Conversational AI Tool

Developed For Etisalat Telecoms

THE CLIENT

Etisalat is one of the leading telecom operators in the UAE. The company has many store outlets all over the UAE with interactive features to give customers a first-hand experience of various products offered by the company.

THE BUSINESS CHALLENGES

Active salespeople are one of the key factors that ensure the smooth operation of in-store sales activities. They provide relevant information to customers who walk into the store to help them find what they are looking for. But for a majority of customers, the retail shopping experience is a frustrating one. Customers have to wait for sales concierges to free or often had to go through various sections before finding the right product. This is time-consuming. Our client faced the same problem in their smart store outlets and wanted a solution for it. 

 

Deploying more sales concierges was not a viable and scalable solution. The company would need to spend a large amount of time to train the workforce to make them ready to interact with the customers. Also, maintaining such a team increases the operating cost. Moreover, there is an increased demand and adoption of self-help services among customers. 

THE BUSINESS REQUIREMENT

Our client put forward the business requirements to build a tool that can engage with the in-store customers, understand the queries of the walk-in customers and deliver the right responses to them. The interface should be intuitive as well as attract the customers to use it. 

OUR SOLUTION

Today’s customer prefers to engage with a digital sales concierge rather than dealing with a real person. The digital medium provides easier and quicker actions as compared to any other method. For our client, we built kiosk-based in-store AI conversational tools that can address a customer’s queries at the point of occurrence. Walk-in customers can interact with the device to seek information about the products they want. The system delivers meaningful, quick information to the customer which in turn enhances the user experience for the customers.

 

The system also has face recognition capability to identify recurrent customers so that it can recommend new offers and suggestions. They are the perfect customer support option for in-store sales. The users can either talk to this digital sales concierge or type in a chat interface to ask questions, submit feedback, inquire about offers, etc. The system built using advanced AI and NLP technologies can accurately understand the requirements of the user and responds to them accordingly.

 

TECHNOLOGY OVERVIEW

In-store AI conversational tool is armed with a powerful AI algorithm with Natural Language Processing (NLP) capability. The robust NLP is able to process questions asked by the user and reply with a personalized answer that is derived from the information obtained from PDF documents, brochures, or notices about the business. The business owner can upload all documents related to the business to the system and the system, in turn, analyzes the contents of the documents and fetch answers from the document for the queries asked by the user. The system is built using the following technology components. 

BENEFITS OFFERED

Unlike other solutions available in the market, the admin do not need to configure the questions and answers from the dashboard. The admin can upload the documents related to their business, such as offer details, business brochures, etc to the system. The system can learn from the documents by itself and respond to the queries asked by the users.  

THE RESULTS

Using the in-store digital sales concierges, our client was able to provide personalized experiences and interactions to every customer. This has boosted the customer experience as most of the customers engaged with the Kiosk based tool rather than talking to a human counterpart. Our client was able to reduce the number of staff assigned as sales concierges and was able to use them for more challenging tasks. 

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Highlights

Natural Language Understanding

Natural Language Processing

Speech to text

Text to speech

Entity recognition

Face recognition

Technologies Used