Replicating Rich Data in Offline Stores Way to Future Success: Capillary

With computer vision, natural language processing and deep learning, stores can now start doing amazing stuff

Sunil Suresh, Global Vice President, Strategy, Capillary.

Capillary Technologies has developed products that help brands get a 360° single view of customer and inventory across all channels, so they could stitch together previously siloed customer journeys with data, and build unified, cross-channel strategies that deliver a consistent, omnichannel experience.

In this era of artificial intelligence (AI), Capillary continues to innovate and find ways to help brands stay consumer ready and deliver newer, better experiences into the future, as per Sunil Suresh, Global Vice President, Strategy. Excerpts from an interview:

BW CIO: How are AI and machine learning technologies helping the offline retail industry?

Sunil Suresh: Offline retail around the world is feeling the heat from e-commerce. This is very different from even a decade ago, where it was all about loyal customers, and easy sales.

Today, consumers live in what we call an ‘Easyverse’, an easy universe, where they expect everything to be personalized, easy and seamless. E-commerce marketplaces offer consumers this easy and personalized experience and importantly, the rich data on consumer browsing and buying behaviour available to them makes it possible for them to offer truly personalized experiences, as well as generate insights that help them optimize inventory and other aspects of their business.

While consumers are able to have great personalized experiences 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 has not purchased the last time? What does she like?

While creating a connected omnichannel experience is the first step for offline stores to compete with e-com marketplaces, that, in itself, is not enough. Being able to replicate the same rich data available online, in offline stores is a critical factor in future success. This will bring offline retailers onto a level-playing field and allow them to create personalized experiences as well as generate the rich insights needed to optimize business operations.

This is now possible with AI and machine learning technologies, including computer vision and natural language processing. We can now track conversions, generate personalized ‘fashion profiles’, track trends and all in all create an ‘offline clickstream’; a rich data trail similar to what is possible online.

BW CIO: Elaborate on Capillary’s AI offerings for offline stores.

Sunil Suresh: Capillary has strong capabilities in AI, with a strong data sciences team, including over 10 PHDs at capillary labs. We have also helped incubate and support an AI lab at IIT Kharagpur. Our AI platform, Capillary Zero, is increasingly powering all of our insights, customer engagement, commerce and loyalty platforms. A particular area of investment is in our suite called ‘store vision’, a range of AI-powered solutions for offline retail.

The first of our products is VisitorMetrix, a computer vision powered footfall and conversion tracker. Customers who have deployed this, have been able to increase sales by over 5 percent and in-store conversion by over 10 percent. Our other products include the Store Sense, which empowers offline stores by providing individualized insights on customer behavior.

A personalized analysis of each customer’s needs, preferences and motivations builds an ‘offline clickstream’ of data. Retailers can then use this to provide relevant recommendations and engagement strategies for their customers, despite working with the limitations of the offline world.

Facilitated by the capabilities of AI, our Recommendation Engine and Campaign Manager have been equipped with Machine learning algorithms to enable our clients to enhance their customer’s experience. This has enabled our clients to boost discovery and personalisation on e-commerce websites, as well as in communication: whether over sms, email or push notifications with Right Product at Right Time with Right Offer. Our implementations have shown direct sales increases of up to 30 percent.

Our BI tool – Automated Insights, uses artificial intelligence to identify granular trends hidden deep within our client's data. The tool identifies threats, assigns an impact estimate to them and highlights the most important ones to business managers with recommended solutions.

BW CIO: Please give us details on the AI suite VisitorMetrix Plus and Store Sense.

Sunil Suresh: The Capillary VisitorMetrix Plus and Store Sense help brands improve in-store sales and optimize marketing spends.

Built on Capillary’s own AI platform called Capillary Zero, VisitorMetrix Plus helps brands unlock growth with conversion improvements. VisitorMetrix™ Plus uses computer vision and machine-learning algorithms to provide granular segments based on demographics, Fashion profiling and propensity to buy.

Capillary VisitorMetrix Plus works as a real-time dashboard and a trend analysis engine that helps brands better identify and understand the ‘power hours’ of their stores. Power hours are the time periods when any retail store experiences the maximum footfall, and hence, sales.

Capillary VisitorMetrix also helps study the impact of below the line marketing initiatives by the brand, and whether the right target segment who received a promotional email or a discount coupon visited the store in the days that followed or not.

Extending the power of AI to brands with Store Sense helps clients capture the in-store behaviour of their consumers. It uses machine learning algorithms to understand the demographic-wise intent, trials and purchase patterns to empower brands with insights on merchandising, layout planning and store operations. Store Sense also helps brands improve store staff training by presenting continuous feedback on products and sales pitches to create an increasingly personalized customer experience.

BW CIO: Who are some of the key customers of Capillary?

Sunil Suresh: Capillary Technologies connects 200 million consumers, and enables 25,000+ stores and 250+ enterprise e-commerce implementations across 30 countries. Leading brands, such as Unilever, Walmart, Landmark Group, Madura Fashion, Arvind Brands, Blackberries, Redtag, Calvin Klein, Gap, Courts, Starbucks, Pizza Hut, and Puma, work with Capillary to drive retail excellence.

Bengaluru-based VF Brands, that own Lee and Wrangler jeans in India, was one of our earliest customers to deploy VisitorMetrix on around 400 stores across India to improve store productivity and profitability.

BW CIO: How is AI powering the future of retail?

Sunil Suresh: Offline retail has many inherent advantages – that of immediacy, better experience and human interaction. While this is so, the disadvantages of lack of data and the ability to provide an easy, personalized and seamless experience create a very real threat from the ecommerce marketplaces. This is where AI will help bridge the gap and bring power back to offline retail.

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.

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 percent incremental sales being generated by doing just this - getting accurate conversion data and working on improving this.

Next up, is to understand how customers behave in store. Again AI, computer vision and natural language processing 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. 

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Rich Data offline stores Capillary ai


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