In 2003, Shaw Industries, a Berkshire Hathaway company that produces and distributes carpeting and flooring, was determining the prices of its myriad products largely by relying on the intuition of its sales staff. Management had set a long-established ladder of pricing for salespeople to refer to, but that was essentially where the guidance and oversight ended.

The inevitable result? Very irregular pricing., says Tim Baucom, a vice president in Shaw’s commercial division. Sometimes salespeople asked more than the market would bear, and it resulted in a missed opportunity. Sometimes they were overly aggressive and left money on the table,” he says.

Part of the problem was that Shaw had thousands of stock-keeping units. Further, about 20% of Shaw’s orders are custom made, which added to the complexity. It was, in a word, “chaotic,” added Baucom.

In 2003, Baucom and some of his colleagues made a phone call to an up-and-coming pricing-analysis-and-optimization company called Zilliant, which claimed to bring clear insight into pricing using patented analytical science. Baucom and his team then spent 18 months exploring Zilliant and contacting its existing customers in the construction industry.

In 2005, Zilliant began customizing models from Shaw’s transactional data, a process that took about six months, plus three months to train the staff how to use the tool. It was a lengthy and work-intensive process, Baucom admits. Still, he adds, it was also worth it.

Price-optimization software, says Chris Fletcher, research director at Gartner’s customer relationship management (CRM) analysis team, takes every piece of available internal and external data, from spreadsheets to enterprise resource planning systems, to create individual pricing models designed to maximize profits and deter salespeople from excessive discounting. A Gartner study he authored in 2012 found that companies that implemented price optimization successfully “realized improvements of 2 to 4% of total revenue or more, and the ROI of the price optimization investment is typically realized in less than two years.”

But actually deploying price-optimization software is harder than it sounds, says Fletcher. “Sales teams, as we know, are mostly compensated on revenue, not margin,” he says. “They like to have levers they can pull, and, frankly, pricing and discounting is one of those levers.”

As a result, they have a tendency to ignore what price-optimization tools are trying to tell them. Fletcher also tells businesses to be prepared for a lengthy implementation process. He suggests starting with a pilot test of one or two products within a geographic area before attempting to get sales staff on board. “Use it to learn where data is and see what kind of results you get with it. Once you can show proof of concept, you’ll get support from senior management to proceed.”

Baucom concedes that change management was somewhat of an issue for Shaw at first. “But this is a tool for management and leadership, and I think once they understood what we were trying to help them do, which was help them sell as much product as they could for as much as they could get for it, they were OK with it.”

And while having the data Zilliant spits out doesn’t guarantee they’re going to land a profitable sale, Baucom believes his sales staff is more likely to land a better deal than if it relied only on intuition. “It keeps them from either charging too much or walking away from what would have been a fair deal.”

Indeed, Fletcher tells his clients they have to have a carrot and a stick when getting their sales teams on board. “The stick is that they have to use the tool because management is going to be looking over their shoulder,” he says. “The carrot is increased revenue. We’ve found over time that sales teams staying compliant with pricing guidelines [recommended by the software] reduce the amount of rogue discounting that used to go on.”

In that way, the analysis generated by the software “causes an uplift in topline revenue,” contends Fletcher.

Since deploying a price-optimization initiative, Shaw’s price-determination process is no longer chaotic. Shaw’s salespeople input the information about a particular lead, including the type of construction project, its size, and particular products, into its CRM system. Zilliant’s software application then infuses itself into whatever CRM system the company is already using. Then the firm’s algorithms analyze the data to recommend a price to shoot for based on three scenarios: a normal pricing situation, a discount price for a competitive situation, and a deeply discounted price.

Salespeople retain their right to operate at any price point within those extremes. “If they feel like it needs to be even lower, then they have to justify a price below that and get management approval. Managers, in turn, can see how each individual salesperson is performing based on the number of won and lost projects, and what price [was] used to successfully land a deal,” Baucom says.

To be sure, Shaw is a global enterprise, with more than $4 billion in annual sales. Small-to-midsize enterprises were, until fairly recently, left to sift through mountains of siloed data and trust the intuition of their salespeople.

Founded in 1998 as an on-premise software solution, Zilliant for years sold its products to large enterprises through traditional up-front licenses and annual maintenance fees. “The negative to that is that our solutions were very expensive,” says chief executive officer Greg Peters. “The average deal cost $3 million to $4 million for large enterprise software installations. We were profitable, but we were not getting as many new customers as we would have liked.”

Then about three years ago, says Peters, as the cloud matured and concerns about the security of data housed there came about, Zilliant switched to a software-as-a-service business model. Now Zilliant costs a fraction of what it did, and comes in the form of an annual fee companies with revenues of $300 million a year or more can more easily afford. (Shaw, too, has begun using the company’s SaaS model.)

That translates to tens of thousands of new prospects for Zilliant. And, Fletcher says, the time until the average price optimization is fully implemented has moved from 12 to 18 months to less than 12 months. As a result, the adoption rate of price optimization is increasing, he says.

Fletcher believes price-optimization software is on course to reach mainstream adoption in two to five years. “When there are concerns about the economy, that’s when products that help you increase margin and sales efficiency really get to see a lot of interest,” he says.

“All of the vendors have done a better job making products simpler to use and easier to implement.” In general, the SaaS software market is expected to increase 25% this year to $59 billion, according to Forrester. In 2014, the market is expected to total $75 billion. That’s good news for Zilliant and companies that offer price-optimization software as a service, such as Pros and Vendavo.

It may be good for their customers, too. CFOs are aware, says Peters, a former CFO himself, that pricing is one of the last bastions of guesswork in the enterprise. “A lot of times people don’t understand how the end price even happened, and yet it has a direct impact on margins,” he says.

Leave a Reply

Your email address will not be published. Required fields are marked *