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Product Configurator Pricing Strategies

Before the eCommerce revolution, a company could stock specialized equipment and be the sales leader for their industry. The internet has made it easy to find and buy anything quickly and cheaply, so that a company needs to differentiate itself with every aspect of their specialty: industry-specific expertise, support, and add-on services such as advise, training, design, maintenance, equipment bundling, etc. Above all, a company needs to offer their customers personalized or configured products. This offsets their customers’ ability to find an online tutorial and use a have-it-all retailer to buy all the components to create their own custom product.  For example, Nike has created an experience around their shoes. A NikePlus member can access exclusive products, world-class experts, and other benefits. And anyone can configure their own Converse. Nike has embraced their specialty to differentiate themselves.

But there are challenges to offering a product configurator. Depending on the complexity, negotiating the design can take many iterations. Web portals and ubiquitous internet access have eased communication and order process challenges, but then that newly configured product will need a price.

Configurable products are hard to price because a few choices for a few features add up to a lot of unique products. For example, an air cargo shipment will have many traits that describe it. Primarily, the origin and destination (O&D) are the most important. Just considering these two features makes the cargo pricing problem huge because there are thousands of airports. When we looked at spot transactions for an international carrier, many O&D had lots of traffic, but about 95% of the unique O&D combinations contributed about 30% of revenue. That is a good chunk of revenue created by many different products. Since there are many more important factors to a cargo shipment, such as trip time, day of week, and service level, you can see how a configured product can be millions of different products.

Since a product configurator can have a lot of choices, each product to quote is like a “brand new” product. That is, it will have scarce or no history. In some industry verticals there may be similar products, but not enough transactional history to help make a robust, new price recommendation by looking at what something similar had sold for in the past.

One product configurator pricing strategy is to set the price for each feature. Returning to our air cargo example, imagine pricing each origin airport, destination airport, each service level and then summing these to quote a request. This approach can miss the value added by having a desirable complement of features for a product. Certainly, a shipment leaving Friday night (i.e. last chance to ship for the week) is different from one leaving Friday morning or Monday night. As a result, this approach will require complicated rationality rules to capture those interactions.

Instead PROS proposes a nearest-neighbor pricing strategy approach where we use similar products’ price history considering important features while respecting data sufficiency. This tackles the sparse data issue and lets us leverage sales history to inform our pricing strategy. So this approach can help price new and slow moving products too.

We begin by defining the configurable product families. Each product family includes products that share fundamental feature(s). For example, a truck rental company has a lot of different trucks. An 18’ box truck is probably used for small local moves and deliveries while a tandem axle day cab is meant for commercial use and hauling more. Each of these would be its own product family. To take it a step further, perhaps the 18’ box truck’s most important pricing features are customer’s geography and whether there is a lift gate, but the engine model and customer’s industry are most important for the tandem axle day cab. We expect each product family to have its own list of significant attributes that influence price.

This pricing method goes beyond using a price list to accommodate products that don’t fit into neat, narrow categories with abundant transactional history. Check out PROS Smart CPQ to learn how to start creating configurable products for your customers.

About the Author

Amy Bock, Lead Scientist at PROS, partners with businesses to build B2B pricing science that achieves precisely targeted profitability. She has an M.S. in Industrial Engineering from Texas A&M University and a B.S. in Systems Engineering from the University of Arizona.

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