Making Sense of Shopping Data

Using Brand Awareness and Trend Data to Generate Insights into Customer Behaviors

Almost all of us have stores we go to regularly, and stores we do our best to avoid. But what makes the difference between stores we love, and stores we love to hate? Is it simply the selection of merchandise? Is it the availability of staff, or the hours the store is open? The answer probably does not come down to a single data point, but rather a variety of factors that influence our shopping behavior and loyalty to certain retailers.

For many retail executives, understanding what drives customer behavior in the store feels like it could be a plot from Mission: Impossible. Making sense of the all the customer and sales data from even one store – let alone from hundreds or thousands of stores – can be a monumental task. Yet, it’s critical to capture and understand this data in order to ensure that all stores are performing as best they possibly can.

Thankfully, retailers can now utilize big data and analytics in order to generate insights into customer behavior and store performance. Information is gathered from multiple sources such as cameras, point of sale systems, people counters and other devices and then correlated by software in order to generate meaningful data points. This gives retailers insights into trends like how many people walked into a store, which departments they visited, what they really purchased, and how much money was ultimately made.

Shopping Data

Customer Insights software from 3VR can help retailers establish important metrics such as customer traffic, conversion rates, shopper demographics, and the effectiveness of displays.

Establishing In-Store Trends

Products such as Customer Insights using video analytics from 3VR are a critical component for establishing trends at a single store and across multiple locations. The software helps retailers capture useful information such as store traffic, conversion rates by store or by department, shopper demographics, wait times, the effectiveness of displays, and traffic flow.

Shopper demographics can change from store-to-store, or even at a single location depending on the time of day. Having data regarding the age and gender of customers can help retailers make sure each location has the appropriate personnel, and the products and displays that best match shopper demographics.

In addition, spending trends can change by time of day or day of the week. Perhaps one location is generally very busy on Wednesday evenings, so it makes sense to keep that location open for an hour or two longer than other stores in the area. For stores that are busiest in the mornings, it’s important to make sure that shelves are restocked and everything is in order the night before. In addition, the number of employees should be adjusted to match customer demand. As any store manager knows, under- or over-staffing can both have a negative impact on sales.

Once trends for shopper counts and spend rates are established, retailers can use the same analytics to drill down a level further. For example, one store might not have a high number of shoppers overall, but perhaps those who do come in tend to visit a particular department more than others.

Improving Customer Experiences

Having these metrics helps improve store operations and customer service, which ultimately leads to positive customer experiences. Making sure a store runs smoothly on a day-to-day basis and can give shoppers the merchandise and services that they want is critical to success