Big Data is Big News. But What Are People Actually Doing About It? Here are a few ideas how Store Operations could benefit.
The world creates 5 exabytes of data every 2 days, which is roughly the same amount of data created between the dawn of civilization and 2003, Google CEO Eric Schmidt said famously at a tech conference in 2010. (An exabyte is a 1 with 18 zeros after it.) While there’s debate as to the accuracy of his figures, there is no debating the explosion in structured data, and increasingly, unstructured social media data such as consumer comments and opinions, that is available to retailers today.
Many retail organizations appear to be challenged to make sense and extract value from their data. Most retailers do not know how much structured and unstructured data they currently store, according to a report based on a survey of 70-plus retailers released September 2012 by Edgell Knowledge Network (EKN). Maybe that’s why much of the literature (white papers, reports, etc.) on Big Data neglects or glosses over an important point: the need to ensure the right actions are carried out in response to gained insights. How can you determine what to do if you don’t know what you have?
One Big Data report that does touch on the value of execution — albeit briefly — comes from the Economist Intelligence Unit (EIU). In a survey of 752 executives, 67% said acting on data in real time is either very or somewhat important. But that nugget of information is mentioned on page 25 of the 30-page report. It appears “thought leaders” have neglected an important aspect of Big Data: institutionalizing best practice response throughout the organization to new insights.
As a former retail technology director for a large multi-banner supermarket retailer, I couldn’t help but notice another aspect of these reports: the low degree to which respondents say Big Data will help operations. In the EKN report, retailers said the areas that could most benefit were merchandising (62%), marketing (60%), and multi-channel (44%). Store Operations came in fifth at 25%. In the EIU report in response to a similar question, operations didn’t appear at all, except possibly as “Other” (13%).
But I can easily come up with several examples of how a platform that can pull from a variety of disparate data sources, analyze it, and when appropriate send best-practice alerts can help retailers solve age-old store problems and increase sales and profitability. Here are just a few:
- Out of Stocks. A historically stubborn problem. But what if the system could monitor a preset item movement threshold metric coming from the POS or inventory system for the top 25 selling items? When products are in danger of going out of stock, the store manager and/or stock clerk receive the alert on smart phone, kiosk, or printer with a list of items to restock, arranged with location and hierarchy of sales. The result: reduced out of stocks, increased sales, and a better customer shopping experience.
- High Value Customer Arrival. Retailers know some customers spend more — sometimes way more — than others. What if when high value customers check in (via social media, email, or smart phone), they get a tailored promotion sent to their phone; meanwhile, the appropriate manager is alerted to the arrival along with a recent shopping history summary. Instead of getting a promotion at checkout, the customer is greeted by a promotion and an informed associate. The result: the associate can sell more to the customer and provide better customer service.
- Product Launches. They take a lot of time and planning but are potentially great sources of increased revenue and profitability — if executed correctly. What if the system could monitor sales of the new product by store, along with compliance status as to whether the NPI was set up correctly? If sales in a store are below expectations, the system sends an alert to the store’s manager with a list of best practice corrective actions. The result: increased sales and better analysis of why sales were better in some locations, factoring in localization and how well the launch was executed at each location.
The above examples would benefit by having store managers with smart phones so they could get these alerts from the sales floor, but as I mentioned, the alerts could also be sent to the customer service desk or a dedicated printer or PC. I have lots of other ideas in the areas of workforce management, buy online pickup in-store, unusual customer traffic fluctuations, truck arrivals, weather, and more. But I’ve written enough.
So put on your visionary hat, store operations experts. What scenarios could you envision for store operations involving Big Data and real-time best-practice alerts? And if you’d like to learn more about the Reflexis vision for all this, please drop me a line.
– Kevin Carleton, Senior Director of Customer Engagement, Reflexis Systems Inc.