It was when Andres Reiner, CEO of Houston, Texas-based pricing optimization vendor PROS, started talking about the supply chain that I began to understand the scope of his company’s proffer.
After all, optimizing margins by using data to set prices seemed like something any business should be doing; and if outsourcing the number-crunching to PROS made sense, fair enough. But as Reiner drew a broader picture for me, over breakfast at Dreamforce 2015 last month, it became clearer that the needs PROS can address for its clients are by no means peripheral. There’s the impact on sales teams, for example. As Patrick Schneidau, PROS CMO, told me in a follow-up call, “The key point, what we’re trying to accomplish, is to make it easier for the sales rep to sell.”
But it’s fair to say that PROS and its product have deep roots in the pricing game. The business was launched, in fact, in the wake of airline deregulation in the United States, which happened way back in 1978. There was an obvious use case in crunching data to set attractive prices for a necessarily finite inventory: seats on planes. (More than 50 percent of passengers in the airline space are still using PROS technology, said Schneidau.)
It wasn’t until the late nineties that PROS began to diversify, offering price optimization tools to manufacturing and service industries. As recently as 2013, it acquired Cameleon CPQ, a configure-price-quote solution which helps enhance sales efficiency by putting PROS’ big data to work for sales reps.
Big Data Sets Margins
The foundation of the PROS price optimization and revenue management solution is a very big stack of data. There are three main sources:
- Historic transactional data;
- Historic and current operational data; and
- Market data–from commodity indices to data on fuel costs, and even the weather.
If you know what people expect to pay for a product (demand), how much is available or how quickly it can be produced (supply), and the over-arching market conditions, you should be able to figure out how much to charge. And if you’re basing your estimates on deep, data-driven insights, rather than hunches, you can set an ideal “right price.”
“The easy part is the data,” Schneidau said. “People typically have it already. What they can’t do is apply data science or predictive analytics to it. We look across the market at the prices which have historically won–and at what drove the pricing. It’s based on those attributes that we can determine whether the customer would pay more or less.”
What’s more, a single “right price” is not really what a sales rep, engaged in real world negotiations, needs. As Reiner explained to me, “You can’t give a salesperson $2.95, because they’ll say ‘Why not $2.96’?” Instead, PROS focuses on three pricing levels: not just the “right price,” but also a ceiling and a floor. This orientates sales reps to an optimal pricing spread–and if they cant close the deal on the product of interest, PROS helps them suggest alternative products. And crucially, there’s no need to send price adjustment requests back to head office for approval: the sales team know the spread already.
The example of Hewlett-Packard, a PROS customer, is relevant here. The problem faced by the sales team was that it took way too long to get pricing exceptions approved–as much as a week or two–giving competitors an opportunity to undercut. PROS helped deliver pricing intelligence in real time, leading not only to an increase in the number of deals closed, but also to better margins and more sales opportunities.
The Supply Connection
It’s data-driven predictions that pull the supply chain into the picture, allowing, Schneidau said, “an alignment between product, price and availability.” New Zealand’s Fonterra, another PROS client, is the world’s largest exporter of dairy products. Obviously, the commodity it deals in is milk, but the form the milk takes can vary widely–from whole and skim, to yogurt, to butter, to a range of nutritional formulas. PROS helps Fonterra not only set price, but predict demand, putting the solution at the heart of its manufacturing operations.
The B2B applications of PROS are clear, but there’s an eCommerce product too–adopted, for example, by insurance companies as well as airlines. CPQ software, customized to brands and specific needs, can be embedded in web portals or online catalogs, providing data-driven price quotes in real time.
Finally, although traditionally an on premise solution, PROS is transitioning this year to a “cloud first” strategy. Whether the “as-a-service” option makes sense for each customer, said Schneidau, depends on the use case.
