Data Informed: The Wisdom of Crowds: Using Big Data to Optimize Pricing

March 26, 2015-

By Doug Fuehne

There’s no denying that big data is a big deal to sales teams and executives. Consider that every minute of the day, Facebook users share nearly 2.5 million pieces of content, nearly 300,000 Tweets are posted, email users send more than 200 million messages, and Google receives more than 4 million search queries. Buried in that mountain of data – from credit-card transactions, frequent-buyer points, cell phone calls, website clicks and emails – lies important, game-changing value. The volume and velocity of this data is growing at unprecedented speed, but for companies that successfully respond to the challenge and opportunity of big data, the result can be a series of actionable insights that can have a significant impact on win rates, deal sizes, and sales-cycle length. According to a study by PricewaterhouseCoopers, those who described their companies as proficient in demand analytics estimated that they outperform their industry peers in sales, margin, and profit growth by more than two times, and show an eight-times better total shareholder return on capital.

How does that happen? It’s like the principles that James Surowiecki identified in his landmark book, “The Wisdom of Crowds,” that describes how large groups of people can collectively come up with precise answers to different questions. Big data gives us an opportunity to aggregate information from diverse, disparate, and independent sources and replicate “crowd wisdom” in a marketing context – particularly pricing information. This is a question that’s ideally suited to the “wisdom of crowds” because, fundamentally, what is a price if not the collective judgment of a free market?

In many companies, data warehousing is the foundation of reporting and analysis initiatives, and that’s a strategy that does deliver some value. Sales reps can find answers to basic questions like, “What industry is this customer in, how large is their business, and in what region they’re located,” but it leaves unanswered the most important question in any sale: What is this particular prospect truly willing to pay? Getting the answer to that question requires finding important correlations among a much wider variety of factors tucked in piles of data from inside and outside the organization.

Pricing analytics seeks to define thousands of customer segments based on commonalities among hundreds of independent variables. By identifying correlations based on rich, deep data histories, companies can make smarter decisions and pursue targeted sales, marketing, and pricing strategies that have a much greater likelihood of success. And that means more revenue and faster sales cycles.

How Big Data Helps Answer the Key Sales Question

Using a range of sophisticated statistical analyses – as well as breakthrough IT infrastructures equipped to plow through and process petabytes of data – data scientists are now unlocking the hidden value of big data for more effective sales and pricing strategies. They are delivering crucial guidance right at the moment of transaction. They are tapping into ERP systems, CRM histories, competitive analyses, third-party econometric data, subscription sources, and much more. They’re pursuing the best answer to the key question: “What is the maximum amount this prospect is willing to pay?”

That analysis starts with an effective segmentation strategy, scanning and rescanning vast mountains of historical data to create “peer groups” of customers and prospects who demonstrate similar purchasing behaviors that are meaningfully predictive. But this doesn’t mean simply lumping prospects or customers into rudimentary “A,” “B,” and “C” buckets. Instead, it means creating hundreds, thousands, or even tens of thousands of narrowly and sharply defined peer-group segments defined by dozens of variables such as company size, industry/NAICS codes, states, ZIP codes, previous order frequency, average order size, preferred provider status, shipping preference, market competitiveness, sales channel, preferred payment method, D&B credit score, time spent on website in the past 30 days, number of telephone calls received in past six months, and more.

For example, suppose you are an office supplies retailer. Just one peer group segment could be for consumers based in Massachusetts with at least a six-month history of purchases who make purchases every 30-40 days using the website. Their preferred credit card is Visa, they have accepted one upsell recommendation in the past year, they spend an average of only 10 minutes per month on the company website, and place an average of eight calls to the customer-care center every six months.

With the volumes of data that are usually available, this kind of segmentation can give rise to data sparsity problems. But with big data, even a massive number of segments can be populated with enough data to derive statistically significant results. We can verify the value of these dimensions with simple analyses and assemble them into a predictive model. This lets sales and marketing tap that crowd wisdom more effectively and formulate optimal pricing levels and strategies.

The Biggest Bang for Your Buck

It’s tempting to assume that big data-driven predictive models are valuable to only direct-sales teams pushing big-ticket complex sales. In reality, big data is having the biggest impact in sales and marketing organizations with two key characteristics: a large number of SKUs – in the tens of thousands – and a network of horizontal customers that spans industries and geographies. In direct sales of high-priced items, sales teams have fewer transactions to manage and can devote resources to individual pricing analyses. By contrast, in distribution organizations, margins are typically quite thin, and the power of pricing is often magnified.

Another strategic advantage of data-driven pricing analyses comes from speed. As the saying goes, “Time kills all deals.” Win rates can increase significantly – without sacrificing margins – by presenting the optimal price as early in the sales cycle as possible. By avoiding the protracted, outdated negotiation tactics near closing time, sales teams can avoid disruptions and delays.

For sales and marketing organizations seeking a surer footing as they work to optimize pricing, the best path may be to tap into the hidden power of big data and gain new insights into the factors contributing to price sensitivity.

Doug Fuehne is vice president of strategic consulting with PROS (NYSE:PRO).