Retail AI Assistant

Retail conversational AI turned out to perfectly fit in the in-store sales as a customer support option. The retail conversational AI was built to improve the shopping experience of users by reducing wait time (conversational tools can provide instant answers to many of the common questions customers may have when examining products) and to retain, re-engage customers by sending messages.

The Customer

The client is a global retail chain that sells a wide range of consumer products. They were seeking newer options to deliver unmatched customer experience and thereby, boost sales.

The Business Challenges

Salespeople and customer care executives are the key factors that ensure the smooth operation of sales and customer care activities in a retail store. Businesses relied on sales concierges to engage with customers. They address the customer’s concerns and provide relevant information to customers to help them find what they are looking for. However, when the number of visitors to the store increases, retailers have to deploy more sales concierges to assist them. This solution is not scalable nor efficient since more manpower is needed. The business needs to spend a large amount of time to train the workforce to make it ready to interact with the customers. And among customers, there is an increased demand and adoption of self-help services. This is because it is easier, less intrusive, and quicker than any other method.

The Business Requirement

The client wanted a complete solution that solves many of the challenges in a retail business such as improving customer satisfaction, providing a good shopping experience, and increasing customer retention.

The Solution

The solution we built for our client is a ‘Retail conversational AI’ where customers can interact with the conversational AI to seek information. The retail conversational AI can engage in a human-like conversation with the customer. The system can deliver a more personalized experience and care through natural human-like conversations to the customer.

 

It also provides a direct and straightforward path for customers to sort out the problems, address concerns and seek any information by engaging with the conversational AI. The Retail conversational AI can automate some of the tasks and can replace human labor or offload a significant amount of work from them. Moreover, the in-store AI assistant can provide timely, accurate, and tailored answers to the users. The benefits offered were,

Technical Overview

The system is built with robust Natural Language Processing (NLP) and Natural Language Understanding (NLU) algorithms to facilitate conversation more naturally and coherently, adding a human element to conversations. The tool can easily identify user’s intent in real-time and provides faster resolution without any typical delay. It also has speech-to-text and text-to-speech engines so that the system can provide instant answers to common questions customers may have.

The Result

With the implementation of retail conversational AI, the client was able to support and scale business teams in their relations with customers. The system can provide meaningful and quick information to the customer which in turn enhances the user experience for the customers.  It also leads to increased sales as customers were able to find the right product in a short time.

With advanced retail conversational AI, businesses can increase sales and customer loyalty by improving the customer’s experience. Such tools can help reduce customer service costs by 30%. Retail AI assistants can provide timely, accurate, and tailored answers to the users. Instead of deploying staff to man help desks to interact with customers, a retail conversational AI can rise to the challenge and provide 24/7 interaction with your customers. Gartner.Inc predicts that by 2020, customers will manage 85% of the relationship with a business without interacting with humans.

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Highlights

Natural Language Understanding

Natural Language Processing

Speech to text

Text to speech

Entity recognition

Face recognition

Technologies Used