RETAIL

Case Study

Dramatically scale inquiries, serve customers after hours, and expand purchase opportunities

Situation

A new generation of customers prefers to shop online where markets and competition are not just local or national, but international as well. Customers can now choose to buy from big companies or small—even from an individual person selling online. Organizations must adapt to this trend or risk losing sales and market share.

Challenge

Our client’s goal was to significantly increase over-the-phone and online sales. They faced a high cost of staff recruitment and intensive training to provide customer service during special sales. Approximately every two months, the company ran limited-time promotions— such as a Mother’s Day sale—with a wide variety of discounts, featured items, and gift items. Inevitably these sales generated large demand peaks, creating the need to employ and train new personnel solely for an event lasting an average of four days, and sometimes as little as a weekend or even a few hours. This makes the sale process extremely difficult and expensive.

Solution

Our client had no call record data, hadn’t analyzed customer needs, and didn’t have metrics indicating how customer needs were met. Rising to the challenge, our analysts conducted fieldwork to establish this baseline of information. After gaining an understanding of customer needs, we designed an AI solution to be the first point of interaction between the customer service center and its users. The chatbot needed to be capable of self-learning and gradual enhancement based on a variety of technology tools such as machine learning, big data, advanced data analytics, and sentiment analysis. The chatbot would respond to general questions about products, returns, exchanges, product warranties, and billing, and track purchases made online, over the phone, or at stores. In the event that it wasn’t capable of answering a question, it would route it to a human agent to handle the specific issue, incorporating an analysis of customer feelings, which aided the agent in providing a fast and efficient solution.

Results

In the first three months of activity the chatbot handled more than 690,000 inquiries, reducing the average service resolution time from 7 minutes to 1.7 minutes— a 75% improvement. We also identified that 30% of the inquiries—more than 207,000—were made outside customer service center office hours. Of these, around 72,000 were made with the intention of acquiring a store credit card. Without the chatbot, these customers would not have had the expanded opportunity to purchase products.

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