Why AI and Price Optimization Are A Perfect Match

Gartner recently published a short whitepaper in which they ranked 14 separate uses cases for Artificial Intelligence (AI) in B2B sales. As they reviewed each scenario, they built a matrix that included a scoring rubric with the following criteria under the two headers of business value and feasibility. 

Business Value:

• Cost efficiency  
• Revenue Growth  
• Business Viability  

Feasibility:

• Technology Maturity  
• External Organizational Factors  

There were several great business-critical use cases they reviewed, including sales forecasting, lead discovery and customer lifetime value analysis. But when it was all said and done the use case that ranked at the top of the list was Price Optimization. In each of the five criteria price optimization received the highest possible scores.

Now I am not going to place myself in the same company as the good folks at Gartner in my abilities to evaluate trends and opportunity in technology. They are the soothsayers with the magic crystal ball and I’m lucky if I can keep track of what day of the week it is. That being said, it just so happens that I wholeheartedly agree with their assessment, which I’m sure they will be relieved to know.  

Gartner built an incredibly well thought out matrix and certainly had a panel of experts weigh in on each point. My endorsement comes from having held what I think is a unique cross section of roles in B2B. Specifically, having been a pricing director, a sales leader and finally a technology business partner...not all at the same time of course! 

Having done rotations in each of those roles in a large B2B distribution company I believe it gave me a specific perspective around the challenges each of these positions face. I will attempt, as someone who worked on the ground, to highlight some real-world examples of why I think Gartner hit the nail on the head.   

Pricing Director

Every year the challenge would get thrown down from senior leadership: “We need to get margin expansion!” Every year I would of course respond with, “Yes Sir/Ma’am.” At which point I would turn to my wonderful team of genius spreadsheet jockeys and ask, ”Where the heck are we going to find it this year?” There comes a point when you have used all the tricks in your bag, passing through cost (plus), while controlling for contractual obligations.  

When you have hundreds of thousands of products across a myriad of market segments, trying to understand customer and product price elasticity becomes an exercise in futility. This is where AI is tailor-made for B2B pricing. Using the power of data science-driven algorithms, technology has the capacity to evaluate hundreds of millions of possible permutations while simultaneously controlling for key business rules. It allows for a much more surgical approach to the problem, versus the traditional, “Let’s throw as much price against the wall as we can and see what sticks.” Additionally, by using the power of AI for more predictive/prescriptive prices, the likelihood of being able to reproduce those results year-over-year becomes exponentially higher.  

Sales Leader

When I made the move over to leading a selling team, I and my peer group of intrepid sales managers would on a semi-annual basis have to stand tall before senior leadership and play, “Defend your life!” We would have to review our account base, our growth strategy, our team and then ultimately our profitability. When, for whatever reason, we would perhaps be showing margin decline, the directive from the powers that be was always, “Sell the value and/or your reps should know their business and be able to demand a price premium ... show the differentiation!” To which I would dutifully respond again in kind with, “Yes Sir/Ma’am!”  

I had an amazing sales team: experienced, smart, enthusiastic. They never failed to amaze me. They did know their business and they understood their customers. But, like any other B2B salesperson,  they didn’t know the prices their competitors were charging, and they couldn’t be expected to know the best price point for the entire 800,000 SKU portfolio. This is where the power of AI technology to develop scientifically-derived customer segmentation, recognize trends in real-time that leverage the totality of the company's transactional data and deliver specific guidance down to sales reps when they need it is a game changer in B2B.  

Technology Business Partner

After spending considerable time in the commercial part of our organization, I thought I’d try my hand in the operational side of the business. I know a thing or two about technology and I have a background in the business. I’ll just take it easy and become an IT  business partner to the sales ops group. My delusion lasted about two hours before (again) I got pulled into a meeting with senior leadership and they said, “We need to innovate! We keep reading about this AI and Machine Learning stuff. Go out and find or build us a reliable platform that delivers at least a 10-15x ROI for our investment!” To which, as you might have guessed by now, I replied, “Yes Sir/Ma’am.” 

When you scour the landscape of applications that use AI and machine learning  you quickly realize there is a lot of smoke, but you need to really dig in to find the fire. The potential is enormous, but is hard to find a partner with a long track record of using data science to deliver insights and even harder to quantify the potential financial benefits. This is again where price optimization has a distinct advantage. There are several (none of course quite as good as Zilliant) price optimization technology companies out there that have been around for 20+ years. There is enough of an installed customer base that you can find real-world feedback on the effectiveness. More importantly, price improvement has a very specific and measurable ROI. Improved pricing drops directly to the bottom line.  

Conclusion

If you take one thing away (besides the fact that I pretty consistently capitulated to senior leadership) it’s that each part of the business has different demands. But in the end, driving specific measurable results will always be a requirement. I think every large company in the world recognizes that investing in innovative technology is part of staying competitive. From my experience, AI-driven price optimization is as good a bet as you can make. So, if your company hasn’t explored this area, it’s probably time to take a look  – but don’t take my word for it – just ask Gartner!  

If you’re interested in how Zilliant can help your company determine if price optimization technology is right for you, send me an email at michael.naughton@Zilliant.com or connect with me on LinkedIn.