The Retail Ecosystem is going through the toughest and biggest transformation in Customer Experience. With Devices, Channels and Market places, the competition is unprecedented, and the heat is rising.

We have seen Social and mobile disruptions. Day by day the digital frameworks are shapeshifting and creating new means to increase the footfall and conversions.

The new disruption in this industry is the application of AI in Retail to Digitally sense the consumer requirements, intellectually shape his profile and in real time serve him with a personally immersive experience.

The Retail Lake -Building context over quantity

Data has become the key ingredient in this, and it can be of any shape size or form. Earlier the retailers got opportunity to connect with their customers in the physical stores alone.  This did not bring much room for collecting the customer data, understanding their requirements and providing customized promotional offers for them. However, with the advent of the e-commerce revolution and the associated digital transformation in retail, there arise a paradigm shift in the retail space. The customers’ purchasing patterns have started changing drastically and they have started using omnichannel including mobile, web, physical stores and started relying on social media, forums, blogs etc. to get real time reviews, ratings regarding the products they wish to shop.  All these end points started collecting large amounts of customer data and insights which hold the power to redefine the retail business landscape. Retailers have acknowledged this disruption and are frantically aggregating the data from all sources including third party, market place, the point of sales terminals, beacons, social media, CRM, clickstream, order management to get valuable insights and optimize their business. However, on the flipside, these data from multisource have got the whole enterprise into a stumbling framework with too much of data and a single moment of truth.

The Data Lakes are overflowing with data, and still, the conversion pipelines are dry.  A study in the data lakes throw two very important insights:

• More than 75% of data is not contextual to customer experience
• False Positives increases with the lake and insights lose their relevancy

The way forward is not in aggregating the data and flooding the lakes, but intelligently federating the information and connecting them with a contextual vocabulary that drives the Customer experience.

This vocabulary of experience changes with domain and is enriched in time.

Customer Experience – “Distortion with a Lens” or “Diffraction with a Prism”

Let’s now look at the Usage of this intelligence that we have selected and connected. This connected intelligence forms the foundation to drive Recommendation, Personalization and Relevancy.

Let’s start with Recommendation and Personalization…Both these words have been used and abused against each other and together too.

They serve different context. Personalization is the intent of the customer while recommendation appreciates the context. Recommendations are mostly driven by Reasons and Rationales, while Personalization is the direct result of Relationships. Both require the customer to be digitally sensed and shaped. Recommendation uses the data that intersects the ecosystem of customer and enterprise, personalization uses the small ecosystem of the customer.

Customer 360 in most retailers, gives a lens view of the customer by enlarging his profile and distorting his image.

It’s not about enlarging but about bringing the spectrum of behaviors and expanding hues through prismatic diffraction.

Relevancy drives more than 43% of conversions. Retailers in a hurry to convert have been totally concentrating on the customers while ignoring their products and sites. The path to conversion in a site happens through search and browse. If the metadata of the products is not enriched or updated, zero search results happen which leads to a drop in the conversion.

Metadata happens to be mainly two types

• Domain related – like color, precision, sound quality, etc.
• Folksonomy related – conversation linked like “best family car” “Value for money” etc.

Enriching the product content data from conversations itself can reduce zero searches by 75% and boost relevancy by 28-35%.

Conversations from channels and site can evolve the vocabulary of a site and enhance the experience.

 

In this turbulent economy, increasing competition and changing customer demands, retailers require not just data from multisource, but the right data and the right strategy that maps the right metrices to intelligent decision processes and helps them in transforming business.  FiRE (Federated insights in Retail Experience) is a new platform from Happiest Minds that connects the customer and the enterprise at every touch point of interaction, sensing the signals, bridging the conversations and creating a seamless experience. The platform powered by Artificial Intelligence and Machine Learning derives business intelligence and also adds semantics and Natural Language Processing to give an emotional touch to the retail customer experience.

More exciting times ahead in the retail business landscape and let’s watch the space for interesting updates.

 

 

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