It goes without saying that COVID-19 has significantly disrupted B2B supply chains. Predicting geographic differences and timing amid business closures, adjusting plans depending on uneven demand and inventory positions, and adapting fulfillment methods have been difficult and fluid situations to manage.
In addition to supply chain issues, we’ve witnessed a radical channel shift to eCommerce. At the onset of the pandemic, companies that were equipped to take orders online and had invested in a quality online customer experience, as opposed to those who have not made the effort to get their customers set up on eCommerce platforms, were in a better position to serve customers and potentially gain some wallet share. This difference in capability has been accentuated by the current COVID disruption.
Fortunately, it’s never too late to optimize and expand on the eCommerce offering you have in place today. Keys to success here are rational and market-aligned pricing, relevant product recommendations, inventory availability and speed of delivery.
Though none of what we are collectively going through can be described as normal, B2B companies must move decisively to meet the moment on its terms. Reacting quickly, intelligently and with compassion from a pricing perspective is something companies can still control.
Steps to Use AI and Price Optimization to Price Effectively
A study1 from Enable on the impact of B2B rebate deals, while just one facet of pricing in B2B, revealed an interesting fact: 64% of companies found challenges with their current deal management processes. Certainly, ensuring that pricing is aligned to current market conditions from transaction to transaction is a key element of an optimal, and profitable, deal management process. Let’s jump into how AI and price optimization can help smooth these processes while safeguarding against margin loss.
First, it’s critical to use the data you have and understand it. People are emotional beings, which can impact the day-to-day commercial decisions they make such as what price to quote. Nervous field sales reps may start dropping prices to the floor preemptively or overriding price targets based on fear rather than fact. To offset this tendency, they need guidance informed by data and objective guardrails to limit this behavior.
Take an objective look at changes in recent revenue performance, customer transaction history and selling prices, ideally utilizing comprehensive visual analytics. Make an assessment at each product category level to get granular on which prices need to move down and which can stay at current levels. Both the pricing department and sales team must trade in the blunt approach to pricing in favor of an informed approach.
Second, it’s critical to use AI and price optimization to offset supply chain disruption. While broad market demand and supply chain issues aren’t always directly in your control, B2B companies do have a significant opportunity to spring back financially via more optimal pricing. Through an annual benchmark report2, we analyzed billions of B2B transactions to uncover some key areas that are ripe for improvement. Broadly, companies experience some degree of margin loss – anywhere from 0.5% up to 17.1% – due to a combination of misaligned market pricing, inconsistent pricing and inefficient pricing practices.
This margin loss occurs under normal market circumstances as well as the unprecedented environment we find ourselves in now. What many are finding is that accounting for drastic cost changes, understanding customer willingness to pay, mass updating price agreements, predicting the impact of price changes, and swiftly changing course as the market demands are simply not realistic manual tasks and so they remain undone.
Companies in the best position to respond to these rapidly changing dynamics have tools in place to perform these actions efficiently and intelligently. As input costs change dramatically, pricing teams need to rapidly calculate cost pass-through at scale. They need to determine pass-through strategies at the specific product and category levels while taking into account what prices different customers will bear.
Which products do they want to pass on all the costs for? To stimulate demand, are there some products for which they should only pass 50 percent through? With AI, price optimization and dynamic price management software, pricing teams have the agility to test different strategies, make updates quickly in a centralized hub and accurately predict the impact different strategies will have on revenue and margin.
Additionally, the ability to perform “what-if” modeling allows pricing departments to be proactive. By understanding where customers are historically sensitive and where they aren’t, a proactive price manager can make targeted changes at the category level that preserve margin without decreasing sales.
Conclusion
While 2020 has provided no shortage of supply chain challenges, adopting AI and price optimization can offset negative financial impacts. When equipped with advanced pricing science and software, companies can correct course on the pitfalls of the traditional pricing approach and regain anywhere from 1 to 3% in lost margin.
1Supply & Demand Chain Executive, “Study Reveals Impact of Covid-19 on B2B Rebate Deals,” November 19, 2020
2Zilliant, “2020 Global B2B Industry Benchmark Report, October 2020
About the Author
Barrett Thompson is a General Manager of Commercial Excellence at Zilliant. He leads the Business Solutions Consultant team, aligning Zilliant solutions to customer needs and promoting pricing and sales best practices among customers. Over the past two decades, Barrett has built and delivered optimization and pricing solutions to Fortune 500 businesses in diverse vertical industries including building materials manufacturing and distribution, industrial components manufacturing, semiconductor manufacturing, office-supply distribution, hardware-software distribution, pharmaceutical and medical-device distribution, telecommunications, and multiple travel & transportation verticals. Barrett received both his Bachelor of Science in Applied Mathematics and his Master of Science in Operations Research from the Georgia Institute of Technology.
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