Think Like a Shopper

Real-Time Analytics are the Key to Improving the Customer Experience

Heatmap Composite

In order to provide a better experience, retailers need to think and act like their Web- and social-media savvy customers, even when it comes to the in-store experience.  A recent presentation given by Hughes at NRF’s BIG Show indicated that at least 20 percent of in-store sales are influenced by online behavior.  That number is continuing to rise, partly due to the millennial generation.  A full 82 percent of millennial shoppers research products online prior to purchase. 

While most talk is centered on the Omni-channel experience – including digital marketing, e-commerce, customer service, and social media interaction – one area that is often overlooked is the brick-and-mortar experience.  Yet, customers who do not receive good in-store service will walk away from the brand.  

Understanding customers’ needs through the buying cycle – and supporting their needs post-sale – is what makes shoppers loyal to certain stores and brands.  To create a better customer experience, retailers, CPG companies, banks and manufacturers are already interpreting shopper data in order to better understand customer behaviors and needs.  The question is, how can retailers use those same metrics in order to create seamless and positive in-store experiences?

For retailers looking to improve in-store experiences, analytics are the key.  Analytics can help retail professionals more effectively manage the store from the moment a customer enters to the time he or she leaves.  Are products in the right place at the right time?  Are the checkout lines too long?  Are there “traffic jams” in any aisles?  Knowing the answers to these questions helps stores better utilize every bit of real estate and ensure they maximize sales for each customer.

 

Dwell Analytics

When a shopper enters a store, one of the first things he or she sees are the displays near the entries.  Capturing dwell analytics establishes how many people lingered at the display and how long they stayed.  This can help marketers understand what attracts people and allows them to make changes quickly if people are not engaging.

Dwell analytics can be used in other areas of a store too, helping retail professionals establish patterns throughout the space.  Are customers spending more time at the sweater tables, or looking at dresses or accessories?  Do people tend to linger at the technology displays, yet move on without an item in their carts?  This type of knowledge helps retailers understand what areas and what items hold interest and convert to actual sales.  

Finally, dwell analytics are extremely useful at checkout lines.  Once customers have filled their carts, retailers can ensure that shoppers are not standing in line for an unreasonable amount of time.  This helps cut down on the number of abandoned carts due to lengthy checkouts and impatient customers.  

 

People Counting & Demographics

People counting and demographic data provide retailers with general insight into the types of customers in the store.  This allows retail professionals to provide the right staffing and inventory based on metrics such as gender and age, and it allows them to target discounts and specials to the right groups at the right time.  

For example, perhaps the store is often visited by teenagers on weekday afternoons when the high school down the street dismisses students for the day.  But on the weekends, most shoppers are 30-to 40-year-old parents with young children in tow.  Having this data can help stores maximize sales by having the right items and incentives front and center.  

 

Directional Heat Maps

Directional heat maps help provide a visual for how customers move through a store.  The heat map literally measures the concentration of customers at each location, with areas of heavy traffic showing up in red.  Areas with fewer visitors are depicted by shades of yellow or blue.  The directional aspect is overlaid with this data to show which way customers tend to move through the store.  This provides an understanding for how aisles and displays should be arranged in order to maximize customer attention yet minimize bottlenecks in the aisles.  

 

A Complete View

All the analytic data collected at each store location provides retailers an understanding of performance across various geographies and seasonal offerings.  Combining this with point of sale, online and social data provides a more complete view of customer and brand engagement.

The in-store experience is more than just buying a product.  It is the interaction with store employees, and finding the right product at the right location which makes for a better customer experience.

 

Next Blog: Brand awareness and trending data for better customer insights between locations 


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