Retail Analytics Series Part IV : The Large Business
By Bob Deakin
The SBA Office of Advocacy classifies a large business as one with more than 250 employees and $1 billion in gross receipts. Iconic enterprises like Amazon and Walmart are ubiquitous, but large businesses comprise less than 0.5 percent of all U.S. businesses.
As of 2024 there are 21,139 large businesses in the U.S. and these titans employ 23 percent of the workforce. Small businesses (fewer than 100 employees) comprise 99.9 percent of all businesses and employ 46.4 percent of private sector employees.
Fun With Retail Analytics for the Enterprise
Marketing agency Brand Vision Insights separates retail analytics into descriptive and predictive. Simply put, descriptive analytics live in the past and profile your customer by actions and sales. The aggregate data mined creates a customer profile, setting up the predictive.
Predictive analytics utilize statistical and machine learning algorithms as a crystal ball. This usable data provides vital insight into customer demand and potential risks, and spits out recommendations based on data patterns.
“Data analytics allows retailers to segment customers, identify trends and patterns in purchasing behavior, optimize pricing and inventory, target marketing offers, forecast demand, and make other important business decisions.”
What Do Retailers See in the Crystal Ball?
Sources mined for data include transactions (purchase history, frequency, times and POS) and customer data (contact, demographic, products, website/app activity). Marketing KPIs, customer responses and ROIs are vital info, as are interactions on social media. Comments, reviews, complaints/raves, and engagement all tell a tale of the individual.
Lastly, the supply chain, particularly post-Covid, shows what happened and how to prepare for what will happen again. At this point, it’s on the enterprise to deliver what customers want or need.
Analytical Case Study For the Large Business
Analytics applies to inventory management, merchandising, payroll, pricing, sales forecasting, supply chain, website efficiency, the brand of coffee in the break room and everything else that affects a business.
Zebra Technologies conducted a two-year study for Walgreens using actionable intelligence. It determined, for example, that inconsistencies in store opening times and returning unsold paperbacks for credit amounted to millions each year.
In one instance, the manager closed the store 40 minutes early to attend a family party. Zebra’s algorithm calculated that those minutes cost the store $450 in missed sales.
Looking closer, statistics showed an unusual number of the same birthdates entered by cashiers when checking IDs for alcohol and tobacco sales. This exposed that many were not actually checking IDs, but merely entering the same (required) date into the system each time.
In only 24 hours, analytics identified more than 500 Walgreens cashiers nationwide using this shortcut and subsequently took disciplinary action. It continues to use the algorithm to catch deviating team members.
What's In Store?
Predictive analytics for in-store shopping give employees insight into a customer’s wishes, often via electronic devices. The employee, whether in a retail store or theme park, can ID a customer by their loyalty card, phone, email or wristband. The employee can see what the customer has ordered or inquired about in the past, and make educated suggestions.
Andrew Eliseev of digital strategy firm Luxoft breaks down 7 Ways Data Analytics and AI will shape online and in-store retail.
“Up until just a few years ago, the retail industry at large focused mainly on marketing processes and enhancing customer service,” Eliseev writes. “With the current competitive landscape, the focus has greatly shifted to collecting data, analyzing it, and then drawing conclusions to further improve marketing and customer service strategies. This full cycle is referred to as data analytics, typically performed through the use of AI in retail.”
Consumers are hip to analytics, and they practically pose for images in the retailer’s crystal ball. Today’s customer wants you to know what they are thinking so they don’t have to. If they purchase a set of wipers for their Honda every six months, they don’t want to look the parts up again on your app or in your store.
The Isn't Your Grandmother's Shopping Experience
Retailers are only scratching the surface of AI and analytics in the shopping experience. As personalized as it has become, we have all become merely a line of code.
Yes, it may be too coincidental that you looked up the Los Angeles Lakers on Instagram an hour ago and now see tickets for tonight’s game as you shop for a handbag. But, now that you mention it, who are they playing tonight?
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