See how we are responding to COVID-19 and supporting our employees and customers
Back to all case studies

Customer Review Analytics Solution for E-commerce

Enhanced customer satisfaction with smart emotion intelligence monitoring.

Customer Review Analytics Solution
Key Details

Enhanced customer satisfaction with smart emotion intelligence monitoring.

  • Challenge
    Detect sentiment and emotional tone in customer product reviews with ML
  • Solution
    AI-powered sentiment analysis software
  • Technologies and tools
    Python, PyTorch, jupiter, Streamlit, Hugging Face, pandas

Client

The client is a large US E-commerce platform with rapidly growing online business. The company is constantly working on enhancing the customer experience by improving products and services offered. They needed to build an automated system which identifies sentiment in customers reviews about the products and services. They wanted to get insights into customer satisfaction to implement prompt marketing and customer care tactics towards poor customer satisfaction and offering upselling to the ones who are receptive to it. So, they asked the InData Labs team to develop a customer review analytics solution.

Challenge: detect sentiment and emotional tone in customer product reviews with ML

Sentiment analysis is important to uncover customer satisfaction and recognize their preferences through emails, comments, tweets, etc. To enable insight extraction, the InData Labs team developed an ML model based on customer reviews on products to detect opinions of customers.

To build an emotion intelligence model for product review analytics, we went through the following process of:

Solution: AI-powered sentiment analysis software

To enable sentiment classification, we made ML models and deep neural networks. We worked with:

  • Transformers models like BERT.
  • Logistic Regression, Support Vector machine (SVM) and Naïve Bayes – traditional ML models for document review analytics and sentiment classification.
  • Deep neural networks used for sentiment analysis: Recurrent Neural Network, Long short term memory (LSTM) and Gated Recurrent Unit (GRU).

Take a closer look at the pipeline created:

Customer Review Analytics Solution scheme

To develop the solution, we used the following technologies:

Customer Review Analytics Solution technologies

Result: an automated system to identify sentiment in customers reviews

Automating customer satisfaction data collection, we enabled actionable information about consumer attitudes toward the client’s products and services at much higher response levels. Our sentiment analysis solution has helped the client determine if a particular customer segment feels more strongly about business or not. Besides, it’s made it easier for them to track how a change in product or service affects customers.

The key benefits of the product review analytics:

  • Data collection and analysis of total customer reviews
  • Tracking the company’s brand strength
  • Tracking overall customer satisfaction
  • Customer mood change detection
  • Customer emotional trigger detection
  • Customer churn prediction
  • Live insights for prompt marketing tactics to support customer satisfaction strategy
Tags:
  • E-commerce
  • Marketing & Advertising
  • NLP
  • Big Data

Contact InData Labs

Want to start getting value from your data? Fill the form. Click send. Let's talk.

    By clicking Send Message, you agree to our Terms of Use and Privacy Policy.