nothing. If you get into an accident 2/10 times you drive that’s a risk you’ll want to avoid. If you win the lottery 2/10 times you play, that’s a reward you’ll want to repeat. If the risk and return are understood relative to the baseline, you can make educated decisions. However, relativity, risks and subsequent gains are all based on baselines. Baselines you establish after you’ve spent a certain amount of time measuring something. There are 2 main problems with that.

One, what if you are measuring the wrong things?

Second, what if your initial numbers were flawed?

 


Point 1

What if what you are measuring isn’t the most critical factor to your sales team’s success?

Let’s say you’re optimizing for a number of opportunities and looking to increase your close rates? In theory that should lead to greater revenue, however, what if increasing the average deal value reduces the close rate. You’ll optimize for the wrong thing and likely never capitalize on your market potential. Because, in certain situations certain clients have a greater appetite for larger service offerings. Could that revenue amount potentially eclipse the revenue from more clients at a lower rate? These are the types of questions you should be asking yourself, before you set your KPIs and goals. One of the ways to avoid this pitfall is to know what your true north star metric is. What do you want? More clients? More revenue?. Once you understand that, you need to determine three unique ways to get to that north star metric and optimize the outcomes. You need to forecast multiple scenarios optimizing for different outcomes and see which one gives you the highest likelihood of maximizing your north star Now understand this, simply doing this doesn’t guarantee you’ve unearthed all the nuance. This is just step one.

Point 2

Domain experience should influence your benchmarks.

What if your initial batch of results were too low because the control group you used was wildly unprepared and had terrible performance. You wouldn’t know that because you don’t have the data to prove they were terrible. Now, your baseline numbers are reflective of poor performance and you’ll only ever look to optimize it. You pigeon hole yourself with data constraints and now you optimize from a bad place. When I speak to a sales person that has a 90% close rate, my initial gut reaction is to look at how many prospects they have disqualified. When close rates are that high you don’t have enough opportunities at bat and aren’t utilizing the right tools. When a sales person says they have a 2% close rate, my initial thought is to look at who you’re speaking to and focus the conversation. Trying to be all things to everybody results in a serious waste of time. I would look to determine the true value prop of what it is you’re selling, who it is most likely to benefit, how do we position it in a way they want it, and look to speak to those people. Data tells you where something is, it is up to you to understand whether that positioning is right or wrong and in what context.

Conclusion

Data only gives you a certain part of the picture. To truly take that next step and differentiate yourself to create the biggest gap between you and your competitors, you have to find value in the unmeasurable.  If it can’t be measured, no one else is measuring it. If it can’t be measured, no one else is looking at it. That is your competitive edge. You will inherently separate yourself from the pack, because if it could be measured, everyone else would already be doing it.

Most organizations have a very linear approach to problem solving. Previous solutions inspire future solutions, creating a very derivative and unimaginative strategy. They do this because it’s safe, because they have a framework of how they can measure progress, but like I said, true separation comes from doing something different. The only thing that is different in today’s marketplace are things that cannot be measured, because your competitors are afraid of investing in something they cannot tangibly track. The organizations who are brave enough to make decisions based on their domain experience, irrespective of what a data scientist tells them, are the ones that make the biggest strives forward. True change comes from anticipating what your market needs, before the data supports it.

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