AI and machine learning have collaborated to make consumer shopping extremely interesting and easily provide more personalised recommendations, enabling retailers to better anticipate customer needs, assist in automating the entire shopping process.
Five years ago, online shopping was tedious and required hours of browsing. Even websites were less responsive, offered limited user experience and were a total chaos in terms of product search and returns. As per the reports of Littledata in 2022, nearly 49 out of 50 customers who visited fashion e-commerce websites did not make a purchase on their first visit. The current worldwide conversion rate in apparel e-commerce market is only 1.8 per cent and thus retailers are taking the help of AI to improve online customer experience, increase checkout conversion and engage visitors. The AI integration will help to increase the rate of conversion which is expected to rise to 3-4 per cent and beyond as per reports.
There is currently a conversion rate of just 1.8 per cent across the apparel e-commerce market. And for those in the bottom 20 per cent, the rate is three times lower- Littledata
The advancement of technology is improving the situation drastically. AI and machine learning have collaborated to make consumer shopping extremely interesting and easily provide more personalised recommendations, enabling retailers to better anticipate customer needs, assist in automating the entire shopping process. Additionally, they create interactive communication channels such as chatbots, proving that AI implementation in e-commerce is a game changer.
Moreover, retailers are adopting smart solutions to keep up with the rapid-fire demands of customers, who expect to find the desired product they want at the right price, in the right size, colour, style and material, all in the shortest time.
According to a market study report, the market for AI in retail would grow at a CAGR of 35%. The market size is anticipated to increase from US $ 1.80 billion in 2020 to US $ 14.71 billion in 2027.
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Searching for the desired product has become more specific
Conventional methods of searching for a product on the e-commerce website rarely gave accurate results and the customers lost time in browsing the desired products. For example, a keyword of ‘Black T-shirt’ used to give results having a mix assortment of T-shirts, denim, dresses, hence there was a need to have more specific results. To overcome this problem, automated fashion tagging solution was adopted.
Automated fashion product tagging is the process of analysing and labelling a product image in an e-commerce catalogue by automatically identifying visual attributes. For example, if a product catalogue contains an image of a black sheer dress, image recognition and Machine Learning (ML) technology can ‘tag’ it as ‘knee-length, full sleeve, black sheer dress’
AI has made online search more ‘smart’ with ‘visual search’ gaining traction among retailers. Now, AI-powered search engines are in the driver’s seat, with an edge in understanding the intent behind searches and returning more relevant results. Whether it’s a specific product or information about a product, this is helping shoppers locate what they need faster and more efficiently online.
This is extremely important, as 60 per cent of online sales comes from the search field. An intelligent search uses artificial intelligence and has high speed, colour search 100 per cent automated; behavioural search customised for each customer; voice searched image.
Additionally, Neuro-Linguistic Programming (NLP) is also being used to improve search engines because it studies the relationships between how people think (Neuro), language pattern they use (Linguistic), and actions (Programming), as well as the positive and negative effects of these interactions.
Reliance-owned start-up Fynd, has added a Chrome extension Fynd Now, where customers can upload an image from their phone’s gallery to link a visual from the web and discover it or similar products on the platform. The feature uses AI to identify the product in the image/video and recommends a similar product from Fynd.
As a result, some retailers are employing the NLP approach to search, which is based on AI allowing computer programmes to understand human speech, allowing customers to conduct searches in a similar manner like one does in the physical store. Myntra, Ajio, Nordstrom, Urban Outfitters and many others are just some of the retailers that have found success with visual search.
Solutions like Clarifai, Google cloud vision API, InfiViz etc., are examples of deep learning image recognition system which actually help to identity the products being uploaded for search.
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24/7 available e-commerce chatbots assist in customer service
Traditional methods of e-commerce have never made use of chatbots as value was unknown but now the chatbots are considered the newer form of AI that allow customers to communicate with businesses via text or voice messages. Chatbots’ enhance linguistic capabilities and contextual responses for the consumer and therefore are the result of a convergence of AI, NLP and machine learning algorithms.
Customers get information about their orders, products and the company’s return policy and recommendations in a matter of seconds by connecting with a chatbot rather than waiting for a human customer service representative to respond. Companies in the retail sector are spending money on chatbot technology for their online store.
The potential for chatbots to bridge the gap between consumers and businesses is promising, and it bodes well for the future of the industry. When it comes to customer service, chatbots are used as live agents and are even more convenient for the users, thanks to features like round-the-clock availability.
For example, Nykaa worked with Verloop.io to improve customer engagement by solving problems over chat. Nykaa used bot-qualified questions from Verloop.io to handle repetitive requests like cancellations, returns, shipping inquiries, replacements, refunds and payment issues. Verloop.io’s broad integration options helped Nykaa switch software systems smoothly (including Ticketing Software, CRM, among others). Verloop’s automation and NLU modules used both classical machine learning and deep learning.
Solutions such as verloop.io, ChatGen.ai, Tidio, Snatchbot etc., integrate with the e-commerce platform to provide the most effective chatbot assistants to help with conversion rates.
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Predictive Analytics and personalisation create the best basket of assortments
In the past, stores only stocked products chosen by senior management on the basis of sales projections or guesswork. However, now, predictions of what will sell and where it will sell best are being made much more quickly, thanks to AI implementation.
AI’s ability to personalise e-commerce services is one way it aids today’s online retailers in providing an optimal customer experience and ensuring customer loyalty.