Finding the Hidden Value in Big Data: Smarter is Better

February 4, 2015 Neil Biehn

By Craig Zawada and Neil Biehn, Ph.D

Many organizations are grappling with how they use data to deliver real business value. It’s no easy task with today’s massive data acceleration that continues to grow in complexity and at exponential rates. According to Computer Science Corporation, by 2020 data production will be 44 times greater than it was in 2009.

While a lot of the early hype of big data centered on the companies providing big data infrastructure, the real test comes with how data is used to make smarter decisions. For the most astute, the value comes from infusing science into the data with automated predictive and prescriptive analytics. In fact, as the art of doing 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 smarter, more informed sales decisions about how to price products and deliver a better customer experience. Bottom line: smarter is better.

Sales teams – from the bag-carrying account executive to the senior vice president – are under pressure to deliver results. So what exactly does that mean? The answers vary. For fast-growing companies, the goal may be to increase top-line sales. For companies in mature industries, the objective may be to protect gross margins by limiting discounts or giveaways. Maybe the goal is to prevent customer churn or increase cross-selling. Or maybe it’s all of the above, which means different sales programs, targets, training, compensation plans, reports, measurements and more. Ultimately, it’s about using data to be smarter about your business.

The Three Pillars: Predict, Prioritize, Prescribe

With the advent of big data – think petabytes that are constantly updated and expanded – 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.

  • Predict

By scientifically grouping customers into segments, sales teams can deliver significantly more value if they can successfully predict which prospects – whether they’re new or existing customers – are most likely to buy right now. Imagine a salesperson taps data that statistically correlates their prospects’ geographic location and industry, with their propensity to buy. The odds of meeting quota just increased.

Consider a medical products distributor that had no view on when its customers were increasing or reducing purchases, or the volumes of either. Using its own data – with predictive and prescriptive analytics – the company was able to predict future purchase patterns simply by looking at the size of the customers’ product portfolio. With information-at-hand about the breadth of products customers were purchasing – and how it was changing – the distributor was able to more accurately predict customers that were churn risks and those most likely to grow their business with the company.

  • Prioritize

With just so many hours in the day, 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.

A high-tech B2B service provider used a third-party data aggregator that identified 22,000 companies as potential sales targets, a number far too large for the size of its sales team. Using its own data – and predictive lead and account scoring – the company narrowed the list to just 500 organizations. The company then created precision peer groups that predicted the probability of purchase in those micro-segments, starting with the most likely to buy. By the end of the next two quarters, companies on the list comprised more than half of its pipeline.

  • Prescribe

To make smarter decisions about their priorities, salespeople up and down the chain of command need new ways of looking at information to understand which accounts to target, what prices to negotiate and more. Unfortunately, the vast majority of sales reports are retrospective, and that approach is no longer sufficient. In fact, it’s akin to 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.

The marketing team of a specialty fasteners distributor continuously developed promotions for its sales team, with the intent to grow the business. The sales team was uncertain which accounts they should target and which products represented true incremental opportunities with customers. Using predictive analytics and a peer-group analysis, each sales rep was provided a list of prioritized accounts based on the likelihood of incremental sales, as well as specific products to upsell to customers. Armed with this prescriptive guidance, the sales team grew sales by more than $20 million.

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 – sometimes dramatically – 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.

Neil Biehn, Ph.D., is vice president of science and research at PROS, Inc. For the past 15 years, he has researched pricing, revenue and profit optimization, and the underlying data science. During his tenure, Biehn has designed pricing algorithms and optimization models for companies in the manufacturing, distribution, services, travel and transportation industries. He is a published author in a variety of formats that include scientific journals and white papers, and is a contributor to the recently published book titled Innovation in Pricing: Contemporary Theories and Best Practices.

Craig Zawada is Chief Visionary Officer at PROS, responsible for creating and articulating the vision for how PROS uses big data and the latest technology to help companies drive incremental sales growth and profit improvement. Prior to joining PROS, he was a partner and leader in the marketing & sales practice at McKinsey & Company. Zawada co-authored both the first and second editions of “The Price Advantage,” which has been recognized as one of the most pragmatic books available on pricing strategy. As a frequently sought-after presenter, Zawada has spoken at conferences around the world on next-generation sales and pricing improvement strategies.

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