As the Holidays Approach, it’s Time to Rethink Retail Pricing Methodology…and AI Can Help

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According to this recent consumer survey from McKinsey, the holiday shopping period will be longer than in previous years — beginning earlier and ending later — with some portion of consumers starting earlier in order to beat expected approaching-holiday price increases while others will wait till later, expecting to benefit from holiday-eve promotions. In addition to this timing variation, over 60% of shoppers plan to do their browsing online, yet 85% expect to buy at least one product in a store. 

Add it all up and it’s clear that the timing duration, and location of holiday shopping is not like the old days. 

In this kind of shopping environment, how should retailers align their promotions and markdowns for the greatest impact? Keeping in mind as well the need to optimize their supply chain fulfillment to meet consumer delivery expectations while managing parcel carrier costs? 

Breaking from the Traditional Rules-Based Approach

In this increasingly complex environment, one thing is becoming abundantly clear — the traditional strict sets of pricing rules and policies, often put in place many years ago, can no longer keep up. And retailers are turning to AI to help. 


The individuals who originally set forth all that legacy pricing structure may have even long since left the company, yet that relatively ancient framework — perhaps volumes of rigid business rules — is still looked upon as a virtual bible of pricing, even if the whole process now feels cumbersome, inefficient or simply inaccurate. 

AI-powered solutions provide a modern alternative — leveraging data science to successfully break away from a status quo built around yesterday’s mindset and tools.

Where can AI and data-driven pricing solutions help reshape a retailer’s outmoded pricing structure and deliver pricing that fits today’s consumer?

Ending the Tradition of Blanket Markdowns

Enter this year’s holiday shopper. Some are the bargain-hungry customers at the beginning of the season who will naturally be drawn to the most attractive prices and discounts that resonate with them. To capture these customers, should all the hot new designer denim be subject to the same standard holiday promotion, or should the more popular washes follow a different promotional cadence? 

Thanks to granular-level data and insights gleaned from store and online purchases, it is possible to know much more about customer color preferences and more, making it possible to tune the promotion and markdown strategy accordingly. Retailers can now gauge how different items sell at different speeds at different times, to different customers.  

Many retailers used to approach promotions in a standardized, blanketed way, but with AI, they can now zero in on what Gartner calls the customer’s “biting point.”

And don’t forget the shopper looking for that end-of-season deal. AI-driven demand planning using current sales data can recommend the best markdowns for a clean transition with the highest profitability given the current inventory levels at each location. Trying to use a spreadsheet to glean the optimal markdown across thousands of SKUs and hundreds of locations is simply more than that tool can handle. But best of all, the insights from AI-driven demand planning will give the retailer more courage to push back against that traditional impulse — to just get rid of all end-of-season stock in one big push. 

Bringing Location into the Mix

Is it possible to avoid marking down an overabundance of small-size inventory at certain store locations when consumers are still looking for that size online? With AI, where the nuances of pricing and fulfillment between brick-and-mortar and online sales can be analyzed, the answer is yes. Every retailer ideally wants to maximize sell-through for every SKU — seasonal items in particular — and AI-driven lifecycle pricing and allocation helps maximize margins by considering all options for fulfillment.  

But will online customers feel cheated by potentially seeing a lower price on their next store visit? Regional retailers probably have more leeway than national chains, yet they risk alienating the customer who compares a higher online price with a heavily marked-down item they just scoped out in the store.

Again, thanks to AI and data science, the optimal boundaries for promotional and markdown price differentials can be determined and set — between the website and store locations, or even between the sizes and colors in a single SKU — to prevent customer irritation and loss of loyalty. 

Building Overall Trust in the Outputs of AI and Machine Learning

Some retailers may be driven to experimenting with AI-driven pricing and demand planning simply because they can no longer find planners who want to be buried in spreadsheets. Still others will be attracted to the promise of leveraging data to get a more granular understanding of their customer. Either way, there can be a significant “whiplash effect” when they first compare the AI-generated pricing and markdown recommendations against results from the methods or strategies they’d used before. 

Typically, up to two-thirds of AI-driven recommendations will be perceived as radical change from the past. Some customers will feel a sense of enlightenment by this, while others will totally panic.

This is where the expertise and professionalism of the data science team and AI-driven software come in. A core capability of any AI solution should be the ability to fine-tune the results with as many business rules as necessary to give planners ultimate control. Furthermore, AI-driven pricing solutions should take one step further, to identify the recommendations that have a high degree of confidence based on the analysis and those that may require additional review.

But Don’t Wait to Try AI — your Competitors may Already be Ahead of you

We know that change — in particular, improving upon a long-standing rulebook — doesn’t always come easy. But keep in mind that your competitors may already be using AI to figure out your pricing patterns, as this study in Harvard Business Review implies, by scraping websites in real time and including that data, along with price elasticity considerations, in a dynamic price-response system. 

As we navigate the ever-changing shopping terrain this holiday season, it’s crucial to recognize that AI isn’t a futuristic concept — it’s the present force shaping our reality. Much like the spreadsheet revolutionized data management in its time, AI stands as today’s game-changer. Embracing it isn’t just a strategic move for the future; it’s a necessity for thriving in the now, ensuring your approach to the retail landscape is as advanced as the technology driving it.

David Barach is Senior Director, Solutions Strategy at, a Zebra Technologies Company. Barach guides development and customer deployment of AI-powered data solutions specifically related to pricing optimization, inventory management and advanced demand forecasting for major retailers. He brings a unique background of 20 years in the retail data intelligence space, following multiple internal finance, planning, buying and store operations roles for retail giants Target, Macy’s and Kroger. From this firsthand perspective in both retail and software, he has focused on the application of robust, leading-edge statistical analytic solutions for retailers’ age-old challenges of optimally aligning pricing and inventory with shifting shopper demand. Joining in 2017, Barach consults directly with customers for successful custom development, deployment and adoption of the company’s data solutions, driving high value creation and improved bottom-line results across core retail operations.

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