I think it was Thomas Jefferson who said the immortal words, “Not all analytics are created equal.” Or something like that.
And he (or whoever it was who said that) couldn’t have been more right!
For someone just diving into sales analytics and business intelligence, it can be tempting to treat all analytics as the same. Those neophytes will soon realize that analytics can’t be treated with a one-size-fits-all blanket approach. In fact, what separates the best organizations and data analysts from the others is their understanding of these nuances, and how and when to apply each.
There are four distinct types of analytics, none of which you can survive and thrive in your sales management role without. Here’s a breakdown of each:
1) Descriptive – What HAS happened?
This is the simplest type of analytics, a straightforward description of what happened. After all, you can’t begin to fix problems or come up with solutions if you don’t even know what you’re dealing with to begin with. All of your business intelligence analysis should fundamentally start with identifying what happened, by asking questions that lead you to answers and realizations like:
“The sales pipeline generated by our team of outbound Business Development Reps was significantly smaller than it was last quarter.”
“We were 25% short of hitting our sales quota this quarter.”
All of those are matter-of-fact descriptive statements, merely telling you what is and what happened. Consider descriptive analytics an update on your state-of-the-world, a very surface level analysis. This might seem basic, but descriptive analytics are an essential starting point – you have to first identify the problem or issue before you can go about solving it and asking tougher questions.
2) Diagnostic – WHY did this happen?Knowing “what” isn’t enough; sales managers and general problem solvers need to understand why it happened, so that they can take corrective action. This branch of analytics is what really excites decision-makers at organizations, giving them real actionable and difference-making insights. Figuring out why something happened leads to answers like:
“We made a bunch of new hires to our BDR team and took too long to onboard them; we expected to have a fully ramped up team by this point but only half the team was ramped up and generating pipeline at full capacity.”
“We had 5 big opportunities, well above our Average Selling Price, that we expected to come in, based on the intuition of the closing reps working them. They all ended up becoming Closed-Lost or pushing to next quarter.”
Once you have dived into the data to determine the root cause of your problem, you can then begin correcting it.
3) Predictive – What COULD happen?It’s time to study the data and gaze into the crystal ball and answer, “What could happen in the future, if we either stay the course or make significant course corrections?” This isn’t simply a blind guess, a stab in the dark. Rather, these are predictions based on a variety of data-driven factors, including historical precedents, present conditions and other related variables.
“We’ve seen that fully ramped up sales reps who consistently hit the activity goals we’ve set for them, and with at least average activity efficiency ratios, will produce enough output to fill the sales pipeline.”
“Using a combination of historical- and pipeline-based sales forecasting, we can see that larger deals close at a smaller rate. We should pay special attention to these larger opportunities in our sales forecasts.”
Looking into past data for patterns and trends can accurately inform you as to what could or should happen going forward. This is extremely helpful in planning, setting reachable and realistic goals and girding your expectations.
4) Prescriptive – What SHOULD happen?Ah yes, the main reason why CEOs and sales managers are so unabashedly data-driven and -focused these days. They believe in the power of data to diagnose their problems, predict the future and then tell them what they should do to improve upon this future, their future. No sales manager in their right mind could possibly turn down data that powerful.
Of course, data isn’t an infallible crystal ball that can tell you tomorrow’s winning lottery numbers. What it can do is highlight your problems, help you dive into why those problems occurred, and use a mix of historical trends and present-day variables to predict future outcomes. It’s on you to take those data-found and data-backed factors and put them together to create a prescription specially for your business and its problems. That leads to observations and realizations like…
“Once these reps are ramped up – per our training and sales coaching plan – working at team average activity efficiency conversion ratios and hitting regular activity input goals, we should see a full sales pipeline again next month.”
“We need to watch out for red flags in our sales forecasting, like larger opps or those with little recent momentum. We also need to stop relying so heavily on our reps’ intuition when it comes to closing opps, and use historical and pipeline analysis instead.”
These prescriptions were arrived at after first considering what happened, why it happened, and what could happen if certain things were changed – all backed by data analysis. One doesn’t work without the other – all four types of analytics must be effectively used in concert in order for an organization to truly extract real value from data analytics and business intelligence.