When I was in sunny California last, I had a chance to sit down with Rapt and talk about their rather unique solutions that revolve around pricing strategy, decision analytics, and price optimization that, when combined, can help a company maximize their revenue opportunities.

Rapt’s sophisticated software platform, that integrates more statistical, analytical, and optimization algorithms than you can shake a stick at, was designed to uncover the many complex supply, demand and price relationships that, when harnessed, predictably improve profit and market share. Unlike simpler modeling tools and platform, Rapt can break down products, or SKUS, into features and analyze the impact of each feature on demand. This is one of the reasons why their solution is becoming popular in high-tech.

Let’s say you have three laptops, the Pinta, the Nina, and the Santa Maria, and each are selling quite well. However, like all electronics today, their life-cylce is limited and you need to design your next generation laptop. Each has a different processor, CPU, hard drive, display, and battery life. How do you determine the best configuration for your new laptop? Rapt’s forecasting engine can integrate your historical sales data with marketplace data, analyze the sales patterns and trends at the feature level, determine which features (CPU, hard drive, etc.) are the most popular, determine how much each feature influences the overall sale, and tell you which combination of features would sell the best in a laptop. You can then use it’s Price Director solution to determine the optimal price-point for your product. This product contains advanced algorithms that work on order, inventory, and market data to extract the elastic and cross-elastic effects among products, their attributes, and consumer demands which it can use to determine the optimal price points for revenue or market-share optimization.

However, one of the most interesting facets of our discussion centered around the fact that the largest uptake in their rather unique solution offering was not in consumer goods industries, but in media, and new media in particular. MSN, Yahoo!, CNET Networks, NBC Universal, The Weather Channel, and MTV Networks, among others, all use Rapt’s solution to determine how to price their advertising, which is defined by high variability in demand, uncertain availability of supply, and the rapid innovation and evolution of medium capabilities. If they can tackle one of the most challenging pricing problems out there, surely they can be helpful in more traditional industries. But then again, many companies in these traditional industries most likely have not yet adopted decision optimization in their award process, should-cost modeling in their product design process, or advanced spend visibility solutions in their strategic sourcing process. All I can say is that … the technology’s finally here, let’s start to use it!

These days it seems like everyone is focussed on cost savings. This is not a bad thing, considering the vast majority of companies are not best-in-class, which means the vast majority of companies, on average, are probably spending too much on their purchases. But despite some vendor claims that revenue is, and will remain, flat, or that there’s nothing you can do about it since the market sets the price and constitutes the demand, this is not true.

We all understand that the fundamental goal of business is to make money, or profit, and we all learned the same calculation in our first business class: Profit = Revenue – Cost. This tells us that, as a business, there are two levers we can manipulate to increase profitability, Cost and Revenue. Now it’s true that we as sourcing and procurement professionals have a lot more control over cost then we do on revenue, but that does not mean our focus on cost should be myopic. We should also understand the revenue side of the equation and work with marketing on the pricing side of the equation, because neither the market price, the highest price marketing predicts they can get, nor the price at which demand (or consumption) is maximized is the optimal price.

If your goal is to maximize profit, the optimal price is the one where the profit equation is maximized, and this means this price is determined as much by cost as by revenue, and we all know that the cost for a product is not fixed – it depends upon the supplier we use (which determines a host of physical attributes such as quality, appeal, etc.) and, more importantly, the quantity we order. Generally speaking, the cost per unit will decline if we order more units, but this is usually only true to a certain point. Each supplier has a base capacity they can produce on their production lines during their regular hours of operation. To exceed this capacity they will have to add shifts, add lines, or both – which will increase the cost per unit. Or if your product requires a raw material in short supply, costs will increase as you try to divert supply away from your competitor, and there will be a point where you just will not be able to secure more material.

Is marketing, or if you’re big enough, product pricing, going to understand all of the factors that contribute to product cost – and, if so, are they going to understand the factors and inter-relationships as well as we do? Probably not. And that’s why sometimes we need to get Rapt up in revenue – to make sure that not only does the organization choose a price-point that theoretically achieves their profit, margin, or market-share goal (which, without our assistance will probably be based on cost-data that is only an approximation, and not necessarily a good one), but that the price-point is realistic and that the forecasted demand can be met in the intended time-window.

Furthermore, as the users of some of the most advanced analytic and business intelligence tools in the organization (spend analysis, cost modeling, and decision optimization, for example), we are much more likely to understand that our historical data alone is not necessarily sufficient or accurate enough to predict future demands, that different product features and price-points will have a considerable impact on actual sales, that costs can vary significantly by feature and demand level, and that the only way to analyze all of these variables and make the best pricing decision is to use a good decision support tool based on sophisticated analytics and optimization to model the different scenarios at different price points and obtain a true picture of feature – price point – demand level correlation.

And that’s why tomorrow I will introduce you to Rapt, a decision analytics and price optimization solution provider whose gaol is to help companies maximize their revenue opportunities.