How the Art of Merchandising Should (and Should Not) Work
One of the hottest trends today is the expansion of fresh products in virtually every segment of retail including convenience stores, drug stores, discount stores, specialty stores, and adding broader assortments in supermarkets.
One of the interesting things about this trend, according to Kevin Sterneckert, chief marketing officer for Symphony RetailAI, is that it is a perfect case study of how the art of merchandising should (and should not) work. I recently spoke to Sterneckert about this topic, out of stocks, and the impact of artificial intelligence (AI) on the driven merchandising function, a conversation that was captured in a podcast you can listen to by clicking here.
Fresh Merchandising as a Competitive Weapon
Today, retailers promote fresh products as a competitive weapon and have invested sharply in merchandising functions that get these products into stores. As expected, fresh product sales have delivered a nice bump in KPIs for these retailers, however the full impact of the shift (some good, some bad and some missed) has not been fully understood.
“I was thinking through this recently in terms of how the fresh trend in grocery is impacting the center store,” says Sterneckert. “The prominence of fresh in marketing and promotion changes the nature of the rest of the store. If customers are buying more fresh, they’re shopping more frequently and their transaction sizes are smaller. They’re also buying less frozen, packaged, boxed and canned goods because they’re buying more fresh.”
“But if your forecasting engine and your demand engine doesn’t understand these changes, then your understanding of demand is incomplete,” adds Sterneckert. “Your forecasts need to understand the whole customer and the whole store and not just segments or clusters or categories or sub departments of demand. The customer isn’t thinking in silos when they shop. They have a mission. They want to prepare meals and they’re thinking about this holistically. If you’re thinking by category or thinking within a sub department, then you’re missing a big part of who the customer is and what’s driving behaviors.”
Today, most retail organizations manage their merchandise functions in category silos because it is much too hard to manage all products holistically. Instead they opt for a system of managing an aggregation of silos.
“However, you can’t understand demand for forecasting and replenishment if you’re not thinking ‘What’s the behavior of the customer in the fresh categories’ and what is the impact of this influence in the store,” says Sterneckert. “Most retailers today are not thinking this way and they can’t. To do so requires AI and machine learning.”
How AI-Driven Merchandising Influences Out of Stocks
It is not a stretch to say many retail merchants used to succeed by being brilliant guessers. They relied on years of experience and gut-level instinct to make assortment planning and purchasing decisions.
However, today, there are just too many personalized assortments to consider, too many customer segments, too many store formats, and too many channels to manage and identify meaningful patterns in the shape and pace of customer demand.
AI engines help retailers do this on both a macro and micro level simultaneously. Retailers are beginning to understand this, however the problem is that too many think of AI as a silver bullet, especially for solving big problems like out-of-stocks.
“I was talking with a very large grocery retailer just a couple of weeks ago and they were talking about using AI with their forecasting function to solve their out-of-stock problem,” says Sterneckert. “I shared with this retailer that the first and best way to gain improvement in out-of-stocks is to gain discipline in perpetual inventory and planogram compliance.
“The retailer replied to me, ‘Well, you know, we have a lot of retail leaders and store managers that believe they don’t have to pay attention to that because their stores are profitable and their sales are growing so it’s optional for them.’ I said, ‘Well, how will AI applied to your demand forecasting improve the situation if the data and information and compliance isn’t in place for AI to leverage?’
Sterneckert’s point is that if you insert an AI engine into a retail organization with poor compliance to planograms and poor perpetual inventory accuracy, then AI will not fix the out-of-stock problem because the data fed to the AI engine “is dirty” and “the AI engine will not learn properly” to make better decisions.
To hear more about these topics -- fresh management, AI-driven merchandising and solving the out-of-stock challenge -- click here to listen to the podcast with Symphony RetailAI’s Kevin Sterneckert.
Also, find out more about the RIS News Targeted Research report the podcast is based on, “AI Driven Merchandise Management,” click here to read the report.
Finally, to listen to other pod casts in this series based on the report click here.