Brick and Mortar Store Genome

Traditional retailers have already lost online momentum but have an opportunity to win the in-store battle. There is no doubt that Amazon’s proposed acquisition of Whole Foods is going to accelerate online grocery shopping, putting increasing pressure on optimizing the performance of the brick & mortar store. Beyond growing online sales, Amazon will bring its data science to the physical store, analyzing shopper behavior just as it does behavior across its digital properties.

Merriam Webster broadly defines ‘genome’ as the genetic material of an organism. The retail industry has a unique opportunity to decode the brick & mortar store ‘genome’ and leverage improved understanding and new capabilities into dramatically increased store performance. And like the human genome project, the benefits are substantial and wide-ranging.

As one of the first supermarket operators to deploy video analytics years ago we found that specific products would invariably drive increased aisle traffic along with associated product sales. We learned the impact of on-shelf vs. off-shelf displays. Imagine doing promotion planning — or strategic personalized marketing — with a goal of maximizing shopper traffic across the store, knowing that as department, aisle, or category traffic increases, the sale of adjacent products also increase. In-store analytics represent a powerful opportunity for brick & mortar retailers to optimize store performance.

Here are five steps to optimizing the brick & mortar store:

Step 1 – Conversion rate scorecard: Ask nearly any store manager what their department, aisle, or category conversion rates are and you’ll probably get a blank stare. Even the best retail operators have very little understanding of true customer behavior in the store, let alone how it can be positively impacted.

There are a growing number of solutions using mobile device detection or digital video available to retailers to analyze the flow of customers in and around the store, including dwell time in specific areas and even purchase conversion. Retailers should create conversion rate scorecards showing customer traffic entering the store and then the percentage of shoppers going to departments, aisles, and categories. These scorecards can also reflect dwell events (the number of shoppers spending more than a specified amount of time in front of a category) and purchase conversion.

Digital video solutions using anonymous facial recognition are able to measure customer sentiment, measuring how many customers are happy, sad, frustrated, angry, etc. in different parts of the store. Other solutions using 3D motion sensors are able to understand behavior at the category, knowing what specific product a customer picks up and from what shelf.

Step 2 – Overlay with merchandising activity: Each year brand manufacturers and retailers pour billions of dollars into in-store merchandising events from special displays to special signage and more. Rarely is the impact of this promotional activity measured let alone the larger impact to customer behavior.

Retailers need to consistently and accurately track merchandising activity in each store each day and week. This would include location of on-shelf and off-shelf displays, special signage, endcap displays, and other merchandising activity.

Step 3 – Customer-intelligent product selection: Kroger and a (very) few other retailers leverage customer intelligence into store-level product assortment plans but the vast majority of retailers continue to stock their shelves with products that have always been carried, that their vendors ship in, or that syndicated data shows are good sellers in the market. No heed given to an understanding of the customers actually shopping the store.

Retailers can gain these insights either through loyalty programs which provide customer-identified purchase data or by using anonymous facial recognition capabilities which provide customer demographic data (gender, age, ethnicity).

Step 4 – Automate out-of-stock notifications along with merchandising and pricing compliance issues: There are a rapidly growing number of solutions available to help retailers automate and quickly discover product out-of-stocks, promotion compliance failures, too little or too much product inventory and even pricing issues around the store. Robots equipped with a full range of cameras and sensors are able to roam the aisles, neatly avoiding customers, to peruse the shelves. Cart mounted devices provide similar information and notifications as customers shop. And fixed camera deployments on high-volume categories or areas of the store add another layer of data.

Step 5 – Tie to realtime in-store marketing: Retailers such as Coborns, Foodtown, and Niemann Foods already have the ability to message a customer in the store based on realtime location. Imagine the power of knowing that if traffic in aisle 5 is lower than usual on Tuesday afternoon, communicating a promotion on a relevant product in that aisle to customers in the store can help drive aisle traffic along with sales of adjacent products.

Ignorance of true shopper behavior in-store will no longer stand as Amazon leverages its vast technological prowess to understand and then optimize the physical store. As the market stands today, traditional operators are behind the curve in online sales but the battle for physical store optimization is just getting started. Decoding the store genome will help retailers gain significant advantage as they leverage new understanding paired with new in-store marketing capabilities to pull ahead of traditional competitors and better position themselves for Amazon’s entry into brick & mortar retail.

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