Designing the store of the future around the changing consumer

by Tom Martell – Market Leader, Retail Vertical at N3N

To separate themselves from the pack, today’s retailers must create seamless customer experiences that traverse the entire customer journey. Retailers that don’t adapt to changing consumer needs will not survive beyond the near-term.

Keeping pace with the evolving customer journey

With rapid transformation across the retail industry, the ability to create unique and engaging in-store experiences has become critical to a physical retailer’s success. As a result, customer engagement and performance analytics are becoming core to every retailer’s strategy, and accurate measurement of the key in-store engagement metrics is the need of the hour.

While it’s possible to collect pre-shopping (traffic, weather, seasonality, etc.) and post-shopping (sales, repeat, etc.) data, it is quite difficult to capture the context of the shopper when they are within a retail environment. It’s really tough today to accurately track how customers respond to visual and environmental cues when faced with multiple decisions in a store, as well as how store associates enable shoppers when they are making these complex decisions. Specifically, retailers would like to know everything from how customers move from section to section, where they dwell and buy, and where they dwell and don’t buy. Retailers need this data to know what’s going on within their stores, and to understand their customers’ preferences.

Retailers must have visibility to every aspect of the customer journey in order to understand how to engage them at every step of the way, from getting her in the door to optimizing her customer experience in-store, to making the most of her visit, to continuing the conversation once she has left the store. Each touchpoint can either make or break the customer experience:

Building the store of the future

As retailers look to adapt to the needs of the evolving consumer and build the store of the future, they will need a deeper understanding of customer and staff movements to ensure that there is adequate staffing to support customer demand and to better understand how staff interacts with shoppers to enable the best possible experience. Empowered by this data, retailers can optimize staffing and other store operations and gain insights into the following key areas to improve day-to-day operations and decision-making:

  • Dwell Analytics – Understanding where customers spend their time, whether it is interacting with specific fixtures or locations within a store, allows retailers to determine if targeted, high-margin areas or fixtures are gaining the attention of customers and converting to sales. Additionally, this creates an opportunity for retailers to assign sales associates to key areas to drive engagement, enhance the shopper experience and increase conversion.
  • Employee Pathing – Monitoring the overall pathing behavior of employees helps retailers optimize their staffing needs to better understand how employees help guide the shopper journey. This is also a great opportunity to understand employee adherence to the overall service strategy established for the stores.
  • Customer Engagement –  Understanding where, how frequently, and how long employees engage with shoppers helps retailers determine which types of interactions lead to increased sales and conversion. Modeling successful engagements and scaling those behaviors to an entire network of stores can have a positive material effect on store performance.
  • Queue Efficiency – Driving efficiency at the queue provides 2 key values to the retailer: (1) The more efficient the queue, the faster customers are able to check out, which leads to, (2) Efficient queue management leads to a more positive customer experience, which results in repeat customer visits.
  • Heat Maps – Heat maps offer a great visualization of aggregated data over a defined period of time. Retailers rely on this data to understand areas of activity and those that lack (dead-zones). These tools allow retailers to test why areas are receiving (or not receiving) activity.  The store layout may need to be addressed or staffing can be assigned to help drive traffic to areas that are not receiving adequate activity. Again, this is a quick visualization tool that helps a retailer determine if there is a need to dive deeper into a trend that might be developing.
  • Mobile Device Detection (MDD) – MDD helps retailers understand whether a customer is a new or repeat visitor. Understanding this metric and continuing to drive repeat visits is a goal for retailers. Retailers can also use MDD to measure the success of marketing promotions, helping retailers to test and refine their overall store marketing plan.

Bringing the data together to deliver a seamless experience

By uniting Internet of Things (IoT) data from sensors, cameras, WiFi, and RFID with data from legacy systems onto a single canvas, retailers can gain a complete picture of store operations and customer experience. Additionally, consolidation of visualizations between job functions reduces costs and drives optimization for retailers by enabling them to leverage information and insights across the organization – overcoming the current siloed data environment.

Retailers that invest in gaining a deeper understanding of customer and staff movements, experience and behaviors using analytics and operations visualization tools, and take action on insights derived to keep pace with the evolving customer journey will stay ahead of the curve.

N3N helps you visualize all of your retail operations, gain real-time actionable insights and take action on workflows, processes and connected things to stay ahead of trends and deliver the personalized shopping experiences today’s retail customers expect. Learn more at n3n.io/retail