The Science of Selling

I started my sales career in 1989 and the first lesson learned was : sales is all about numbers. Those days numbers essentially meant collecting information on markets, distributors, retailers, competitors and customers. The data thus collected and stored in computers were used for churning out diagnostic reports and answered questions like :

  • Has market share gone up or down?
  • How does the sales trend look like month on month?
  • What about calls productivity? Lead generations? Conversions?

Late 90’s and salespeople were exposed to dreaded terms like CRM. Dreaded? Yes, because that necessarily meant that they collected more micro level data from the market and make sense of customer reports, credit reports and productivity reports in greater details. While all those understanding was more about trends, what went wrong etc, the corrective measures arrived at were largely based on Manager’s own understanding of issues and his/her intuition. Needless to mention that such decisions were accepted without much of buy in and transparency.

Today, we are operating is a much more complex business environment and uncertainty is  all pervasive including selling. One of the ways that sales as a function can wade through these complexities is through better use of data and smarter way of data analysis. I argue that a good sales force is not good enough but a sales force that uses analytics generated insights is the way forward. I am discussing two types of analytics that are of immense value for an insightful sales team. Consider some of the decision dilemmas of a salesperson:

  • How  do i prioritize my sales leads?
  • What are the channels of communication my customers are comfortable with?
  • How much time my customers have for me?
  • How do i customize my offering to a particular customer?

Many of the questions like these can be addressed using prescriptive and predictive analysis which can further help the salesperson to be more productive on several outcome measures such as objective and or relationship performance. The sales management processes can be made more efficient with the help of these tools and several issues related to territory planning, sales force deployment, key account management and sales cycle management.

Getting analytics in daily sales life : Few steps to consider

  • Before you start looking for a consultant, identify people in your team who understand business very well ( Do not care if they know nothing about analytics….). Let this small group come up with questions that are critical to their success and they want these answered in clear terms.
  • With this small group’s ideas, get these issues validated with the team members.
  • Now is the time to look for data scientists.
  • Test the analytics model with live data and share it with the team – let them play with the output and get excited with new insights.
  • Now broaden the scope of analytics to answer questions that sit on the intersection of marketing and sales. Your sales team would love this. After all who does not want to how the buyers are going to buy in future?
  • Contrary to several other consultant’s ideas, my suggestion is that you employ analytics in your sales management decisions only after the sales team has validated and adopted the new paradigm of selling. Once you understand technology, you do not fear managed by the same!

Selling is all about people, relationships, skills and managing emotions. The great thing about analytics that it does not substitute any of these but it simply moderates ( positive) the influence of attitude, behavior, skills and orientations on sales performance. The data science enables better decision making in the daily lives of salespeople and then, they ride on insights, more than just a shoeshine and a smile, to the customers offices and serve them well. Then they pursue the real purpose of selling.

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