Putting A Price Tag On The Buyer-Supplier Relationship

The power of Big Data is turning the buyer-supplier relationship into a measurable metric in B2B eCommerce. Everything from geographic location to on-time payment history can affect that relationship, and subsequently the contract between a corporate buyer and vendor.

Price-setting is a delicate process for suppliers, especially lower-margin, smaller vendors in the supply chain. Historically, these businesses would use anecdotal evidence and arbitrary factors to set prices with their corporate customers, resulting in a lack of consistency. This strategy fails to optimize vendors’ relationships with buyers, and misses opportunities to find the “sweet spot” of product pricing: identifying the most a customer is willing to pay while still maintaining a positive customer experience.

Organizations aren’t blind to these pitfalls: Bain & Company research published this year found that 85 percent of professionals admit their pricing practices could improve, with analysts pointing to tailored customer pricing as a key aspect of improved pricing performance.

In that effort, Big Data is coming into the world of dynamic pricing, said Mitch Lee, profit evangelist at Vendavo, and it’s doing away with arbitrary price fluctuations in favor of evidence-based decisions.

“On the B2B side, there is much more of a relationship between a buyer and a seller,” Lee recently told PYMNTS, discussing the differences in dynamic pricing approaches between the B2C and B2B eCommerce world. “Though it may seem transactional, both the buyer and seller have more at stake in the long run.”

In B2B sales, the customer experience is a critical component of turning inquiries and requests for pricing into transactions, he said. In its simplest form, dynamic pricing is about a vendor setting the price of a product based on what a particular customer is willing to pay. In reality, however, the process is more nuanced – and indeed, factors less easily measured, like relationship history, make a significant impact.

For instance, a vendor may see an increase in the cost of raw materials, a trend that would typically drive up the cost of its end product. But a supplier may choose not to increase prices as much as market pressures would recommend, because it is more beneficial in the long run to ensure the satisfaction of a longstanding customer.

According to Lee, technologies like artificial intelligence are now able to turn those scenarios into decisions backed by data, not just anecdotal history.

“In B2C, there are many consumers for each supplier,” he noted. “On the B2B side, that’s a much smaller number of relationships between suppliers and customers, and you don’t want to mess any of those up.”

Technologies that accumulate troves of transactional data, mixed with data from market conditions and competitors, offer vendors the ability to adjust prices based both on external market forces and internal relationships with business partners. Looking beyond basic customer segmentation – pricing for a customer based on their industry or region, for instance – means analytics tools can help a vendor account for additional factors when landing on a price.

“There are all kinds of attributes that suggest different customers have different willingness to pay,” said Lee. “Harnessing AI on the back of transactional data and distributed computational power can tease out information about other attributes that aren’t necessarily shooting up in product family, or geographies, or customer classifications.”

At its core, dynamic pricing is about boosting profits and margins, of course. And according to Lee, implementing a data-driven approach to dynamic pricing can quickly yield not only cost savings, but also hard-dollar additions to the top and bottom lines.

Last week, Vendavo announced the rollout of PricePoint, a tool that integrates artificial intelligence into the dynamic pricing process. When announcing the solution, Vendavo pointed to statistics from Ventana Research, which found that 73 percent of businesses rely on spreadsheets to analyze sales data, though only 25 percent say spreadsheets are timely and accurate. AI and data analytics tools, Lee noted, are able to take into account data based on local and global price lists, geographic regions, customer segmentation and beyond.

But like many aspects of corporate finance, Lee said dynamic pricing still requires a human element. Technology can recommend pricing based on numbers and facts, but human experts can take into account those anecdotal factors that are similarly important when making pricing decisions. It’s part of the overall picture of B2B eCommerce and the buyer-supplier relationship, providing clients with a positive experience across channels while keeping a pulse on market conditions and competitors. Sometimes, that can mean holding back from hiking prices even in times of material scarcity, Lee said, for the sake of maintaining a beneficial, long-term relationship with a customer.

“When you think of scarcity and what happens to prices, that’s one of the things algorithms don’t always do well with,” he said. A machine learning-powered solution will identify scarcity of products or materials and recommend increased prices as a result.

“Present that information to a human in the decision-making process,” said Lee. “There are some things you just can’t put into AI. There are some things that have the nuances of a long-term relationship in B2B eCommerce.”