The fate of retail performance for the second half of 2024 is anything but certain. Despite expectations that inflation, subsequent rate hikes and the lingering effects of the pandemic would negatively impact spending, consumption rates have remained surprisingly high. This counterintuitive trend has sparked debate among economists, analysts and retail executives about the sustainability of current spending patterns. When will Americans stop spending? And when they do, are retailers, many of which were badly hit by the pandemic, prepared for a significant drop in revenue?
One way to prepare for such an event is for organizations to sharpen their approach to price optimization. This can be done through the effective use of granular data, which will in turn help retailers maintain customer loyalty and keep their revenues steadier, regardless of fluctuations in national consumer spending rates.
Unpacking Data Granularity
Many large retailers across America are racing to create the largest piles of data, without realizing that it takes capital and personnel to sift through this pile of data for any valuable insights. When it comes to advanced analytics, less is often more. Data granularity refers to the level of detail in our data. By homing in on the correct level of data granularity for each product sold, retailers can run leaner data departments while still generating actionable insights.
In the late 2010s, the obsession with amassing consumer data peaked when retailers were collecting an average of 23 behavioral advertising cookies on their homepages. A backlash soon followed as customers felt spooked by the sense that they were under surveillance from companies they once trusted. Privacy and customer satisfaction are now closely intertwined. The Cisco 2022 Consumer Privacy Survey concluded that nearly 4 out of every 5 customers surveyed agreed that a company’s approach to handling personal data reflects its attitude towards its customers.
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Partly due to this shift in consumer sentiment toward valuing privacy, California enacted the Consumer Privacy Act of 2018, imposing strict regulations on how, and how much, customer data can be collected. America’s retail giants should anticipate that other states will follow suit and transition to less invasive, more granular data models.
Retailers should look to Facebook as an example. Just six years after the disastrous Cambridge Analytica scandal, the tech giant has managed to rehabilitate its image by refining its algorithm to avoid relying on sensitive personal information. Its advanced algorithm can now operate on simple, publicly available data and still generate the insights needed to sell advertisements effectively.
Restructure Rather than Retreat
Because the analytics departments of major retailers are bloated, many executives mistakenly downsize other areas of the business instead of reevaluating their data strategies. Executives are hesitant to admit they’re receiving minimal return on investment from their data departments for fear of being perceived as Luddites clinging to traditional methods. Everyone wants to appear “future-forward,” yet it’s hard to acknowledge that sometimes, one step forward can result in taking two steps back.
For American retailers, this retreat takes various forms, such as downsizing physical footprint or undertaking mass layoffs. In addition to store closures and layoffs, retailers have had to consolidate operations, renegotiate deals and scale back promotional activities.
Mass Discounting: Inventory is Cleared, But Margin is Usually Left on the Table
Given how counterproductive recent waves of mass discounting have been, it’s no wonder U.S. retailers are looking for a more effective approach. Discounting is a valuable tool, yet what separates effective from ineffective promotions is understanding that every item a retailer sells has a different level of price sensitivity — which requires access to highly granular data. Blanket, top-down discounting efforts might clear some inventory in the short term, but without optimizing pricing for each item, retailers are leaving significant margin on the table. Even worse, these promotions often fail to clear the desired inventory at all.
Discounting requires a significant increase in volume to counteract the reduced margins. For instance, if a retailer’s product has a gross margin of 40% and the price is cut by 5%, they’ll need to sell 14% more to maintain the same gross margin. A 20% discount necessitates doubling the sales volume to break even. Offering a 40% discount makes it impossible to break even, no matter the sales increase. Underperforming discounts are one of the biggest costs for retailers today.
The Psychology Behind Pricing
Besides missing out on short-term revenue, overzealous discounting efforts can erode customer trust in the long term. This might seem counterintuitive, as traditional thinking suggests that discounts strengthen customer loyalty during tough times. With stubborn inflation squeezing the American consumer, times are certainly tough for the consumer. Therefore, major American retailers are responding by bringing down prices. However, it takes nuance to optimize these discounts for both short-term and long-term profitability.
Speaking purely from a psychological perspective, a 20% discount is the highest tactical discount you can offer on a product without providing justification. It’s the point before people start to wonder how you can offer such a price without hurting your margins — unless the price was artificially inflated to begin with.
When discounts reach 30%-40%, a compelling rationale must be provided to prevent customer suspicion. A 40% discount requires careful storytelling to avoid diluting the brand image, such as framing it as a birthday sale. Discounts of 60% or more can be problematic unless they are at the very end of your markdown season, as customers start to perceive them as artificial and question if your prices mean anything at all. Customers will be less likely to buy at full price in the future; after all, you inevitably will discount again.
No Purchase Exists in a Vacuum
Once an organization understands the relationship between margins and discounts, as well as some basic psychological pricing guidelines, do they have enough information to calculate the optimal price for a specific product? Almost never.
Determining the optimal price is a complex process involving many moving parts that cannot be determined by these two factors alone. Demographics, seasonality, location, macroeconomic conditions, rapidly changing social media trends and countless other factors also influence a customer’s willingness to make a purchase. This can only be achieved by combining human expertise with granular data. Many skeptics of AI claim that these emerging technologies will pull humans farther apart, but in this instance, advanced algorithms help us understand humanity in greater detail.
The Linear Model is No Longer Applicable
Much of the resistance to using granular data stems from a tenacious attachment to linear pricing models. This traditional approach assumes a simple relationship between customers and all items sold, where decreases in price directly correlate with increased customer willingness to buy. As most American retailers aren’t typically in the business of selling luxury goods — which can be weakly price sensitive or even price insensitive — they assume that price is always the primary factor in purchasing decisions.
However, as retailers consolidate and offer a wider selection of goods, they house more and more weakly price sensitive items. As more adopt the modern, American one-stop-shop model, it means that an increasing number of retailers might sell household and wellness items, high-demand gadgets and niche artisan goods — all under the same roof. A customer buying these items often values brand prestige, quality and availability over price.
The value of convenience has been catching up to price — a 2020 National Retail Association study concluded that 83% of customers surveyed claimed they valued convenience more than they did five years ago. This change in sentiment was steeply accelerated by the pandemic, where essential goods were purchased regardless of price.
Yes, retail has transformed rapidly since the start of the decade. But by leveraging granular data, retailers will no longer have to play catch-up. Retailers that succeed in this volatile landscape will do so by being agile — responding to changes in consumer behavior in real time.
Fabrizio Fantini, PhD is VP of Product Strategy at ToolsGroup, a supply chain planning and optimization firm. He can be reached at [email protected].
