Vendavo Taps Into AI For Dynamic Pricing

The practices behind dynamic pricing often include a mix of hard data from market forces, coupled with anecdotal factors and personal relationships between buyers and suppliers. A new tool from enterprise software provider Vendavo aims to put hard data behind the buyer-supplier relationship in an effort to enhance the dynamic pricing strategy.

Last week, the company announced the rollout of PricePoint, an intelligent dynamic pricing solution that deploys artificial intelligence, sophisticated analytics and machine learning. Taking into account customer segmentation, geographic location, local and global market factors and more, the solution makes pricing suggestions for B2B vendors that can boost margins while maintaining customer satisfaction.

In its announcement, Vendavo pointed to recent data from Ventana Research, which found only a quarter of businesses agree that the use of spreadsheets is timely and accurate in the dynamic pricing process. According to Vendavo vice president of product management Alex Hoff, dynamic pricing is traditionally a manual task with some guess work involved.

“Maintaining up-to-date global pricing and actually operationalizing those strategies is hard work for every pricing and product team,” he said. “The task complexity and constantly changing market conditions keep many of us up at night. But when you consider the substantial profitability improvements the right price delivers, it’s critically important.”

In a recent interview with PYMNTS, Vendavo’s profit evangelist, Mitch Lee, said the buyer-supplier relationship is a critical component of the pricing strategy, yet one that is more difficult to put numbers to. Artificial intelligence can help B2B suppliers make smarter decisions on pricing when the relationship with their corporate customers is at the forefront.

“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.”