RESEARCH & RESOURCES

Q&A: How Big Data Can Drive Optimal Pricing Decisions

Setting the optimal prices for your company's products or services is vital to your firm's financial health. We explore big data's role in setting pricing

Pricing is a vital part of any enterprise's business, but how can big data play a role in maximizing your pricing decisions? Oscar Moreno is senior vice president of products for PROS (www.prospricing.com), a company that develops profitability management and optimization software solutions. In this interview, Moreno sheds light on how big data can help drive optimal pricing.

BI This Week: We know that enterprises are analyzing "big data" to extract and maximize the information contained within it. Your firm specializes in prescriptive pricing and revenue management software. What, exactly, is this type of pricing optimization software? What kinds of data do you analyze and how does this help set prices?

Oscar Moreno: PROS predictive and prescriptive capabilities provide actionable pricing recommendations to customers that can be implemented in existing operational systems such as ERP, CRM, or sales force automation. We extract big data from these systems and from other parts of an organization to create a pricing optimization platform. This platform helps companies -- and the people who work for them -- apply statistical analysis, operation research algorithms, distributed processing, and real-time capabilities that provide recommendations in a pragmatic, user-centered approach. These new capabilities enable enterprises to outperform in their respective markets.

PROS software is based on a powerful configuration framework that uses diverse datasets to achieve price optimization. In the travel industry, we use data that includes passenger name records, availability, network configuration and competitive fares, to name a few. For manufacturing, distribution and services customers, we encounter more diversity in datasets, which include sales transactions, customer master and material master data, market indexes, competitor prices, inventory and rebates.

What are the benefits pricing optimization offers? How does it relate to big data?

First and foremost, consider the complexity of today's business environment. Companies face extraordinary business challenges, from the massive volatility in raw materials and input costs to currency fluctuations and a globally competitive marketplace. The selling environment is equally complex. Between competitive pressures and aggressive procurement departments, salespeople can no longer rely on experience and gut instinct to close business. Add to these scenarios the fact that business operates at an unprecedented pace, which sheds light on why pricing optimization is so critically important.

Companies have made significant investments in ERP, CRM, and other sales force automation systems to manage their businesses more efficiently. What's missing here is the big-data component -- how they extract data from these systems and from other parts of the organization and put it to use in their companies to make smarter business and sales decisions.

That's where the benefits of price optimization come into play, adding benefits that translate into top-line improvements:

  • Analyzes a large number of price points and permutations that derive data-driven pricing recommendations specifically targeted toward individual customers

  • Uses unbiased data science decision-making for every product, customer, and micro-market

  • Provides a data-driven approach to the sales force, which enables it to negotiate with confidence to drive price and margin uplift, price consistency, and price rationality in complex environments with multiple channels, millions of products, and thousands of customers

Let me offer a couple of big-data industry examples that offer an even more acute view:

  • In the manufacturing industry, we've seen that bringing real-time pricing and quoting capabilities to their sales forces creates a big uptick in customers' abilities to constantly learn and adapt from new transactions, new prices and competitive moves.

  • In the travel industry, airlines and hotels around the world have used big-data solutions for many years. They operate with large and diverse datasets, from reservations and booking systems, to competitive price information that's mined and analyzed in a distributed processing framework.

How can companies use real-time big data to acquire the speed and agility for sales organizations to execute transactions?

The key for sales teams is to have real-time data. Remembering the volatility in raw materials and currency fluctuations, companies require deep insights into the factors that drive profitability. That means pricing technologies must operate in real time to accommodate today's business complexities. PROS, for example, typically handles 1,000 pricing decisions per second, which can easily expand to manage higher volumes by adding more compute nodes. To put "real time" in perspective, Twitter averages about 2,000 tweets per second.

Imagine a company that has three distribution channels, one million products and one thousand customers. If we consider no other variables, at any given point in time there will be 3 billion potential sales scenarios. Real-time pricing optimization software provides unparalleled agility to execute in these environments. Despite the complexity, sales organizations can make massive real-time price decisions without having to worry about pre-computing all the possible decision points. Even better, with big data supporting their negotiations, they regain the confidence to know how to price profitability for their organizations.

Big data can help you set prices, but determining the effectiveness of those pricing decisions requires lots of additional analysis. You have to analyze competitors' pricing, the impact of any of your own pricing changes, and market reaction. That's a lot of analyzing and complexity.

