Resulting from the ongoing COVID-19 pandemic, how American consumers shop and why they buy has changed significantly. The new “homebody economy” has brought to the forefront the value of convenience. Beverage alcohol is no exception, with Adobe reporting that E-commerce purchases of wine, beer, spirits, and accessories saw a 74% increase in April of this year.
Beverage alcohol saw significant growth during the early stages of state lockdowns, with the week ending March 21 seeing sales on alcoholic beverages spike by 55 percent. While beer and wine saw significant growth during that timeframe, spirits saw 75% growth over the same week in the prior year.
This spike, however, doesn’t recover lost revenue from the loss of on-premise sales. Nielsen reported that average sales per on-premise outlet were 68% below rates of a year-ago in the that week ended April 25, and hovered between -67% to -75% throughout the rest of April. While at time of writing, on-premise locations are reopening at limited capacity across much of the nation, it will take time for that critical line of sales to fully recover.
The effect hasn’t been universal, however. For off-premise retailers, demand has shifted in different spirits subcategories in substantially different ways. Our research performed on a subset of midwestern spirits wholesaler data shows clear differentiation in the types of products that are moving to shelves from distributors as compared to prior years.
While it’s just one example of many changes in demand across the industry, pre-mixed cocktails, in particular, saw an explosion in March and April, with more than 40% growth over the same months in 2019. Even more, May and June saw retailers order double the volume of pre-mixed cocktails as the same time period in 2019. The largest area of growth in pre-mixed cocktails came for margaritas, comprising all of the top five SKUs by volume sold in this category.
Even more, using Jump’s innovative clustering techniques built on demographic, behavioral, and locational consumer data, we can describe at a more granular level how the pandemic era has changed retail demand and drivers of sales. Clustering is a data-driven machine learning technique where we group retailers on shared, objective factors, like sales volume of a specific product or category, consumers within their draw area, or climate. By clustering, you let the data speak about what makes stores look or act alike (rather than setting vague business rules that may or may not actually capture the things that really drive purchasing behavior.) In practice, clustering is like taking a basket of fruit and splitting it into apples, oranges, and grapes according to color, size, and shape.
As an example, one of our clusters, referred to as “Patio Entertainers”, showed explosive pre-mixed cocktail sales and high coffee/cream liqueur sales. The surrounding area was predominantly residential, with most residents being homeowners and upper-middle class. Many of these grocery stores.
While the adoption of pre-made cocktails was clear at a population level, it was even more pronounced for some of our clusters than for the full population. Our Patio Entertainer cluster stores had pre-made cocktails comprise more than 6% of volume of total spirits orders in 2020. Because clusters are built on common features, you can build a winning strategy for the hundreds of highly-similar retailers in the Patio Entertainer cluster all at once, saving your salesforce valuable time and money. In practice, this application of clustering delivers a target list of extremely qualified retailers for a pre-mixed cocktail product.
Armed with this more granular knowledge of local trends, marketers can build strategies that accelerate growth of new or existing products, like prioritizing the best fit retailers for a launch of your new luxury tequila and adapting based on how those opportunities differ from your top markets for your flagship product. Prioritizing the right markets saves precious time and marketing spend while you’re building must-have momentum for your priority SKU or brand family.
With the hypothesis that less frequent trips to retailers drove consumers to buy bigger bottles at a better value, Edj dove into a trend we’re referring to as “Bigger Bottle, Lower Shelf.” Edj uses Weighted Price Per Volume to categorize the “shelf” of a product. (High price, low volume bottles will have a higher WPPV.) Edj found that Iowa retailers at large saw an 8% decrease in Weighted Price Per Volume for orders in May, providing some evidence that there was, at least temporarily, some truth to the hypothesis.
Naturally, this is not ideal news for manufacturers of higher shelf spirits. Through clustering, however, Edj identified a cluster of stores that actually has seen tremendous growth in ordered WPPV, both year-over-year and within the Pandemic era. This cluster, the Top Shelf Tequila cluster, was one of only two clusters to see growth in WPPV in the month of May.
This cluster observed a major spike in average WPPV in May, hinting at a demand shift to a higher shelf for stores in the cluster. Notably, the average weighted milliliter in this cluster was priced 28.9% higher than the prior year. Our Top Shelf Tequila cluster purchases the most tequila as a percentage of store volume among clusters. Stores are located in diverse areas, mostly cities, that are middle class.
By focusing resources, like sales calls and marketing dollars, on stores in or areas surrounding the Top Shelf Tequila cluster, manufacturers of top shelf spirits better circumvent the “Bigger Bottle, Lower Shelf” trend, fighting for shelf space where it’s most likely to produce a return.
Ready to turn these insights into cases sold? Contact us today for a demo of Jump by Edj.
Headquartered in the bourbon capital of the world, Jump by Edj is our solution to the informational disadvantage faced by today’s distillers, brewers, and winemakers. Through your depletions data, our consumer data warehouse, and cutting-edge marketing analytics, Jump by Edj brings neighborhood- and retailer-level market and consumer analytics to beverage manufacturers of all sizes. Using the latest in spatial analytics, machine learning, and trade area analysis, you’ll know the right locations to target (and why), turning data into new cases sold.