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How to Leverage Guest Engagement Analytics from ExtremeLocation?

Ryan Hall Published 19 Jul 2018

In Part I of the ExtremeLocation Demo Series, we explored how Harnessing the Power of Location-Based Analytics offers valuable location-based insights to businesses, especially to those with a number of distributed locations, and how this data can be applied to improve an organization’s day-to-day operations and create an enhanced experience for guests.

In Part II of the Demo Series we dive deeper into the ExtremeLocation solution, to demonstrate the dashboard analytics function that the location engine supports and delivers. Check out the demo below to learn more.

 

In this demo, we’re able to see some of the guest engagement trends provided by the dashboard view of ExtremeLocation. 

Before even delving into the on-location insights, businesses can see the number of guests that walked by without entering their location.  To take it a step further, an organization can determine if a guest was labeled as ‘engaged’ (stayed longer than 5 minutes) or ‘bounced’ (left before 5 minutes).  Both categories are relevant and useful data points.  For example, retail stores rely on guests to drive sales and remain profitable; the more guests they attract usually equals more sales.  If a retail brand sees the foot traffic outside their store is strong, but guests aren’t entering inside, clearly there needs to be improvements to the outward appearance of the storefront.  If guests are entering the store but leave soon after, maybe the layout, products, or overall appearance inside the store needs to be improved.

Once inside the location, and after a guest dwells for an allotted amount of time (customizable by the business’ preferences), we can determine which guests are repeat visitors and which guests are new visitors.  This is important for a number of reasons.  If a brand rolls out a new marketing campaign aimed at driving new customers to their location(s), it can directly correlate the campaign’s success by reviewing this data.  The same could true for campaign’s targeting previous customers.            

Not highlighted in this specific demo video is how the location engine provides access to the zonal analytics at a given location.  Operating from the same dashboard, businesses can determine:

  1. Amount of time visitors typically spending in each zone
  2. Which zones are attracting the most visitors
  3. Least popular zones by amount of time spent by visitors
  4. Number of associates (to visitors) for each zone

  

As an example, if a business hopes to understand which zones are the most popular and which are not (as well as the time when spikes and lulls in engagement hits), and in turn optimize the layouts of their stores to mirror these engagement patterns, it is able to do so.  A business may also tap this data to ensure the most popular zones in a store are equipped with the most associates, so that the guest to associate ratio is balanced. 

All of these dashboards are completely customizable to meet the needs of the business and its locations, ensuring the ExtremeLocation engine delivers the greatest value for its customers.  For more insights around ExtremeLocation, be sure to visit the central solution page!

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