One of the most compelling reasons to adopt price optimization software is the ability to make effective price decisions in real time. Remember that price changes in many organizations are made annually, bi-annually, or even quarterly. For many, it's a burdensome, extraordinary task that's managed every minute between pricing cycles. That dynamic simply doesn't stand the test of today's business environment based on the volatility and fluctuations customers face.

The context around each price decision is constantly changing, with new competitive prices, changes in sales volumes, other negotiated price points for the same product and customer segment, and new inventory or supply positions, among other factors. There's no perfect price point, and every price decision adds information that impacts the business. The key is to have tools that rapidly learn from these collective results. Pricing optimization software adapts this new knowledge, improves models, and produces new recommendations, which reduces these cycles from quarters to months, days or hours, and brings agility to the enterprise while dealing with the behind-the-scenes complexity.

Organizations face a broad set of challenges when it comes to pricing and big data. Can you provide an example of what those challenges might be and how big data and pricing can help a company.

The data complexity found in most B2B sales environments requires big data solutions to arm sales people with the tools they need to sell with confidence. Price guidance is a technique that delivers an envelope of recommendations to the sales person, and hides most of the complexity associated with big data and price optimization. The envelope typically includes three price points -- floor, target, and expert -- that provides an effective mechanism to make price optimization operational, and still allows some freedom for sales people to apply their unique expertise. We've seen great success with the adoption of price guidance, which delivers the right price recommendation at the moment the transaction is negotiated.

Price guidance uses scientific segmentation to create peer groups or segments. Once segments are identified in a scientific and unbiased way, statistical analysis is used to calculate win-rate probability for each transaction. By combining segmentation, win-rate probability, business rules, and various data sources, you empower your sales force with the confidence and knowledge to win.

How is pricing optimization a part of traditional business intelligence?

Traditional business applications focus on visibility and usability of corporate data. Big data applications SUCH AS price optimization represent the next-generation of intelligent business applications that are both predictive of outcomes and prescriptive in recommending a course of action. These big data applications are also able to answer key questions that in our opinion fall under the business intelligence umbrella.

Let me give you an example of these questions:

  • How do I sell more products to an existing customer?
  • Can I extract a higher price for this specific transaction?
  • What customers are at attrition risk?
  • On which customers should I expend more time?
  • Are any of my products and segments chronically underpriced? If so, how do I deal with them?

The ability to answer these questions with big data insights helps organizations respond more quickly to their customers, and also enables them to drive great sales volumes and increase revenue more profitably. It's not unlike using a GPS -- with business intelligence, you have information; by contrast, with pricing information, you have instructions and guidance yet still retain control of the decisions you make to get to your destination.

Without necessarily naming a company, give us an example of what pricing optimization can do for an organization.

A U.S.-based electronics distributor came to PROS when its existing pricing and margins faced extraordinary pressure during the economic downturn. The company had experienced declining margins for some time based on constant price erosion faced by electronics distributors. Margin compression was particularly strong at the time. This electronics distributor provided price recommendations to its sales team, who had little confidence in the data. As a result, the sales team consistently discounted products without the targeted pricing information they needed when theirs customers called for quotes. These pricing practices also created an institutional mindset that eroded profitability since the sales team used pricing based on previous negotiations.

After implementing PROS, this distributor's sales team had pricing guidance targeted directly for each specific customer within a peer-group segment. The amount of business they quoted below floor price decreased by 10%, and they increased closed business above the sales target by 10%. Overall, the distributor experienced margin increases of 230 basis points during a time of declining profitability in its industry segment.

What are the industry trends that are driving the need for pricing optimization?

Based on our experience, we're seeing a number of trends affecting profitability in companies, which clearly indicates a need for pricing optimization:

  • Increased pressure on financial performance

  • Increased access to internal and public data

  • Technology that allows for real-time processing, responses, and distributed processing

  • Ever-changing market conditions means B2B pricing decisions must anticipate future opportunities that bring business agility into the sales execution process

  • Provide companies with price consistency and fairness across product sets and customer segments, even when managing billions of potential price points

What products or services does PROS offer in relation to pricing optimization?

PROS Holdings, Inc. offers prescriptive pricing and revenue management software for companies in the manufacturing, distribution, services, and travel industries. PROS gives customers confidence and agility in their pricing and sales performance strategies by providing big-data-driven insights into transaction profitability, forecasting demand, recommending optimal prices for each product and deal, and streamlining pricing processes with enhanced controls and compliance. Our company has implemented over 500 solutions in more than 50 countries.

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