Artificial Intelligence technology is already in our everyday lives; for example, speech recognition in Alexa, Siri and Google Assistant all leverage AI algorithms. We readily accept that AI influences consumer sales, promotional offers, and product recommendations. But did you know that AI is also gradually disrupting the relationship-focused arena of business-to-business selling?
Here are three examples:
A tale of pricing inefficiency:
You may not realize it, but you’re buying something that’s already been sold. Whether online or in store, when you’re purchasing goods for the home, the original purchase from the manufacturer was from another business before it got to you. The commercial sales business of a leading packaged goods company was experiencing flat revenues and declining margins. When examining their customer agreements, it was observed that discounts were all over the map; in some cases, large-volume customers were paying much more for a SKU than small customers were — in some cases, these smaller customers were enjoying steep discounts.
More alarmingly, several high-volume SKUs were being sold to some customers well below cost, not as a loss leader, but by mistake. It didn’t take AI to show that this was bad business and was hurting their profits! The solution? Applying AI-based analytics and segmentation, grouping customers based on past buying patterns to provide market-driven price ranges for each SKU and for each customer, taking into consideration their propensity to buy within the confines of the broader competitive market.
But AI was just getting started. Was price pushing the buyer away after it reached an unprofitable level for the company? Substitutes were offered up across the breadth of the product catalog, providing local sales teams with flexibility to meet customer needs without selling at a loss. To accelerate sales, regional and local sales teams were enabled to immediately close deals within recommended ranges. This resulted in drastically fewer exceptions, making the sales teams exponentially more efficient during the average close time from weeks to minutes! Deal win rate increased along with average deal volume. Not surprisingly, total margin dollars in the business unit increased dramatically.
AI-driven pricing applications in this industry are tightly coupled with sales workflow tools, especially Salesforce.com. However, there is a separate pricing analytics application to guide the salesperson through the negotiating process. In this industry, mobile capability is extremely important as negotiations often occur in person with the account executive interacting with a tablet or mobile phone.
Mad Men are yesterday’s news—or are they?
When you think of advertising sales, images from the TV show Mad Men including long, two-martini lunches and handshake deals may come to mind. While it’s true that smaller digital buys can be done online, the big purchases are still often done with a drink and a handshake as it continues to be a relationship-based business. Today, a large proportion of the advertising you might experience on the web, television, and radio were priced and presented in-person with the aid of AI.
For leading media companies, the base rate for a spot is called the “rate card” and while Jon Hamm’s character may have had his in ink on a notecard, today’s companies have dynamic rates from computed-based AI algorithms that factor in marketplace data, ratings, historical rates, and available inventory/views.
Many larger ad purchases (aka “ad buys”) are developed and priced using sophisticated AI algorithms. These algorithms incorporate customer behavior and market intelligence and seek to optimize the efficient use of inventory while increasing reach for the customer — everyone wins! The recommended proposal and price are available to the account executive, who has some discretion to change parameters of the deal as he or she sees fit to close. AI technology is accessed through the front-end of custom sales and traffic systems for the largest media conglomerates, or through integrated sales and traffic solutions provided by companies like WideOrbit. As more and more media companies have already invested in this technology, those who have not are increasingly at a significant competitive disadvantage.
Is there room at the Inn?
For most of us, our experience with purchasing a hotel room seems very far-removed from B2B sales. But for some of the largest hotel enterprises, the individual segment represents only 50% of bookings or less. While numbers vary widely by hotel, negotiated rates for large companies who have a large traveling workforce (such as Accenture or IBM) represent approximately 20% of bookings. The remaining 30%? You guessed it: conventions/groups.
Does AI price groups the same as individuals? Of course not. AI factors in not just sleeping rooms, but also function space requirements of the group, calculates bookings on hand, forecasted future bookings (which may be at a higher rate), and provides a proposal with real-time pricing based on these factors.
Oh, no! Another group took your desired date! Time to search for an alternative. AI to the rescue once again, taking into consideration room rate, catering fees, function rooms and even opportunity cost of accepting the group. What other group and individual bookings will it displace? An AI algorithm incorporates all these considerations along with models of group behavior to recommend pricing for the group. This recommendation is presented in a negotiating tool that allows the sales person to adjust the terms in real-time and see the effect on net profitability.
What about efficiency? As with other AI-powered B2B pricing capabilities, the group sales person is empowered to close the deal without further review, provided the pricing is within an allowable range. This AI pricing technology is typically accessed via existing group pricing workflow software. Some larger hotel enterprises have custom applications, while others use packaged software such as Oracle Corp.’s Opera Sales and Catering or Amadeus Hospitality’s Delphi.
These examples illustrate how AI is powering better pricing and deal structures in B2B sales. AI can recommend pricing and deal structure at an account level at near-real time. Sales professionals still make up an indispensable component, but now they are armed with better information to negotiate effectively and profitably.
B2B sales analytics is not about charging higher rates; AI-recommended rates are often, in fact, lower. It is about efficiently winning the deal while earning a more profitable margin.
Jon Higbie, Ph.D., holds a doctorate in management science and is chief science officer at Revenue Analytics Inc., a provider of technology and consulting services in revenue management and price optimization.Favorite