Identifying Risk Factors for Readmissions with More Complete Data

Our client is a multinational conglomerate operating primarily in Japan and Korea with more than 90 business and 60,000 employees.

THE CUSTOMER EXPERIENCE NEEDED A MAKEOVER

Our client operates across a range of functions including retail, financial services, hotels, heavy chemicals, electronics, construction, food and beverage, and more.

The company’s retail arms sought to optimize the customer’s shopping experience across digital and in-person channels by using cutting-edge cognitive tools to make better use of its customer data.

Building a Fashionable Solution

Using IBM Watson Assistant, we created a chatbot that delivers a unique cognitive shopping experience by serving as both a shopping advisor that recommends products and as a store advisor.

Powered by a conversational recommendation engine, the advisor analyzes structured and unstructured data and incorporates speech to-text and text-to-speech capabilities, along with visual recognition. This allows the chatbot to perform similarity search where a user can ask, “Do you have anything like this?” and upload a photo of the item being sought.

Users can also ask the chatbot for product recommendations such as, “Show me some trendy jeans” or “Recommend a summer dress.” If the user is logged in, the chatbot’s recommendations engine references a “customer DNA” to tailor recommendations based on the user’s shopping history and current trends. Trend recommendations are based on the AI platform crawling social media sites to identify trending products and styles. Users can then click on recommend items and add the items to their shopping cart.

When in a physical store, the chatbot is also able to answer questions about the store itself, such as: “Where is XYZ brand located?” or “What restaurants are in the food court?”

The new platform delivered a significant improvement in conversion rates.

Customer Service that Never Goes Out of Style

The new cognitive shopping experience provides customers real-time, personalized recommendations and better service, whether in-store or online. The recommendation engine leverages the company’s disparate data sources to present a consolidated “customer DNA” and provides added functionality across shopping advising, store advising, and more.

The new platform delivered a significant improvement in conversion rates.