Walk the Walk: a comprehensive approach to data-driven pricing for an evolving retail landscape – 365 RETAIL


Retailers are increasingly adopting AI-based solutions for full lifecycle pricing, which deliver pricing, promotions and markdowns that keep pace with rapidly changing competitor, market and retail behaviors. buyers who cannot be predicted by simply looking at the past.

But there are some important considerations that are often overlooked when selecting a cutting edge pricing tool and ensuring successful adoption that is crucial to meeting business and financial goals.

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Successful retailers will take a holistic approach to preparing their organization to be data-driven, including:

1. Plan for the initial data hygiene and ongoing data maintenance that will continue to unlock value from the optimization solution.

I think price optimization is a lot like buying a new car – when you first drive it, it’s incredibly exciting, and you see a big difference between how it performs and how the old machine performs. that it replaced. But if you haven’t invested to start with clear and comprehensive data and to keep that data up-to-date and accurate in the future, you see diminishing returns. Over the weeks, months and years of failing to maintain data, you fall further and further behind more disciplined competitors and lose touch with what got you excited in the first place, which is pricing. and promotions that significantly engage your buyers.

Pricing teams should be trained so that they have the skills to put in place safeguards that capture material anomalies, such as if a recommended price increases by more than X% because the cost has risen sharply – the equivalent. a “check engine” light that catches your attention. Maybe someone entered the wrong cost? Or has this item really experienced a dramatic increase in costs?

But if users overwhelm the system with too many rules and too tight constraints, they prevent data science from doing its job effectively. As they gain experience with the AI-based solution, they should reduce the number of rules and business constraints to allow science to offer more and more pricing autonomy. as she learns in real life conditions from this retailer. In a modern automobile, the engines are specifically tuned for optimum performance in several areas. Any other constraint makes performance less optimal. In my experience, only 10 to 15 rules per category provide optimal results.

When the pricing team is well trained, supported by the IT team to have good data and flow maintenance, and continues to climb the maturity curve by leveraging data science, category managers have the power. confidence that they are driving a well-maintained, reliable vehicle that will not break down and that will last. The reward is the continued ROI for the entire organization, as categories meet their revenue, margin, and profit goals more consistently.

Sam Walters, Senior Counsel and Senior Director, Consulting Services, DemandTec

2. Take advantage of system information on cross effects.

Seasoned price strategists and category managers have strong beliefs about items that have affinity, where promoting one item and reducing the margin will result in the sales of other higher margin items to more than compensate. reduced price, and cannibalizing, where promoting an item may increase units and revenue for that item, but reduce sales of other substitutable items that could do more harm than good to the category as a whole . In my many years of helping organizations harness and effectively embrace data science in optimization tools, I have seen the disconnections between long-held beliefs about affinity and cannibalization and revealed reality. by AI-based tools.

This is especially true when it comes to Key Value Items (KVIs), which are the items that buyers pay the most attention to and have a significant impact on the price image. For example, when a category manager and a pricing strategist see a system-recommended price cut on a KVI they strongly believe in, such as a popular brand of breakfast cereal, they may be reluctant to lower the price. price of a lucrative item in the category. . But science takes into account current and precise elasticity and demand signals, and adopting the recommendation may actually make the retailer a bigger destination, driving overall cart size and foot traffic and improving its image. of price. In other words, lowering the price of an item can generate more revenue and margin for the retailer as a whole. Seeing science in action and the measurable results it delivers begins to build confidence, category by category, and increases overall adoption and ROI.

3. Kiss him power of simulations and planning of what-if scenarios.

One of the most powerful ways to reap the benefits of AI-powered data-driven pricing is to take advantage of simulations and what-if scenario planning. Think of them as test drives, and you weigh the pros and cons of each. Pricing and category management teams can collaborate to see how changing an item strategy would impact margins, units, and profits, for example. Likewise, pricing strategists can explore how to increase traffic by increasing the importance of unit sales in the model while being more willing to sacrifice margins, and data science will tell them what price would support that goal. These capabilities provide the opportunity to become more comfortable with the tool, more confident in the science, and more innovative in the market to engage buyers more effectively while achieving overall financial and business goals.

Together, these proven strategies allow retailers to harness the potential of a leading suite of price, promotion and markdown optimization more quickly and efficiently. With some planning and discipline around data, and an open and curious approach to cross-item effects and simulations, pricing teams and category managers can have a transformative – and measurable – positive impact for their businesses.


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