Levi Strauss & Co, makers of the iconic jeans, faced some pretty serious belt-tightening earlier this year. In April, the company announced that “escalating costs of freight, labor and cotton, as well as lingering supply chain disruptions” and “bloated inventory” were decimating margins. The market was quick to respond, with shares dropping 16 percent that morning alone. (The stock has yet to recover.)
Welcome to the topsy-turvy life of retail, an industry so volatile that only three months before Levi’s announcement the headlines read, “Levi Strauss gives upbeat 2023 sales outlook as demand holds up.” Demand (as we now know) didn't hold up, and Levi’s was forced to resort to deep discounts and promotions to push excess inventory out the door.
These tactics are just some of the traditional levers that retail marketers use to move prices and products, and it’s always been a balancing act that requires consumer insights, careful timing, understanding elasticity, assessing competitor intelligence, and a little bit of luck. Now, thanks to advanced analytics and sophisticated modeling, what had been guesswork is turning into data-driven decision making.
The Math Behind the Promotion
Buy one/get one (BOGO), combos, discounts — these are all tactics used to shape demand. Promotions can spur excitement around a new launch, signal the start to a new shopping season, or stimulate buyer interest to clear warehouses. It’s all about influencing consumer perception and behavior. After all, who among us hasn’t gotten FOMO from missing a BOGO promo?
Like most industries, retailers have had a tough time getting back to any sense of pre-pandemic normal. When consumers were forced to stay home, they saved (but spent online). When consumers emerged from lockdown, they spent (but mainly in stores). Along the way, retailers went heavy on promotions and discounts to meet consumers where they were, playing whack-a-mole with an endless list of challenges. As The New York Times put it, “In 2020, it was pandemic closures and social distancing. Last year, it was the supply chain. Now, the problem is demand.” (The highest inflation rate in two decades hasn’t helped either.)
The demand problem cuts two ways — demand has decreased among consumers, but increased among shareholders who want to see better bottom-line numbers. Simple economics dictate that improving return in a demand-constrained environment means reducing costs, and (along with jobs) some of the first things to get axed are discretionary promotions. It makes sense. Why offer discounts when you’re trying to maximize profits? But what if you could do both?
Here are a few ways that retailers can use advanced analytics to offer more profitable promotions:
Price elasticity measures how sensitive the supply and demand of your products are to changes in price. Some essential products like milk or eggs are less elastic (i.e., people will still buy when prices go up), whereas fashion is more elastic (i.e., you don’t REALLY need those new shoes). Though straightforward in definition — change in demand for unit change in price — its computation and calibration are complex, especially in a global marketplace of stores, channels, and competitive products and brands. That’s a lot of data for an Excel spreadsheet, but not for advanced analytics.
In the past, retailers have had to rely on aggregate numbers and sample sets to understand price responses. But given its ability to accurately crunch huge reams of data, advanced analytics can provide a much more nuanced view to retailers, analyzing performance by item by store by customer segment. That’s a huge advantage that might yield valuable pricing and promotion insights.
There are many different types of promotions — some offered by specific brands, some by retailers, and some on specific types of items (e.g., a sale on all jeans). In different seasons and holidays, customers expect good deals, but discounting items by group might impact sales that were already strong, cutting into profits. This is where data and advanced analytics can play an outsized role.
Sticking with jeans as our example, rather than Macy’s offering 20 percent off ALL jeans, a more effective approach would be to offer sharper promotions on styles that were overbought, attract more target consumers or grow basket sizes. For consumers, value is king. By using data to get more targeted, retailers can improve the top line while also saving on the bottom line.
According to McKinsey, while marketers tend to measure the efficacy of a promotion based on earlier promotions and sales figures (i.e., increases in revenue and margin), there are several other key performance indicators that are critical determinants of success. Cumulative KPI, or “total customer effect” (TCE) as they call it, shows how much additional revenue or margin a promotion accounts for by looking at what additional sales activity it generated.
The point here is that not all sales dollars are equal. Advanced analytics allows retailers to dig deeper and measure factors like how a promotion might have increased tangential purchases (e.g., T-shirt sales rising during a shorts promotion) or changes to buying behavior (e.g., new customers coming into the store). Customers who are newly acquired or reactivated because of a promotion are arguably more valuable than returning customers who may have shopped anyway, so measuring one-to-one promotional sales alone is flawed. The goal of analytics is to produce a comprehensive view of the impact of a promotion beyond just financial success, including customer impact, geographic impact and product impact.
Crystal Ball for Sale: Half-Off
Remember, there is no “mastering” the art of promotions. The world of retail is dynamic and in constant flux. Even if a retailer did everything right this year, it doesn’t mean it will also get it right next year. Preferences change, trends fade away, and behavior shifts. The unexpected will happen, but there will always be data. Are you prepared to respond?
Vamsi Valluri is the practice head of retail at LatentView Analytics, a data analytics solutions provider helping clients make data-driven decisions and achieve business goals.
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Vamsi is a strategist with over 13 years of experience across sectors, functions, and cultures. He is passionate about augmenting the art and science of retail with customer data insights. Vamsi has leadership experience in customer insights and retail and omni-channel analytics.
At LatentView, Vamsi leads the Retail industry practice, overseeing client engagements, developing solution accelerators, and shaping thought leadership. Prior to LatentView, he led data science and analytics at Foot Locker, partnering with executive leadership, and evangelizing and developing novel data solutions.