Eliminating Category Duplication Through the Power of AI

No retailer deliberately sets out to complicate the lives and shopping experiences of its customers. In fact, headlines point to ways the industry is doing the opposite, adding convenient fulfillment options or altering store formats to accommodate quicker in-and-out trips. However, if as many as 17 percent of items in a category are duplicative in nature, as research suggests, retailers may be inadvertently causing undue stress and overwhelming shoppers. When faced with too many options, online or in-store, many consumers choose not to choose.

For this reason, and to optimize assortment offerings as part of their ongoing business operations, retailers must understand consumers’ perception of choice. Confidently making category rationalization decisions requires not only a mastery of massive data sets, but the ability to act on insights instantly. Artificial intelligence (AI) makes both possible.

AI Understands Dynamics Between Shoppers and Products

When retailers make cuts in a category based on historical sales or other observable metrics, they don’t take into account the significance of transferrable demand — i.e., how demand for a particular item would transfer to another if the product were to be removed entirely. In some cases, if you’re lucky, a shopper won’t skip a beat, opting for an alternative or closely related option in its place. But in the more concerning case, the absence of that product could cause the customer to leave without buying anything at all, even prompting them to shop with your competition instead.

Whether a product is relevant enough to remain in a category requires an understanding of the customer perspective, not just sales performance. AI brings this awareness to life. Considering the big picture, AI leverages customer loyalty insights to predict outcomes of removing one product over another. The technology can spot a “slow mover” that might appear an obvious candidate for elimination, but that's the deciding factor for a customer. In learning shopper behaviors, AI understands the motivations and priorities of customers, as well as what products are complementary to another or what can be bought as a substitute for another. Decisions made apart from these dynamics will be to the detriment of sales and customer satisfaction.

AI Prompts Timely and Perpetual Assortment Decisions

In assortment optimization, AI doesn’t stop at “saving the cart” by helping to avoid costly category cuts. It empowers retailers to move away from traditional calendar-reset management to make more timely assortment decisions. If you think about it, assortment optimization on a scheduled cadence isn't based on real opportunity. Instead, AI and machine learning align a retailer’s categories to real-time consumer and market needs, providing recommendations exactly when change is required. Not only that, but AI reveals the forecasted result of removing one product over another, presenting potential return on investment to inform these critical decisions. Annual, sweeping category reviews have been the norm in the retail industry, but through AI-enabled analysis of all available data, retailers can make bold and continuous refinements in their product categories.

Fewer items to manage, more predictable demand, improved forecast accuracy — these can all be realized when assortment decisions are data driven and customer involved. Retailers today cannot afford to make decisions apart from a precise understanding of the consumer, and AI makes it possible for retailers to drive sustainable category growth while keeping customers happy, too.

Kevin Sterneckert is chief marketing officer at Symphony RetailAI, a global leader in AI-enabled decision platforms, solutions and insights for driving profitable revenue growth for retailers and CPG manufacturers.