Retailers Convert Traffic into Sales

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Retailers Convert Traffic into Sales

By Steven Keith Platt - 12/02/2014
Advances in store location-based technologies are empowering retailers with cutting edge insights about consumer behavior in their stores (these technologies include, among others: Bluetooth low energy beacons, WiFi, RFID, magnetic field, and visible light communications). A major benefit associated with these new technology platforms is that they can be inexpensive to use, when compared to more traditional methods. Now, system wide placement is financially possible.  
 
In general, this technology enables a retailer to collect information on the following: 
  • Date, time, and number of customers detected in a store.
  • Store traffic patterns.
  • In-store location dwell time.
  • Store penetration rates.
  • Repeat visits.
In this article we highlight how this technology enables retailers to learn more about consumer behavior in-store and use that information to improve operating performance. To learn more about this topic, please register for the December 11, 2014 RIS-PRI webinar, "Driving Store Sales by Leveraging Location-Based Analytics."
 
Store Traffic
Traffic data is a key store performance measure because store revenue and merchandise management information (such as inventory turnover and gross margin) might not reveal the entire picture. Consider two comparable stores located in the same town, for example. Top- and bottom-line numbers are the same, so one may draw the conclusion the stores have comparable performance characteristics. However, one of the stores has 12% more traffic. The inability of this store to convert traffic into sales points to various performance shortcomings. These may include poor staff training, inconsistent merchandising or out-of-stock positions, a failure to properly institute promotional programs, or extended wait-times, which are causing customer abandonment.
 
Store Traffic Patterns
The study of store traffic patterns can help a retailer understand and improve many operational aspects, including:
  • Optimizing store and fixture layout.
  • Improving merchandise placement.
  • Programming in-store marketing activities based on traffic flow and speed.
  • Positioning staff to improve customer service.
  • Directing customers from crowded to less crowed areas to increase asset utilization and customer satisfaction.
 
In-Store Location Dwell Time
Tracking the duration of customer store visits can help a retailer understand its progress in attracting and keeping customers engaged. Introducing revenue measures into the equation can provide insights into spending levels during each visit, and how various promotional and marketing activities are affecting sales based on time spent in the store. In addition, store dwell time, when combined with repeat visit information, can provide customer loyalty insights.
 
Store Penetration Rates
Store penetration refers to the number of customers that walk past a store versus entering it. While not useful in all instances, measurement of this in a mall environment, for example, can yield interesting insights. To illustrate, it is possible to study whether messaging (static or digital) or product merchandising visible at the front of the store is successful in drawing customers into the store. This measure can also be helpful in understanding whether various other marketing and media campaigns are driving traffic into the store.
 
Repeat Visits
This is useful for understanding if out-of-store marketing activities, such as a direct mail campaign, are successful in drawing customers back to the store. Also, it is possible to estimate product and service purchase cycles and how they are influenced by various marketing activities. Further, measuring repeat customer visits can lead to new insights into loyalty programs with a retailer's best customers, as well as tracking new visitors.
 
This article discusses the wealth of real-time insights generated from customer location-based analytics. The five core customer location-based analytics discussed, when combined with other data, such as POS and human resource information, can provide added store performance measures.
 
Steven Keith Platt is director and research fellow at Platt Retail Institute and research director of the Consumer Analytics Institute at Northwestern University.