Video Analytics in retail stores is the answer to the analytics advantage of e-commerce. SaaS-based video analytics captures not only quantitative data but also qualitative information about psychography as well as demographics.

1. Is Video Analytics an answer to Google Analytics? By Kishore Jethanandani The competitive battle between brick-and-mortar stores and on-line stores seemed like a no contest. E-commerce stores have the clinching advantage in analytics. Video analytics may well help stores disprove that presumption. E-commerce sites learn so much from the footprints of visitors on their sites with the analytics tools. Video analytics will potentially capture not only the quantitative data on shoppers but also the demographics such as ethnicity and even suggestive psychographic data, from the unguarded expressions of shoppers engaging with products and services, advertising and content, for better targeting. Accepted notions of display, store planning and merchandising begin to change with the insights received from the analysis of video and related data. Tim Callan, the CMO of RetailNext, a leading video analytics company based in San Francisco, related the story of a retail store that wanted to position its signature products so that they would be conspicuous to shoppers. The conventional wisdom was that those products should be placed at the entry of the store. “Video analytics revealed that there are dead zones near the door entry where shoppers make way for people so they have enough space to enter the shop and miss the products placed there,” Mr. Tim Callan recounted. Another case led to a substantial redesign of the store as a result of findings from video analytics. Like most lifestyle retail stores, it had a section for shoes. Typically, shoes are displayed on walls for customers to scan before they choose. Most stores place benches next to the wall where customers try out the shoes. “Video analytics revealed that the benches discouraged the shoppers from spending enough time looking at shoes on the wall,” Mr. Callan revealed. “The store decided to reserve separate spaces for the wall and the benches and increased the dwell time on shoes by five times,” Mr. Callan added. Insights on customer behavior revealed by the qualitative data proved to be especially useful for Gordmans (Mr. Callan disputes the name of the store), a mid-western

2. department store, as a guide to customer service decisions according to a report by the Economist. A camera embedded in Mannequins helped to spot a pattern—the tendency of Asian customers to visit at the same hours of the day. The store responded by placing Asian employees to serve them. Merchandising decisions can be fine-tuned to increase conversions and improve outcomes from cross-selling. Retailers like Urban Outfitters draw on insights uncovered from correlations between the data on time spent eyeing products and sales realized at the counters. Customers, for example, could have entered the store to buy custom jewelry and may well be in the mood to buy perfumes. If they spot perfumes in an adjacent display, they are more likely to buy them. Today’s cameras take a 360 degree view of stores and will capture behavior of this nature to provide pointers to cross-selling opportunities. The critical advantage of video analytics tools for stores is the ability to use analytical reports in real-time and feed advertisements, content and offers to customers while they are still in the store. Immersive Software’s Cara software, for example, processes data, in real-time, from face detection cameras to determine the gender and age of shoppers and change the advertisements that are more likely to appeal to the observed profile of visitors to the stores. “The data helps to personalize the experience for customers such as by placing customers in the advertisements they see,” said Jason Sosa, the CEO of Immersive Software. Early adopter American Apparel recovered from a near death experience after it restructured its stores and deployed video analytics among other technologies. A store that faced the prospect of bankruptcy in mid-2012 is now profitable. Stores have a chance to make shopping fun as they understand the shoppers’ behavior a lot better with video analytics. Shoppers could well end up loving it more than the yawn of executing commands on-line.

3. Taming the Big Video Data Beast with SaaS
By Kishore Jethanandani
Aging security cameras with poor resolution represent an opportunity for video- surveillance-as-a-service (VSaaS) providers who stand to benefit from the aggregation and analysis of video data from multiple sources.
The providers are installing significantly improved cameras and analytics for security as well as offering customer service and product and services management.
VSaaS makes it viable to gather, store and process large volumes of video that are simply too large to be attempted in-house. The aggregation of data from multiple sources, at an accelerated rate, improves the quality of analytics of the data.
Prospects for VSaaS companies have improved as they have been able to resolve the long-standing problems of storage and analysis of video data. VSaaS is expected to soar to $2.4 billion by 2017 from $474 million in 2012, according to MarketsAndMarkets data cited by AdWeek.
Retail chain management is undergoing a generational shift as data gathered from HD cameras by VSaaS companies overhauls merchandising, staff management, and store design. The data is being aggregated across chains, geographies, and time periods. The insights gained from video data often change perspectives for stores management.
Seasonal fluctuations in demand play a critical role in recruitment of temporary employees in stores. "Paradoxically, the rate of conversions is relatively low in peak times," Tim Callan, the chief marketing officer of RetailNext, told me. "Store visitors' traffic shows a pattern that looks like a hill rather than a cliff... Stores tend to staff for the cliff, which misses the customer services needs for slopes in the traffic before the peak of a season."
Aggregation of data helps to make comparisons across time periods, geographies, and seasons. "Data on program management is matched against expected patterns in

4. numbers of shoppers and business to find the anomalies that yield unique insights for stores," Callan said.
It also helps to gather macroeconomic data that RetailNext shares with its clients. "While median income data helps, program managers want to know the impact of media selection, promotions, and communications strategies across regions for similar levels of income," he noted.
There are special challenges involved in aggregation of video analytics data and video surveillance providers are gaining ground with their solutions. To start, the bandwidth needs of transporting video data are so prohibitive that none of the customers are even trying to cope with it.
"The servers storing the video remain at the store while the abstracted data is aggregated across stores," said Callan. Other solutions, such as those provided by Imagine Software, have analytics software embedded in the cameras and the data on visitors and their demographics is sent to a dashboard so that the video does not have to travel to servers.
Surveillance providers use technology to reduce the volume of video data that is stored. The providers filter out redundant data, such as night-time video when activity is absent, with the use of sensors. Cameras don't capture video when sensors don't detect any motion.