Last week I had the opportunity to attend the National Retail Foundation’s Big Show in New York City—self-billed as the place “where the entire retail industry comes together to hear from the biggest changemakers, experience the latest innovations, and make the relationships that matter most.” The conference has 35,000 attendees, 175+ sessions, 350+ speakers, and over 1,000 exhibitors. It was my first time attending in person, and I was excited to see a lot of new products and innovative ideas being displayed and discussed. As a part of a data and analytics consulting firm like Aspirent, I was especially interested in what retailers are doing and thinking about in terms of data, analytics, AI, ML, and predictions. Here are my five biggest takeaways from three very big days.
1. Data collection and storage is easier thanks to technology and storage advancements, but creating actionable insights from the data continues to be a challenge.
The exhibit hall and panel sessions were full of many new and sometimes cool hardware devices for point-of-sale, scanning, RFID, etc. For example, retailers now can deploy a camera, use a mobile phone, or send a video-enabled robot through their stores to capture images from shelves and displays. By applying an AI engine to these images, they can then track stock-outs or empty facings of products on their shelves—no more manual counting required. They also know what SKUs are being sold at what time and at what store; when a product was delivered and returned. Retailers are gathering more data about their customers’ purchasing habits than ever before. Now the cloud is making it easier and more cost effective to store the mountains of data they’re amassing. These data mountains aren’t just internally generated data anymore. External factors like the weather can have a huge impact on demand—and data like this can now be easily accessed and stored in the cloud. Clearly data collection and storage are no longer the biggest challenge facing retailers.
Interestingly enough, the solution to one problem can often lead to the creation of another: how to use the data you have for maximum impact. What retailers do with the data—how they generate and act on the business insights it holds to gain an upper hand in the market—remains a struggle.
2. Next-level analytics are in high demand, but hard to make happen.
How much progress are companies making in their efforts to extract valuable insights from their data? There was a lot of talk about big data, AI, predictive insights—all the right buzzwords—but it seems to vary greatly by retailer. While some are doing an amazing job, many others are still working their way up the maturity curve to where the real value lies. They are trying to move from looking at what has happened (“reading the news” as we call it) to a more forward-looking, predictive view that enables better, faster decision making—even recommending what actions to take.
Moving beyond historical reports on monthly sales figures or on-time delivery percentages to develop analytics that help you make informed business decisions can be an elusive goal. As one retailer said as they started their analytics journey, “We do a really good job of reporting all this data to the field. We have some really good store managers that can interpret the data and make the right decisions, but we have some that just can’t.” They realized this was the wrong way of thinking. The advice from their own learnings: “Don’t just send your stores the news—i.e., a spreadsheet with a wall of numbers. Send them insights that say, ‘Here are some specific problems in your store, and here are some recommended decisions you can make and actions you can take.’”
This capability requires a decision-based approach to analytics versus a traditional “send me all the data” approach. That’s why we built a framework, Decision Architecture, that focuses retailers on the decisions they need to make, and the analytics required to make them. It does more than just visualize the data they have (which may not even be relevant to the issues they are trying to address)—it delivers usable in-market, in-store insights faster. This means your manager who’s responsible for $1M in weekly store revenue isn’t spending Monday and Tuesday sorting through spreadsheets to try to figure out what happened last week, what’s going on in his store, and what issues and actions to focus on for the week. These answers are already in their weekly report when it comes out. The demand for analytics that make this possible – analytics that not only shows what is happening, but provide recommendations for how to respond—is only growing. Even more valuable is the ability to predict what might happen based on known factors coupled with recommended actions. Whoever can do this faster, better, and more consistently could determine who gets a bigger piece of the retail industry pie.
3. Data is being valued as a core asset – and treated as a product.
Data is finally, if not fully, being recognized by retailers as a core asset that needs to be developed and nurtured like a product to realize its full value to their business.
Agile was a common topic in this vein—how companies are using Agile teams and practices to keep things small, deliver quickly, adapt more easily to changing priorities and develop something else if/as necessary.
A grocery store chain in Australia, presented a compelling example of how to orient its business around data as a core asset and then manage it as a product. About five years ago, they started to put all their data in the cloud and treat it as a business asset. Within the last two years they partnered with a data science company to start driving value out of that asset. Now they drive insights along the five common retail verticals– Customer, Buying & Merchandising, Store Operations, Replenishment & Supply, and Support. Their ongoing journey and roadmap focused on continuing to extract value from the data they collect.
4. A cohesive data strategy makes all this progress possible.
It’s easy to get swept up in the latest tools and technologies for gathering, analyzing, and visualizing data. I know I did in the three days I was at NRF. However, what really lies at the heart of any retailer’s success is their ability to simplify and unify their data landscape—to create a single source of truth for the enterprise. When organizations don’t have this in place, they end up creating point solutions that cause confusion and conflict over the accuracy of the insights they rely on to run their business. This applies not only to actuals data—yesterday’s news—but also planning data.
Take forecasting as an example—supply chain, merchandising, and workforce teams often create their own unique forecasts—oftentimes from variety of different data sources. Who’s forecast is “more” accurate? Which one should we rely on to make this critical restocking decision? Executive meetings spend more time talking about whose forecast is accurate instead of strategically considering their next steps.
When you have a common platform in place, you minimize these discrepancies—or at least have a lot less of them. You can focus analytics and insights teams on refining one forecast and making it more accurate and actionable. The executive team is equipped with insights to help them focus on the strategy to take the business to the next level.
The collective advice from many of the NRF sessions (and the team at Aspirent): Pick one tool and one methodology to do your forecasting at an enterprise level and have that inform all your different departments.
5. A common customer experience across channels is the ultimate goal.
You have all the data you need; you’ve unified it in a common, trustworthy platform (or at least you’re trying to); you’re building sophisticated analytics off the platform and managing them like products. What’s it all for? To create a seamless and satisfying experience for your customers. Whether they are buying online or in-store, getting items delivered or picking up curbside, customers want retailers to make it as frictionless as possible for them to act on their purchasing habits and preferences.
If they are shopping on-line, they want to know what’s available in the store closest to them as well as shipping details in case they want to pick it up instead. If they’re in a store and don’t see the product on the shelf, they want a store assistant to see if it’s in the back room, and if not, they may want to have it shipped to their house. The challenge for retailers is how to bring their data platforms/sources and analytics in line with these customer expectations. How to equip the different areas of their business—the labor, inventory, logistics teams, and more—to be looking at and acting on data from a unified, customer-driven perspective.
The good news is—and I know this not just from attending NRF, but also from Aspirent’s work with leaders in the industry—retailers have never been more focused on data. They are focused on not just amassing it, not just visualizing it, not just plucking valuable insights here and there, but on doing the innovative thinking and the hard work to make it the center of their roadmaps as they move forward.