Hanwha Techwin has introduced Wisenet Retail Insight 2.0, a business intelligence application which enables retailers to gain a greater understanding of customer behaviour and buying patterns. Wisenet Retail Insight 2.0 utilises people counting, heat mapping and queue management applications running on board selected Wisenet Q and Wisenet X fixed lens and fisheye cameras to display statistical analytics on a centralised dashboard, along with other practical information such as weather reports.
Cloud-based business intelligence
Accessed from anywhere on the network, Wisenet Retail Insight 2.0 is a web-based application which consolidates the data captured by the three analytics applications running on up to 500 Wisenet Q or X series cameras and presents it on a customisable dashboard.
With profitability under threat, retailers are looking at ways to improve productivity
Retailers are able to take advantage of the captured data to measure the impact of advertising, online promotions and other marketing activities on the number of people who enter their stores, as well as making the best use of human resources in order to manage the peaks and troughs of customer flow at checkouts.
Customisable dashboards
“With revenues and profitability under threat, retailers are understandably looking at ways to improve productivity,” said Uri Guterman, Head of Product & Marketing for Hanwha Techwin Europe. “In terms of the business intelligence which could be made available to operations, marketing, merchandising and store management, the metadata traditionally extracted from Electronic Point of Sale (EPOS) systems is unlikely to be sufficient for retailers to identify opportunities to increase productivity or improve the customer experience.”
Wisenet Retail Insight 2.0 generates reports in a variety of formats including PDF, Excel, CSV or HTML, whilst with the help of five customisable tabbed screens, users are able to view real-time updates on a wide range of activity, including the number of store visitors for any particular time of day or accumulatively over a specified period of time, as well as data on queue congestion times and heat mapping information showing the busiest areas of a store.