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This post is the latest in our series on new technologies impacting industrial manufacturers and distributors. Earlier posts have included IIoT, 3D Printing, Generative Design, 5G, Artificial Intelligence, Digital Marketing with CAD, Blockchain, Digital Twins and this one is on Pricing Optimization. As we’ve seen with other new technologies, they are often combined to create solutions – in this case pricing optimization systems use machine learning (ML) from artificial intelligence and configure, price, quote (CPQ) systems including visualization and digital marketing with CAD.
Per Wikipedia, price optimization “is the use of mathematical analysis by a company to determine how customers will respond to different prices for its products and services through different channels. It is also used to determine the prices that the company determines will best meet its objectives such as maximizing operating profit.” In the B2C world, we’ve all been increasingly subjected to it for decades, think of airfares you purchased, hotel rooms you’ve booked, or rental cars you’ve arranged. More recently have you noticed how the prices of goods on amazon.com varies? How about your insurance costs?
In transitioning from B2C to B2B, pricing optimization has to adapt to 3 major differences:
You probably already have different pricing for different customers based on negotiated contracts, volumes or strategic partnerships, and those prices are displayed when those customers logon to your eCommerce/EDI system. Maybe you have different online catalogs as well as different pricing for different customers. We’re not talking about breaking that but rather about using technology to increase average selling prices and particularly profitability. For example, B2B buyers are usually more sensitive to pricing on frequently rather than infrequently purchased items. Therefore it can be advisable to hold prices down on frequently purchased items or for specific components of a configured product where price comparisons are readily available but use demand-based pricing on other components or services.
Typically they fall into 4 categories:
Various consultants track this market in order to provide guidance and advice to their cients. For example, IDC’s Worldwide B2B-Focused Price Optimization Applications 2018 Vendor Assessment ranks suppliers’ as the graph below illustrates and Gartner’s March 2019 Market Guide for B2B Price Optimization and Management Software describes price optimization and management software as an example of AI/ML that can deliver substantial business benefits. Their guide helps companies decide whether this technology is appropriate for their organization and covers these suppliers: Apttus, Brennus Analytics, Perfect Price, Periscope By McKinsey, Price Edge, Price f(x), PROS, SPOSEA, Vendavo, Vistaar Technologies, Vistex, and Zilliant.
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