You know, sometimes the problem is the answer.

This will be anathema to some readers, but perhaps retail shouldn’t abandon their intuition and normal human communication for technology and data. Maybe the slowness to do so is the universe telling retailers that it’s not the way to go.

As I was reading this I had the picture of customers and retailers in a data-duel. Customers are using their data tools to define and find their preferred retailer. Retailers are using their data tools to define and appeal to their preferred customers. That’s like a dating service where two people stare at each others’ picture and review data but never actually meet, or have dinner, or a dance or a hug. No one is going to say “Let’s be a couple, I love your data.”

Relationships ARE intuitive — of the heart. I’d say we need more of that not less.

I have near-weekly meetings with retailers and have been asking this question for over a year. Their answer is remarkable: while nearly all have multiple analytics packages, they are silo’ed by department, without a master plan for execution or vision for what could be accomplished. It’s hard to accomplish something without a plan.

If you need evidence that retailers have been too slow to be leverage analytics and be more data-driven, just look at their stock prices. There is an increasingly clear and sizable gap between retailers that are customer-centric and data-driven versus and more traditional merchants. Using data and, in turn, analytics to support, inform and drive the business is essential and a core element of being customer-centric. Like Amazon.

Retailers have under-invested in technology to leverage data — especially beyond systems to support merchandise information — and have been lagging in areas like relationship and loyalty marketing. While there are plenty of (generally weak) retail loyalty programs, the key value of such programs is the data yield and the output is a better customer experience. Like Amazon.

Until retail leadership makes the customer a priority in the overall business plan, leveraging analytics, moving beyond mass marketing and creating more valuable customer relationships to drive the business won’t happen.

All industries are struggling. Some retailers have done an outstanding job with analytics related to logistics and replenishment. Some retailers are experimenting with innovative loyalty programs. Some retailers are sharing data with partners to develop products, services and/or experiences for joint consumers. None are doing all of these things.

Analytics is a broad term including logistics, consumer data, pricing, product development, pricing and assortment. No retailers excel in all areas but this is a journey that needs dedication to one area at a time every year to stay competitive.

It’s dangerous to paint an entire industry into a corner; retailers don’t operate on a single speed or mode, yet at the risk of doing just that, let me point to a more fundamental problem that may shed some light on today’s discussion. What tends to be more true than not is that silos continue to not only exist but to thrive in organizations. The role and turf division across the retailing enterprise: merchandising, store operations, and marketing remains alive and well. Each will take their own view of reality and by extension develop their own approach to analytics and their user experience. It’s not only the tug of war between centralized and decentralized operations but defining where that line ought to be for strategy and long-term investment in the business. Lacking strong, engaged, and determined leadership at the top, each of the silos will “run with the ball” as they see fit. That’s not inherently good or bad, but with the fast pace of change in this digital economy, having an inconsistent or nonexistent… Read more »

The challenge in adapting to being a data-driven enterprise relates somewhat to the Yin and the Yang of retail, art and science so to speak. Data is plentiful, “context” to that data is illusive at best. Some retailers take pride in their success in understanding the customer, and applying “merchant sense” to the market, whether that is gut feel, or fashion sense, this non data driven intuition is a necessary ingredient for success. On the other side of the table is the pure data driven analytics; scientists sometimes end up with an answer in search of a problem. Success requires putting these two often polar opposite skills and approaches together in a single business. Data and context — easy to say, really, really hard to combine for success.

The problem with using data is that many companies/retailers get too much data and don’t know what to do. They either get “analysis paralysis” or make a mistake by using it the wrong way. But data is powerful, and the best retailers know how to use data. They use Big Data to spot trends. They use micro-data to individualize the experience for individual customers. I’ve had the wonderful opportunity to attend conferences focused on data analytics (Sounds boring, but it is not!) and learned how the best retailers are using data to grow revenue and improve the CX. It’s nothing short of fascinating — how they acquire it and how they use it.

With the abundance of data available to retailers, real-time analytics represents a prime opportunity to improve decision making and enhance the customer experience. While there is no argument that many retailers have been slow to adopt and implement advanced analytics to improve their operations. As indicated above, innovative retailers, like Amazon, are leveraging analytics to transform the customer experience. Amazon leverages the data about online transactions because they have it available. Traditional retailers do 10%-20% of their sales online and are missing the analytic data from their brick & mortar operations which is 80%-90%+. Until traditional store-based transactions move to the cloud and become part of the real time retail infrastructure, they will be at a disadvantage. This is raising the bar for the retail industry. The good news is that retailers realize they need to leverage analytics to be successful. According to a BRP special report on the Top 10 Merchandise Planning Priorities for 2016, 58% of retailers indicated analytics as their top planning priority. Retailers are finding that many of their current planning… Read more »

In my experience working in retail customer analytics, I have found that the biggest challenges have nothing to do with analytics at all. Rather, the hurdles involve organizational structure, culture and compensation. Accepting insight from a team outside the merchant group is often seen as a loss of power and influence to a group that has long believed that they run the company. The “gut instinct” and “I know the category better than any data could show” culture leads retailers to continue old merchandise selection and discounting practices. For example, every segmentation I have done has shown that some stores index higher for specific segments than others — yet how many retailers do you know that vary store layout and assortment based on that insight? Finally, compensation. If merchants are compensated on margin percent or simply on margin from their category alone, they will be unwilling to adjust products, offers or timing to meet customer needs. Instead they will drive the company to continue to “force” products down the throats of consumers who do not… Read more »

There is a fundamental weakness in this data argument: nearly all the data expected to be used is observed behavior data.

Using this type of data is fraught with extraordinary danger — in marketing I’ve found it to result in Rorschach data analysis. That’s the analysis where the data scientist or manager projects their own beliefs into the data in order to fill the gap of what’s entirely missing in this data: the why behind the behavior.

These leaps of faith filling in they “why” are dangerous. Because very often the data merely reveals some very tiny things. But managers and data scientists are expected to find big things — so they invent the whys and overstate whats in the data.

Retailers need to be cautious about the data fad.

First, I note that the instant poll here does not ask even a single question about shopper behavior, probably because there is a misguided assumption that the queried points are the major factors of shopper behavior — NOT! Secondly, I have spent the past 16 years intensely focused on generating data about how shoppers, you know, actually behave in stores. That is, move, act, purchase, etc. THIS is shopper science, which is driving Amazon to the head of the pack, and bricks retailers are largely oblivious to even what the issue is. Data scientists are of limited value in a field largely devoid of the actual data about shopper behavior in the store, that is relevant to sales and profits. The ignorance is so abysmal that until at least one retailer engages REAL shopper behavior data, they are sitting ducks for whatever competitor first gets a clue, that Amazon’s growing assault is NOT about the internet, but their relation to the shoppers. And as I explain in Chapter 3 of the second edition of Inside… Read more »

Some great observations already, so I’ll try to add constructively to the discussion. Risking over simplification, I believe it comes down to the old adage that any successful business solution, including analytics, needs the right combination of people, process, and technology — quite often in that order. People – Retailers need the right executive vision and alignment throughout the organization to truly leverage analytics to compete. Retailers need to break down the oft mentioned organizational silos and fiefdoms required to use a common view of the truth, including the analytics. Retailers need to invest in the skills, both hiring and internal development, and have the patience to see value come to fruition – not expecting miracles after 90 days. Process – Retailers have struggled for years to solve the challenge of master data. Analytics are just as difficult if not more so. Retailers need to define a process to gather data, apply analytics appropriately, acquire both data and analytics where they don’t have the ability to generate it themselves. Retailers need to recognize that they… Read more »