Why AI and Price Optimization Are A Perfect Match
Gartner recently published a shortwhitepaperin whichthey ranked14 separate uses cases for Artificial Intelligence (AI)in B2Bsales. As they reviewed eachscenario,they built a matrixthatincludeda scoring rubric with the following criteria underthetwoheaders of business value andfeasibility.
External Organizational Factors
There were several greatbusiness-critical use cases theyreviewed,includingsales forecasting, lead discoveryand customer lifetime value analysis. But when it was all said and done theuse case that ranked at the top of the listwasPrice Optimization.Ineachof thefivecriteriapriceoptimization received the highest possiblescores.
Now I am not going to place myself in thesamecompanyasthegoodfolks at Gartnerin my abilitiesto evaluate trends andopportunityin 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, itjust so happens that Iwholeheartedlyagree with their assessment, whichI’m sure they will be relieved to know.
Gartnerbuilt an incredibly well thought out matrix andcertainlyhada panel of experts weigh in on each point. Myendorsementcomes fromhaving held what I think is a unique cross section of roles inB2B.Specifically,having beenapricingdirector,asalesleaderandfinally atechnologybusinesspartner...not all at the same time of course!
Having done rotations in each of those roles in a large B2Bdistribution companyI believe itgave me aspecificperspectivearound the challenges each of thesepositionsface.I willattempt,assomeone who workedon the ground,tohighlightsomereal-worldexamplesofwhyIthinkGartner hit the nail on the head.
Every year the challenge would get thrown downfromseniorleadership:“We need to getmargin expansion!”Every year I would of course respond with,“Yes Sir/Ma’am.”At which point I would turn to my wonderful team of geniusspreadsheetjockeys and ask,”Wherethe heckarewe going to find it this year?” There comes a point when you have used all the tricks in yourbag, passing through cost (plus), while controlling for contractualobligations.
When you have hundreds of thousands of products across a myriad of market segments,tryingto understandcustomerandproductpriceelasticity becomes an exercise in futility. This is where AIis tailor-made for B2Bpricing. Using the power ofdata science-driven algorithms,technology has the capacity to evaluatehundreds of millions ofpossible permutations whilesimultaneouslycontrolling 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 wallas we canand see what sticks.”Additionally,by using the power ofAIfor more predictive/prescriptive prices, the likelihood of being able to reproduce those results year-over-year becomes exponentially higher.
When I made the move over to leading a selling team,I and my peer group of intrepidsalesmanagers would on a semi-annual basis have to stand tall beforeseniorleadership andplay,“Defend yourlife!” We would have to review our account base,our growth strategy, our team and then ultimately our profitability.When, for whatever reason, wewould perhaps be showing margin decline, thedirectivefrom the powers that be was always,“Sell the value and/or your reps shouldknow their business and be able to demand a price premium...show the differentiation!”To which I would dutifully respond again in kindwith,“Yes Sir/Ma’am!”
I had an amazingsales team:experienced, smart, enthusiastic. They never failed to amaze me. They did know theirbusinessandthey understood their customers. But, like any other B2Bsalesperson,theydidn’t know the prices their competitors were charging,and they couldn’t be expected to know the best price point forthe entire800,000SKUportfolio.Thisis where the power ofAItechnologytodevelopscientifically-derivedcustomer segmentation, recognizetrendsin real-timethat leveragethe totality of thecompany'stransactional data anddeliver specific guidance down tosales reps when they needitisa game changer in B2B.
Technology Business Partner
After spendingconsiderable time in the commercial part of our organization, I thoughtI’dtrymy 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’lljust take it easy and become anITbusinesspartner to thesalesopsgroup. Mydelusionlastedabouttwohours before(again) I got pulled into a meeting withseniorleadershipand they said,“We need to innovate! We keep reading aboutthisAIandMachineLearningstuff.Go out and findor build usa reliable platform that delivers at least a 10-15x ROI for our investment!”To which, as you might have guessed by now, I replied,“YesSir/Ma’am.”
When you scour the landscape of applications that use AIandmachine learningyou quickly realize there is a lot of smoke, but you need to really dig in to find the fire. Thepotential isenormous,butis hard tofindapartner witha long track recordof using data science to deliver insights and even harder to quantify the potential financial benefits.This is again wherepriceoptimizationhas a distinct advantage. There are several (none of course quite as good as Zilliant)priceoptimizationtechnology companies out therethat havebeen around for 20+ years. There is enough of an installed customerbasethatyou canfindreal-world feedback on the effectiveness. More importantly, price improvement has a very specific and measurable ROI. Improved pricing drops directly to the bottom line.
If you take one thing away (besidesthe factthat Ipretty consistentlycapitulated toseniorleadership)it’s thateachpart of thebusiness has differentdemands. But inthe end,driving specific measurable results will always be a requirement.Ithink every largecompany in theworldrecognizesthat investing in innovative technologyis part of staying competitive. From my experience,AI-drivenpriceoptimizationis as good a bet as you can make. So, if your company hasn’texploredthisarea,it’s probably time totake alook–but don’t take my word for it– just ask Gartner!