The New Pillars of Data Analytics for Better Retail Now

Connie Walsh is Senior Director of Transformation Services at eCapital Advisors, an Incorta partner.

Retailers have had to do a lot of adjusting over the past decade. In 2019,  US retailers shuttered more than 9,800 stores, the highest number since tracking began in 2012. At 12,600 and counting, the number of store closures in 2020 has already topped that. 

What separates the winners from the losers? I would say that effective use of data, and taking action on this data has got to be a key factor. COVID has obviously pushed some companies that were already struggling into store closures and even  bankruptcy, but rising debt levels, and lack of adaptability to changing customer lifestyles and behavior have also played a role. 

For example, we still see very large retailers that don’t even have inventory visibility at the location level, or have not adjusted assortments to align with how the customer lives. There are still some holdouts that aren’t even up online. In a hyper competitive industry such as retail, missteps such as these will eventually catch up with you. COVID has simply underscored just how quickly customer behavior can change and made clear that in order to keep pace, retailers need to double down on their use of data and analytics.

The good news is, even if you’ve fallen behind you can catch up quickly. Years ago, implementations of software solutions took forever. Retailers today can quickly achieve a return on investment with modern technology, using data and insights to drive decision-making. Most of the solutions now are in the cloud, so the ability to get up and running is so much faster and cheaper. And the tools’ ability to handle data from disparate sources and serve it up to business users for them to query themselves is much better. 

But even with new technology, retailers have so much data at their disposal that it can be hard to know where to focus your efforts. That’s why we’re advocates of the “four pillars” framework to guide retailers in their analytics journey. Here are the most important things retailers should be looking to do when analyzing their data:

  1. Know your customers. This has always been at the core of retail, but in the always-on omni channel world, it’s getting harder to connect the dots, partly because there are always more dots. Since COVID, for example, you now have to understand behavior in new channels: BOPIS (buy online pick up in store) and BOPAC (buy online pick up at curb). You have to know what customers are buying; who is buying or not buying; understand differences between new and existing customers; and have a strategy for retention and for reactivating lapsed customers. You need to understand their buying behaviors—are they seasonal or promotional buyers, or operating on some other cadence? The matrix for each customer continues to grow in complexity, and you need a full view into all of it because you can’t make good business decisions looking at data from just one channel.
  2. Empower your employees. If you’re a store manager, you want to see what your top selling items were yesterday in your store so you can re-merchandise. If you’re on the buying or planning side, you want to ensure that you have the right assortment or inventory position. If you’re in finance, you want to be looking at sales trends and profitability. That’s always been the case, but the bar has been raised in terms of speed and access to data. It’s no longer even close to good enough to send out a  report or an Excel sheet based on last week’s data, with no ability to drill into the numbers. You need to have the same data available to everyone, down to the store employee, in real time, and give them the ability to explore it and ask questions and get answers right away. You  should not have to go to IT every time you  want a new report. Extreme changes in shopping behavior have created a new urgency around getting immediate answers.
  3. Deliver intelligent supply chain. Pre-COVID, retailers tended to focus on breadth of assortment to satisfy shoppers’ brand preferences. On a dime, their preferences shifted to, “I don’t care what brand it is; please just have it in stock.” The speed of impact caused whiplash in the supply chain. We’ve never heard so much conversation about cash flow, down to daily levels, and we’ve never been faced with so many critical questions all at once. Chief among them: if this continues, what does that look like? If you’re shifting to a lot more online business, return rates are higher. What are the implications of that? What about shipping costs? Do you absorb them? Is it even possible to maintain bottom line profitability as your business shifts? If you’re aiming for a forty five percent gross margin, does that include returns, shipping and handling? Many companies don’t have a very good handle on that. You need to be able to combine all that data to allocate the proper cost to each part of the business and revamp your supply chain—and fast.
  4. Reimagine the future of retail. This is a little bit loose, because it’s future based and reimagining is going to mean something different for every retailer. The main thing, from a data analytics standpoint, is to place less emphasis on what happened in the past. A lot of that is no longer relevant. Today’s task is to figure out how to use the now to forecast what will happen tomorrow. We need to be able to model more scenarios. We used to do a lot of good, better, best modeling. Now retailers need to be prepared for any situation. The other big need is to be able to take advantage of external data. For example, we had one client who pulled in COVID data from Johns Hopkins by zip code and then analyzed it against how the business was trending, glean insights around products that are working better or worse by state or even city. You can look at unemployment rates and weather patterns to make sure you’re taking appropriate markdowns. Another one we looked at was competition. When a competitor closes, how does that impact your business now? A year from now? There’s almost no limit to the external data available, and being able to combine that with your internal data could yield some of the biggest insights for reimagining your offering.

The fundamentals of retail really haven’t changed, but the speed and complexity of it has. Faced with more data than ever, it is important to be curious, ask the right questions, analyze the data and understand how your customers are shopping, what is working and what is not, and be prepared to take action. And then continue to iterate as your business evolves.

Working with data really is a never-ending journey, and part of the work is to bring it all together along the way. These pillars do not stand alone; they work together to support your organization. 

But you don’t have to do everything right now. You don’t need to pull all of your data sources at once. Pick a few key areas and a few data sets and see what you can learn. Then go deeper. For example, if you haven’t looked at return rates, dig into the data you have on that. Once you know your return rate, start digging into reason codes and break those down. What are the most common reasons people return items? Or, look at returns by item, and see if there are items that are returned at a much higher rate. It’s an ongoing process of getting more data; generating insights on that data; asking new questions based on those insights and then making decisions; collecting data and continuing to iterate.

What we’ve seen is that sophistication around using data is not aligned with the size of the company, and that size alone is not a guarantee of continued survival. These days, nothing guarantees that, but the retailers that can use data to react quickly or get in front of trends, along with making the right decisions are going to the ones that rise above the rest.

Where are you in your analytics journey? Join me and Dr. Fern Halper, VP and Sr. Research Director for Advanced Analytics at TDWI and Tara Ryan, CMO of Incorta, for a live webinar, “Reimagining Retail in the New Normal: The Role of Data and Analytics,” Thursday August 27 at 9 am PT. Register now! 

Connie Walsh is Senior Director of Transformation Services at eCapital Advisors. She has 20+ years of experience helping organizations achieve meaningful growth through envisioning new technological possibilities and improving day to day operations. She loves to dive deep on what companies are currently doing and empower them to translate their data to grow business and increase bottom-line profitability.