Retailers once had it easy. Little competition, loyal customers and easy sales. Then came an explosion in retail; great for consumers but not so good for retailers! The days of easy sale were in the past and retailers now had to work hard to build and retain a loyal base.

The first wave in retail was all about the data. Collecting data, analysing data, and using insights and campaign tools to personalize engagement and create a group of highly loyal customers. Next up was the shift to online. E-commerce and a connected experience both online and offline became a must have to be able to provide consumers with the experience they expected. This continues to play out and is often dubbed ‘going Omni-channel’.

While getting Omnichannel right is critical for survival, we are now seeing consumers evolving to the next wave.

The reality is that over 94% of sales still happen at brick and mortar stores.

While consumers are able to have a great personalized experience online, physical stores are woefully behind. Retailers have next to no data on what is happening in their stores. Who is the customer walking into the store? What has the customer browsed through but not purchased the last time? What does she like?

Having this data is a foundation to both provide the personalized experience consumers expect in stores as well as to maximizing operational efficiencies in a store.

The key to enabling this kind of data augmentation is through smart use of computer vision and natural language processing, artificial intelligence, we can create the tools to start getting the rich data and personalization available online, in offline stores.

I see this playing out in three stages

The first stage is to have accurate and real time data on visitors to your stores; and then integrating this with transaction data to be able to get insights on store staff effectiveness, power hours at your store, conversion rate, campaign effectiveness etc. I can’t emphasize enough the importance of accuracy! If the data is not accurate and reliable, the team will not trust the data and no action will be taken. Getting this level of accuracy and doing this cost-effectively is possible with smart use of AI and computer vision. A leading apparel brand saw over 5% incremental sales being generated by doing just this – getting accurate conversion data and working on improving this. Click below banner to read the entire story.


Next up 
is to understand how customers behave in store. Again AI, Computer Vision and Natural language processing based people and footfall counters can help you generate heat maps in store and answer questions like

  • Where do the customers tend to spend the most time in a store?
  • Which are the most popular sections and products in a store?
  • Which sections have poor conversions and how can those be improved by altering the store layout?
  • What paths do customers take in the store and how does the traffic flow through the various sections of the store?
  • Analyzing customer – store staff conversations using NLP to understand which products did customers ask for but unavailable at store or to understand how many customers asked for a discount or didn’t find their fit.

Once these are in place, it opens up doors to truly exciting and revolutionary applications of AI. With computer vision, Natural Language Processing and deep learning, we can now start doing amazing stuff. Imagine these:

  • Use AI to identify attributes like age, fit, clothing style and expression to get rich data on customer behavior and experiences in store. Do they like the item they browsed in store? How did they react to the store staff engagement?
  • Use Natural Language Processing to identify conversation trends, of course in a non-personally identifiable way. Are customers asking for black shirts? How many folks wanted a looser fit?
  • With their permission, and tagging customer IDs, you could identify your customer as soon as they walk into the store through facial recognition and have the store associate get instant information on the customer profile and their preferences, with clear suggestions on how to personalize the interaction and offerings. This would be a truly personalized and easy experience for the customer.

This is the power that retail technology is giving stores today; and it is amazing!

Capillary VisitorMetrix™, built on our AI platform Capillary Zero™, helps you unlock growth with accurate store performance insights such as conversion ratio, store power hours etc. so you can improve sales and optimize marketing. Visit link below to sign up for an exclusive pilot with Capillary VisitorMetrix™ at your stores.

Aneesh Reddy
Aneesh Reddy is the CEO and Co-Founder of Capillary Technologies. An alumnus of IIT-Kharagpur, Aneesh has been recognized a "Forty Under 40" leader by Fortune Magazine and the Economic Times. He is an active contributor to the start-up ecosystem. He's also an avid trekker, long-distance runner and traveler.
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