AI trends that will reshape Indian e-commerce

Published: September 5, 2017 12:43 AM

Given the data-rich and data-driven playground that AI offers, the disruption in the retail space is here to stay.

E-commerce, app, whatsapp, democratisationE-commerce will be largely affected by AI considering it is a data-rich and data-driven playground.

India’s online spend will touch `8,75,600 crore by 2018. According to a report jointly published by PayPal and Ipsos, Indian shoppers are likely to spend almost 85% more in global online marketplaces in 2017 across categories. This upsurge in online shopping can be attributed to many reasons such as: democratisation of cross-border trade, free shipping, a huge variety of brands across product categories, ease of payment through digital wallets/cashless payments, convenience of doorstep deliveries and lowest prices/best deals, etc to name a few. With the growing demand of online shopping, retailers must keep up with the competition and for that, they must embrace artificial intelligence (AI) and the capabilities it offers. E-commerce will be largely affected by AI considering it is a data-rich and data-driven playground. The disruption in the retail space is for real and here to stay, thanks to AI.

Some trends emerging out of the AI wave which can entirely reshape the Indian e-commerce industry are:

* Hyper-personalisation: Going a step ahead with personalisation, hyper-personalisation can help unlock the complete value for customers. Analysing general patterns for consumers is no longer relevant. If personalisation is about knowing cloth preferences and stitch desired by the customer, hyper-personalisation is about tailoring an additional hidden pocket where the individual customer would need it. What really makes the magic happen is when AI and machine learning (ML) drive the process from behind the scenes, creating personalisation that guides the consumer, enables them to pick up where they left off, and reads their cues and preferences effortlessly to expose them to offerings most relevant to their needs.

* Change the buying landscape: AI and ML capabilities will encourage a shift towards voice, expand the scope of visual search and complement it with suggestive/ voice-activated shopping. For example, Amazon’s voice-controlled home automation speaker Echo allows users to make online purchases using voice search technology. Shoppers who have a device integrated with its Alexa technology can give orders using voice commands. The scope of product discovery through visual searches will expand. Imagine this: a customer will be able to take pictures of items they like, search visually online and get personal recommendations based on an AI-generated model, something like CamFind or maybe Pinterest. But that’s just the start.

* Growth of real-time, signal-driven analytics: Real-time data processing and analytics can help gauge behaviour of shoppers to provide actionable insights. This can prove to be instrumental in driving sales. The shopper information can be used to create a tailormade purchase experience, targeted pricing for products, targeted advertising or new customer micro-segments. Better business conversions may happen after carefully analysing the customer behaviour, demographics and actions.

* Campaign optimisation: AI can help leverage ML and neural network capabilities to suggest, plan and modify various promotional/sale campaigns and the time for its launch, based on the expected campaign response rates based on predictions of customers’ potential actions and affinities using ML algorithms. This will only lead to website and content optimisation eventually.

* Better search results: E-tailers can use AI (self-learning) or something like online learning to offer better search results for customers and maximise customer satisfaction. ML can improve search results each time a user buys/searches or even clicks on a site. It can also generate a search ranking, which allows the site to sort search results by relevance, specific user interest or preference. The systems learn after each time and produce better results for the next purchase cycle.

* Virtual shopping/personal assistants: Chatbots and VPAs like Siri, Echo, Google Assistants or Cortana are like dependable assistants or friends enmeshed in people’s everyday decision-making, and eventually it may happen that these machines start taking decisions or acting on behalf of their human friends and curate items that a shopper may like. These bots use big data, collected in real-time, to learn users’ shopping habits and personal taste.

* Supply chain management: AI is also poised to make the massively complicated (and data-rich) world of logistics much easier for retailers — from making sure the right products are in the right warehouses to predicting which items will fly off-the-shelves. AI can help predict product demand based on customer preferences to avoid stock-out situations.

Flipkart, being amazed by Amazon’s AI leap, is now starting its own AI-based sales prediction engine. The e-tailer has been successfully using AI in the fields of computer vision and language processing to build tools for improving product search results on its platform. In other words, at every step of the buying journey, from search to delivery, AI can deliver tangible and important advantages for both retailers and their customers. This can surely make the lives of both customers and e-commerce players easier.

Titir Pal is head of products and solutions, Absolutdata

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