In this special guest feature, Neil Biehn, Ph.D. of PROS examines the three pillars for how to use data to increase the odds of closing sales. Neil Biehn, Ph.D. is Vice President of Science and Research at PROS Holdings, Inc. (NYSE: PRO), a big data software company that helps customers outperform in their markets by using big data to sell more effectively. For the past 15 years, he has researched pricing, revenue and profit optimization, and the underlying data science.
According to Computer Science Corporation, by 2020 data production will be 44 times greater than it was in 2009. Today’s enormous data acceleration continues to grow in complexity and at exponential rates, and it’s no secret that organizations are trying to figure out how they can utilize their data to deliver real business value.
Early on, the term big data meant focusing on the infrastructure. However as time has passed, organizations have begun to realize it’s not about the infrastructure but rather how data is analyzed and used to make smarter decisions. The real value comes from infusing science into the data with automated predictive and prescriptive analytics. As business has become more complex, prescriptive and predictive analytics are quickly becoming essential for driving enhanced revenue performance. Organizations can use their data to make intelligent, better informed sales decisions about how to price products and deliver a better customer experience. Bottom line: smarter is better.
The Three Pillars: Predict, Prioritize, Prescribe
With the onslaught of big data there are new opportunities to take a data-driven approach to finding the hidden value and to pursue the highest probabilities of achieving a desired outcome. In fact, without the right tools, the odds of winning on a “pass” bet at the craps tables in Las Vegas are 49.29 percent, higher than the predictability of a sales person closing business, according to research from CSO Insights. Here are three pillars that are sure to increase your odds:
By scientifically grouping customers into segments, sales teams can deliver significantly more value if they can successfully predict which prospects are most likely to buy right now. Imagine a salesperson tapping into data that statistically correlates their prospects’ geographic location and industry, with their propensity to buy. The odds of meeting quota just increased.
How can salespeople use their time most wisely to meet their quotas? Is it in existing or new accounts? And which prospects offer the highest probability of winning deals and keeping a satisfied customer? With the right answers from their data, those salespeople have a far greater chance of making their numbers.
To make smarter decisions about their priorities, salespeople need new ways of looking at information to understand which accounts to target, what prices to negotiate, etc. Unfortunately, the vast majority of sales reports are retrospective, and that approach is no longer sufficient. In fact, it’s just like driving forward in a car while looking solely into the rear-view mirror – looking at where you’ve been, not where you’re going. Sales teams need information, reports and analyses that offer prospective prescriptions about where to allocate their precious time and resources. Data science embraces predictive and prescriptive analytics to formulate optimal pricing.
Shifting from Hunches to Finding Hidden Value
In today’s competitive marketplace, helping companies and their salespeople connect seemingly disparate forms of data to make smarter, more informed and more agile business decisions is where the real value resides. When sales teams can begin taking advantage of data to predict what will happen, they can often tilt the playing field by shifting the emphasis from hunches to real business value that helps them adjust and optimize their sales and business strategies. Ultimately, those who use data to make smarter decisions will be the biggest beneficiaries. They’re the ones who understand that smarter is better.
Sign up for the free insideBIGDATA newsletter.