For most sales executives, predicting quarterly sales targets and understanding underlying reasons for performance or non-performance relies on a combination of previous sales result observations and gut instinct or business judgment. A little science, a little art.
All too often, using the past to predict the future does not provide accurate results, especially in such dynamic market environment. Ask any investor.
When it comes to assessing people in an organization, the outliers are easy. It’s the ones in the middle that are challenging. Which sales representative did a better job: the one that closed two deals in a small region or the one that closed five deals in a large region? When these same sales representativesreport on their region’s softening economy, trade imbalance or the relative strength of the local currency, these factors can start to sound like a load of excuses. But are they?
One of the early lessons in statistics class is that data must be normalized if accurate comparisons are to be made. To compare sales representatives in different regions, all the regional differences (economy, trends, TAM, etc.) must be normalized as well as all the specific sales representative specific productivity data (months on job, previous experience, training received, etc.). It’s a vast undertaking that few do comprehensively. As a result, we end up giving more weight to things that may—or may not—be especially important.
“Human intuition is real, but it’s also really faulty,” said Andrew McAfee, an MIT scientist and author studying how technological progress changes business, the economy and society. “Here’s a simple rule for the second machine age we’re in now: as the amount of data goes up, the importance of human judgment should go down.”
McAffe suggests it’s more than time for us to turn many of our decisions and predictions over to the algorithms. “There’s just no controversy any more about whether doing so will give us better results,” he said. His article in the Harvard Business Review (9 Dec 2013 https://hbr.org/2013/12/big-datas-biggest-challenge-convincing-people-not-to-trust-their-judgment/) cites numerous examples of how data-driven decisions are outperforming panels of experts.
The problem, he says, is that “most of the people making decisions today believe they’re pretty good at it, certainly better than a soulless and stripped-down algorithm.” Yet ask anyone who has done A/B testing of web pages: “Going with your gut” can leave money on the table. Results speak for themselves.
On beyond algorithms
Short of hiring an army of statisticians, a new type of Advanced Sales Analytics capability comes to the rescue. What used to take dozens of disconnected software packages (and licenses) with database connectivity and syncing issues, can now be implemented to complement an existing system.
Advanced Sales Analytics capability offers statistical algorithms and models to make sense of the relevant Big Data streams, combined with proven techniques for data mining, and an ability to generate data visualizations for executives -- revealing patterns, trends and correlations that might otherwise go undetected. In short, seeing the future more clearly.
Better forecasts, earlier notice of opportunity slippage and a better understanding of your team’s skills and performance are all part of the package. The results are faster, more reliable and repeatable sales forecasts and accurate personnel insights, that beat “the gut.”
It will take time for businesses to become truly data driven. We have to dislike the results of our subjective, uninformed decisions enough to want to change. McAfee’s prediction: “Data-dominated firms are going to take market share, customers, and profits away from those who are still relying too heavily on their human experts.”
We are seeing signs of it already happening in the tech industry. Is your company prepared for the coming shake up? Or are your experts always right? Let us know in the comments.
(About the author: Sanjay Shitole is an analyst at Capgemini)
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