As a concept, algorithmic merchandising has similarities to the just-in-time methods used in lean manufacturing. The idea is to apply algorithms to the overall stock planning process so, while customers should always be able to locate the products they want to buy, retailers don’t tie up scarce capital in overstocked piles of goods in stores or warehouses.

Yet, the scope of algorithmic merchandising goes way beyond stock levels to incorporate pricing, margins and multiple channels. According to Gartner, algorithmic merchandising optimization “enables retailers to more precisely determine items that need to be displayed and stocked, as well as how they should be priced and promoted, to maximize sales, margin, inventory and customer satisfaction across touchpoints.”

How has Algorithmic Merchandising Helped Retailers? 

Achieving greater insights

Some would argue that disruption to global supply chains during the pandemic exposed the weaknesses of relying on just-in-time planning, with only empty shelves to show for it. However, in our experience, retailers that can build detailed optimization models, including predictive analytics, are much better equipped to evaluate the relative performance of products across multiple touchpoints.

High-level merchandising processes frequently optimized within algorithmic merchandising include retail assortment management applications (RAMA) and retail assortment optimization applications (RAOA), as well as space planning through intelligent virtual store design (IVSD). They may also include the various pricing and promotion types through unified price, promotion and markdown optimization (UPPMO).

Algorithmic merchandising has become even more important within retail because of recent changes in customer behavior and expectations, along with rising costs within the industry. Retailers need to understand, through data analytics, why customers buy the products they buy, where they buy them and where their expectations have not been met.

By addressing merchandising with algorithmics, retailers will be able to counter all the challenges presented by customer expectations and higher costs. Not only are they more likely to place and price merchandise more accurately, but they will also reduce the cost of inventory significantly.

Gartner predicts that Tier 1 retailers in North America and Europe will reduce inventory carrying costs by 30% by 2024; this is a dramatic improvement, considering the $50 billion dead inventory is estimated to cost the US retail industry every year.

“Reducing inventory carrying costs is an effective way to improve productivity and generate free cash flow,” says Gartner. “Retailers will leverage AI to drive more accurate demand forecasting, create tailored market assortments and forward-deploy inventory to localized fulfillment centers to maintain inventory flexibility.”

Building New retail models

Retailer strategies are evolving as the drive for customer centricity and digitalization continues to challenge traditional retail processes and capabilities. Digitally transformed business models deliver retail business processes in the context of streamlined or seamless cross-channel shopping experiences.

Tactics retailers will employ include store-specific assortments that align with digital commerce channels, experimentation with store designs and sustainable production, waste reduction, sourcing, and sales impacting, which now affect all aspects of assortment and display.

We also expect to see changes in shopping experiences introduced by retailers as part of their market-differentiating strategies, including the growth of private brands, innovations and exclusives. These will include recommerce, sometimes known as reverse commerce, which is the selling of previously owned, new or used products, such as electronic devices or books, through physical or online distribution channels.

As McKinsey writes, “Consumers are now accustomed to staying home for weeks at a time and buying a wide range of products online. In the future, they won’t visit stores unless retailers give them good reason to. Retailers must, therefore, gain a deep and up-to-date understanding of customer preferences, envision a new role for their stores in light of these preferences, and execute surgical changes to store formats and in-store customer experience.”

Algorithmic merchandising, analytics and business Intelligence

Technology building blocks for the future of retail

Among the technology trends impacting the industry is AI and intelligent automation, which is being used by merchandising teams to augment product development and selection and to develop predictive and prescriptive recommendations for customers.

The 2021 Gartner CIO Survey shows that 63% of retailers expect to spend more on business intelligence/data analytics and 35% on artificial intelligence. Merchandising and supply chain processes represent two major applications in which to incorporate data and analytics tools in order to improve performance.

The impact of the pandemic on the retail sector has not been evenly distributed across the industry, but organizations from all retail segments are now leveraging merchandising optimization to improve overall performance.

With many consumers changing the brands they buy because of unavailability, retailers can no longer rely on human experience and predictions when making decisions about merchandising. It’s not enough. To keep up with the fast-moving changes and uncertainties in the retail business, organizations will need to accelerate their efforts to adopt automation software and AI, or risk losing customers to the competition.