Data is the New Sales


Marketing has transitioned from a cost center to driving revenue, and they are using data and predictive analytics to do it. Historically, organizations have focused on the marketing side of data, seeing it primarily as a better way to target and communicate with the masses.

Few organizations have extended data driven predictive analytics to the sales force. For those that have, however, the resulting increase in sales productivity and reduction in cost of sales have been significant.

Disrupting Sales through Insights: Data by the Numbers

  • The average cost per sales call is $400 (and for technology products 2X – 4 legged sales calls)*
  • The average, sales rep spends 80% of their time qualifying leads and only 20% closing. *CRM Magazine.
  • 30% of B:B contact data goes bad in less than a year, according to D&B.
  • McKinsey estimates that by 2020 customers will manage 85% of their relationship with an enterprise without interacting with a human.
  • B:B customer will use six different channels to interact with an organization throughout the purchase process.

Looking Ahead: Data is King in 2017

One case study dramatizes the increased efficiency and lowered sales costs that are benefits of using predictive analytics. To evaluate sales productivity, we created a productivity profile. This is simply a model that breaks down a company’s total annual revenue by factors such as the number of sales calls annually per sales rep, the average revenue per sale; and the close rate, the ratio of annual sales to annual sales calls.


The productivity profile shows how, by making even small improvements in a key variable in the profile, predictive analytics can dramatically increase the close rate and the bottom line. One metric of sales productivity is the number of sales calls or other contacts to close an account. Reducing calls per close becomes a key variable that can be influenced by using predictive analytics to identify and rank the best prospects. However, the significance of the improvement in qualifying target accounts went beyond the reduction in the number of calls to close. Not only were target accounts more qualified with fewer calls from sales reps, but prospects were also more receptive for the awareness and interest phases.

Companies have a lot of data internally and its full potential is often not realized, but it is in increased productivity and lower sales cost that data science and predictive analytics will make the greatest contribution to an organization’s bottom line.

The result: more closed sales with less effort.

A sales propensity model was created, and it alleviated two problems:

  1. Tendency of sales reps to invest too much time with low potential accounts.
  2. Need to spend time searching out and evaluating good prospects rather than selling.

The ability to categorize and rank accounts and prospects by potential allowed the company to focus their selling efforts on those with the highest probability to close and use less expensive email and phone follow up on those with a lower propensity. This predictive effort greatly reduced the cost of getting a sales rep in the door, and produced higher quality leads with a reduced number of calls to close.

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