Preparing Your Team For AI Adoption In Retail Pricing

0aaaAlexandr Galkin Competera
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Multitasking is a major skill that retail pricing managers need to have today — and maybe it shouldn’t be that way. Tasks and KPIs are so abundant that optimal prices, a key to satisfying customers and increasing revenue, seem a goal reached only rarely, for selected items and after overcoming endless distraction in the process. What makes things worse is that pricing decisions are needed constantly, giving your team no time to analyze the past and plan for the future.

Market leaders and a growing number of smaller companies are embracing AI-enabled pricing analytics software to optimize the business and boost financial performance. Look at Walmart’s Intelligent Retail Lab or Amazon’s 35% of the revenue brought by AI price suggestions. Algorithm-powered solutions augment the capabilities of retail teams and streamline processes to let retailers increase revenue by up to 16%.

In general, productivity uplift is expected to account for an incremental $6.6 trillion by 2030 thanks to AI adoption. Meanwhile, to benefit from intelligent automation, retailers need to ensure that their teams and businesses are AI-ready. The team comes first, as the effectiveness of a certain technology depends on how well you can use it.


How To Gear Up Your Employees For AI

Machines should be responsible for routine tasks like data processing and insight (or price recommendations) generation. In the meantime, humans will have a chance to switch to high-level management like strategy creation and talks with manufacturers.

At the same time, retail managers rarely accept AI adoption willingly since it requires mastering a new skillset and mindset. Most people don’t like leaving their comfort zone.

To make the transition smooth and beneficial, retail executives should bear in mind three things:

– Pricing managers need to become proficient users of a new tool. Teams have to be able to launch a repricing cycle, set goals and order from vendors, among dozens of other tasks they can do, with the help of technology.

– Pricing analysts should learn to switch from micro- to goal-management. Managers who do not understand the logic algorithms used to make price suggestions often refuse to apply them. But retail teams do not need to control the price of every product anymore. Instead, they should focus on the performance of the whole category or product portfolio. If the goal (for example, to increase sales while keeping margins, liquidate excessive stock or maximize profit) is hit, then it should not matter if algorithm-generated price recommendations seem illogical. 

Also, managers should remember that an AI solution is just a tool, while they call all the shots when it comes to pricing. They set KPIs for algorithms, which analyze vast amounts of data, look through thousands (or even billions) of pricing scenarios and suggest the optimal one that allows hitting the goals.

If managers feel uncomfortable using machine-generated price recommendations at first, they can always set restrictions and/or test their hypotheses via a “sandbox” before applying them in real life. 

– Pricing teams need to test the effectiveness of price analytics solutions firsthand with support from their top management. That’s why one of the first steps would be to launch a market test (which can take anything from eight weeks to several months), during which managers can apply price recommendations to a particular group of products. 

From my experience, usually the results of such price optimization are very promising (e.g., a 24.7% sales surge). Therefore, retail teams start feeling more confident when using the machine’s suggestions, as they experience a major boost in efficiency and the quality of their pricing decisions. For example, by spending just 15 minutes instead of over an hour for repricing and setting optimal prices for any number of products.

Optimal prices for every product (in other words, a soaring revenue) are a reality for retailers using price optimization software in their pricing. However, to make the most of AI, retail businesses need to prepare their teams — or the end users of the tool — thoroughly. The preparation includes the following stages:

  • Teaching managers to use software;
  • Helping teams transition to strategic thinking and goal-management; and
  • Launching market tests to help employees feel more confident when applying machine-generated price recommendations.

Gearing up your team well means the best use of your investment in the optimization of pricing and translates into growing sales and revenue for you.


Alexandr Galkin is CEO and Co-Founder of Competera, price optimization software for enterprise retailers looking to increase revenue and stay competitive. He is a Forbes contributor, speaker at IRX, e-Commerce and RBTE conferences. 

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