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Uncover data secrets with retail analytics

Retail analytics expert Emmett Cox explores the challenges of “big data” and provides tips for businesses to make use of the information they’ve collected.

Retail organizations, like most industries these days, are in the midst of a data glut, and determining how to make the most of that information can be a challenge. Emmett Cox, who has worked for companies such as Wal-Mart and General Electric, believes there are tricks businesses can take advantage of to reap the data rewards.

Cox, the author of Retail Analytics: The Secret Weapon, comes to the topic with years of experience. At 17, he landed a job stocking shelves and experimented with how product placements could impact sales. In addition to his retail background, which extends into store management, Cox is an analyst by trade, graduating from college with an electrical engineering degree. It’s a combination he calls “a perfect blend of the science and arts” of retail analytics -- one that enables him to understand the challenges of both worlds and to operate between the two. recently sat down with Cox, now the senior vice president of consumer experience for BBVA Compass bank, to discuss the growing interest in retail analytics and applying his tips beyond the retail sector.

What makes managing retail data so challenging these days?

Cox: The trend lately has been to bring in pure MBAs, who have a strong quantitative approach to problem solving, but many don’t have the arts behind it. They haven’t lived through the issues of trying to resolve how customers shop or what hurdles exist when selling the product. Looking at the quantitative abstract view of data without having the application perspective of what you’re trying to solve for is a hurdle people are going through right now. Companies are starting to pull back on that, looking for more depth of field and not so much the breadth of knowledge of a specific science. There’s so much pure talent out there, but many haven’t had the opportunity to see what they’re solving for. That’s probably one of the biggest challenges I’ve seen.

For a business looking to make use of data it has been collecting but hasn’t acted on, what would be one important tip to keep in mind?

Cox: Senior leadership and direction. You can have all the data in the world; you can have all the talent in the world, but if you don’t have a great leader or leadership program directing what the issues are that need to be solved and the impact to the business along the way, you will constantly be derailed. It has to be CEO- and CIO-driven. It has to be a consistent message all the way down. As I toured different businesses around the world, it’s usually the CEO who recognizes there’s an issue. They’re the ones with the impetus to make the change, to put the data to use, to make the investment in people, to make the investment of time to push that change through. If you don’t have that at highest level, it won’t work.

One strategy example you’ve cited is finding the relationship, or what you called the affinity, between products and especially across categories. What did you mean by that?

Cox: One of the leading trends probably in the last 15 years has been category management or, within a category, finding the products that sell well or the products that don’t. Then you can go through SKU [stock-keeping unit] rationalization or the process of eliminating the products that didn’t do so well and increasing the space for new products or better-selling products. But category management is a one-dimensional block in the decision process. In other words, it only looks at products within a single category. This is an example of cross category: looking at salty snacks and shoes or salty snacks and hardware. It’s outside of the silo to know that someone who buys from category A also buys from a totally different division on the other side of the store, and that there’s a relationship between those [items]. If you are out of stock on a prime product in one category with a strong affinity to another product in a second category, that one out-of-stock product has a chain effect across a store and across different products for the people buying it. It’s easy to do category management. It’s difficult to do cross-category management.

What makes it so difficult?

Cox: Most retail organizations, most organizations, are siloed. You’ll have someone in charge of salty snacks, and you’ll have someone in charge of ladies’ apparel, but those two people don’t talk. You might have a CEO, but they’re not at the level of visibility that they’re going to see those things occur. You might have a vice president in charge of all edible foods, but they won’t talk to the vice president in charge of apparel or men’s wear. So there typically isn’t anyone involved at that level of merchandise that can take advantage of those types of things. That’s something purely driven by the analysts, purely driven by the market specialist that can bring those things to light. That happens infrequently because of the structure of an organization.

Seems like common sense.

Cox: Yes, nothing I talk about is magic. It’s all pretty much common sense when you see it.

And it sounds like a common story: Businesses struggling with silos.

Cox: I’ve worked on both sides. I’ve worked on the physical, logical architecture, database design. I’ve worked on store planning grants and building the layouts of merchandise. And I’ve worked in organizations where I was part of the senior team or I was the one developing the promotion. So, for me, it makes perfect sense that it all has to gel together because I’ve worked in all those positions. I know the data has to be organized properly or you can’t use it; the data has to be clean or it doesn’t make any sense; you have to have an analytics team to bring that information to light; and you have to have someone on merchandising to actually utilize it. When the links are weak, it won’t work. That’s why it’s so difficult for organizations to get these things out there. They all see it and say, “yeah.” But to apply it in a real-world example is very different. The ones that do are the ones that succeed.

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