The science of sales should be left to software

Today is Tuesday, 12th December 2017. I’m on my way to work and on my way to the beginning of, what promises to be a day when I could get through some of those items that have been languishing on my to-do list. My daily sales status meeting with my team at 9am starts in a few minutes. It is a conference call. Most of my team dial in from the field.


Our team sales call goes well – I get a status update on the deals they have been working on and the plan of action for the day. A few of them are yet to send me their daily sales reports. I look at my watch, I have exactly 60 minutes before the sales management call with my manager, in which time I have to receive these sales reports, collate them and send them to the boss. This is going to be tight.


I look at my to-dos for the day. After the call with my manager, I have a meeting with Chestlaw Associates, one of our major clients. I have to go there along with Maya, my top performing team member. This meeting, along with the travel is going to take about 3 hrs. I should be back in office in time for lunch. Post lunch I have to attend to some of the customer issues that have been escalated to me by my clients. After that, I plan to have a meeting with Allen. This is supposed to be a sales coaching conversation, followed by a joint client meeting. Allen has been struggling with his closures lately. He has filled out the sales coaching template I had created and has sent it to me. We had scheduled this conversation couple of times but had to cancel at the last moment. After this, I have a con call with our sales operations team followed by a late evening catch up with another client over drinks. The day sure looks fun!


The day progresses as planned. The boss is happy with my updates and the meeting with Chestlaw resulted in a couple of new opportunities. Maya has managed this client so well. I come back to my desk after lunch and all hell breaks loose. I get a call from my manager saying the board meeting this morning resulted in us coming up with a new sales forecast with a different set of assumptions. No sooner than I briefed our sales analytics team on this new sales forecast, I got a call from Niel, saying there have been some issues with one of the large opportunities we were pursuing and needs my help navigating the new sales situation. I somehow finished helping Niel and the forecasting exercise, and I notice on my to-do list that there are 2 other customers I need to help out. Looks like I might have to push out my meeting with Allen by an hour. But that was not to be. My boss gets back with more changes to the forecast. I tell Allen to go on the sales call without me. My meeting with the sales operations team for the revised sales forecasts overshoots by an hour by which time I’m slightly late for my meeting with the client.


My day finally ends. I’m Sean – Sales Manager. I did manage to accomplish most of my to-dos – planned and unplanned, except for a very important one – the sales coaching session with Allen. I hope to do this meeting someday.


If you are someone like Allen, I’m sure you can relate to the disappointment that he must have experienced. If you are someone like Sean, you can totally empathize with the chaos in a sales manager’s day.


However, this is not how it should be in today’s AI age. Lot of the sales coaching that Allen needs, can be delivered using analytics and algorithms. Things like KPIs at risk, prioritizing opportunities, risks in deals and what needs to be done to mitigate it, best sales tactics, which leads to work on, key focus areas for conversion, cross-sell and upsell opportunities, troubleshooting poor performance, are examples that can be automated using rules, algorithms and if you have sufficient and accurate data, using machine learning models. The same is the case for Sean – machine learning can help accurately forecast sales and revenue, detect outliers and identify deals that will not easily close. And these models can be very accurate and very helpful. A study conducted by Accenture and published in MIT Sloan Management Review reveals that 38% of early business adopters “credited machine learning for improvements in their key performance indicators for sales — such as new leads, upsells, and sales cycle times — by a factor of 2 or more, while another 41% created improvements by a factor of 5 or more.


Clearly, software is taking out the guesswork in sales and doing a pretty good job of it. The science of sales should be left to software. The art and the witchcraft of sales should be left to humans.

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