By Steve Cavolick
Retail sales in May increased almost 18% compared to April of this year. Online sales grew by 9% month over month, and many of the retail segments that had been crushed when the pandemic hit finally returned nicely last month. The graphic below from Yahoo! Finance breaks down the sales growth by retail group.
While the revenue growth was positive for this May, spending levels were still down 6.1% compared to May of 2019, according to the official Retail Report from the U.S. Census Bureau.
This leaves retailers in a tricky spot. They are facing vastly changed consumer behaviors: more online shopping, fewer shopping trips per week are made and fewer stores are visited, but more products are purchased per trip to better stock pantries. In addition, house brands/labels are being tried more frequently, and new fulfillment models such as curbside pickup and delivery are must haves. More than ever before, retailers need to provide value and affordability.
Our retail, co-op, and distribution customers are most anxious to talk to us about using their data and advanced analytics for the following applications. Could your business also benefit from these?
- Recommendation Engines: We know that shoppers like recommendations as long as they are relevant, timely, and expected, so recommendation engines can be a lever to help you increase revenue by more effectively cross-selling and upselling, and even originating trends. These models require lots of clean historical data so it can predict future behavior based on past purchasing patterns and/or product qualities. Other data about demographics and preferences will be added to better train the model.
- Customer Segmentation and Lifetime Value: Some customers are more valuable than others, and you can rank them on how recently they bought from you, how many times they’ve purchased from you, and the amount of the average transaction. This is one way to segment customers and can determine how and how often you market to them. These measurements can also be extrapolated over future years to understand how valuable they will be to you over the lifetime of your relationship with them.
- Churn Prevention: Recovering lost revenue from customers who defected is more expensive than retaining and growing your existing customers. Use predictive analytics to discover the reasons your customers are dissatisfied and identify those who are likely to leave.
- Sentiment Analysis: Do you want to know what know what consumers think about your company or products? Cloud-based storage makes it easy to keep information from millions of social media posts and AI extracts meaning from the layers of speech in order to create a sentiment rating.
- New Store Location: I hesitated to include this at first, but last month’s data proved that people will return to stores when they feel it’s safe. We know consumers prefer an omni-channel experience that includes brick and mortar, and store placement is a huge factor in predicting success. It goes beyond just knowing demographics by ZIP codes. You need to understand purchasing power of local populations, available parking, presence of competition, and traffic flow.
We haven’t even touched on market basket analysis, warranty analysis, or fraud detection. Within retail, there are so many ways to use your data to align with customers’ needs and increase your profitability.
What’s apparent is that no one knows what the new normal will look like in retail. Shifting customer behavior, some voluntary and some regulated, is changing the landscape of this industry. Retailers who use their data to reimagine their core platforms and strategies will be best positioned to deliver the value, affordability, and service levels their customers need.
The LRS Big Data and Analytics team has over 20 years of experience deploying applications in advanced analytics, information management, and data warehousing. Not sure how to get started? Our strategic offerings can help you align business and technology teams, discover right use case, and determine an ROI. If you are interested in understanding how we can help you find value in your data, please fill out the form below to request a meeting.
About the author
Steve Cavolick is a Senior Solution Architect with LRS IT Solutions. With over 20 years of experience in enterprise business analytics and information management, Steve is 100% focused on helping customers find value in their data to drive better business outcomes. Using technologies from best-of-breed vendors, he has created solutions for the retail, telco, manufacturing, distribution, financial services, gaming, and insurance industries.