Introduction

In today's digital age, businesses strive to ensure that they offer their customers a seamless experience that is personalized and optimized to fulfil specific demands.

Understanding customers' pain points and finding fitting solutions are paramount to satisfying end customers. For the economic development of a company, gaining insight into how customers perceive their products will help them improve their offers and the product itself.

In this case study, we focus on our AI-based solution “Product sales feedback analyzer” for an e-commerce website, which examines bulk feedback data from customers and vendors to understand and interpret product information.

The Customer

Our client is a leading manufacturer and distributor of computer hardware products, laptops, desktops, servers, monitors, etc. With a solid market reputation in Australia and New Zealand, our client channels its products through partners, including retailers, educational institutions, government agencies, and other business entities that purchased computer products in bulk.

The client implemented our AI-based product feedback analyzer on multiple platforms to generate meaningful insights from the feedback data from vendors and customers. We continue to work alongside them as their reliable partner.

The Challenge

• Despite having a very well-structured B2B and B2C site, the customer needed help to classify and make sense of the feedback data due to thousands of feedback from multiple sources.
• Manual analytical processes to verify emails and feedback reduced the quality of the analysis.
• Analyst teams spend a considerable amount of time and effort to understand feedback data from different sources.
• Reviewing thousands of emails and feedback using a large team of analysts increased the resource management cost.
• Absence of good data presentation practices such as interactive dashboards with charts to understand insights and make informed decisions.
• Inaccurate understanding of bulk feedback data to identify products that need rectification and change in marketing strategies.

The Business Requirement

The business approached Zlabs to build an application powered by AI, which would offer insight into customer feedback while eliminating all the possibilities of error arising from manual work.

• The application should identify the trending products, key summaries, and key information from feedback data.
• Identifying customer sentiments to provide value for future products.
• Identifying top keywords from product feedback to improve SEO-based marketing decisions.
• Identifying and categorizing feedback to improve product quality and user experience .
• Identifying top vendors and customers to improve customer retention and lifetime value .
• Generate meaningful insights to detect and prevent issues that impact customer conversion and loyalty.
• Understanding the insights of feedback data through visualization techniques.

The Solution

We developed an AI-based server-side and web application to enable an accurate and timely understanding of bulk feedback data from customers and vendors.

Through our customized AI model, we could determine business priorities and desired outcomes from a large amount of feedback data. Using our AI customer analytics tools, we were able to derive insights to understand market positioning and achieve continuous business improvement.

By leveraging our sales feedback analyzer, our customer was able to:

The Benefits

• The client was able to make better business decisions with the correct information at the right time.
• The customer saved thousands of hours organizing, classifying and summarizing bulk feedback data.
• Understanding sentiments in feedback data helped the client with informed decision-making and timely determinations.
• Elimination of manual errors and improvement in the quality of insights from feedback data.
• The client understood points along the customer journey causing churn to improve the overall customer experience.
• Improved customer retention reduced operational costs and improved customer lifetime value.