Terno | E-commerce Customer Review Analysis

Customer Reviews Analysis with Terno-AI

In the digital shopping age, understanding customers means understanding their words. Every review tells a story—why someone loved a product, why they didn’t, and what they expect next. But sifting through tens of thousands of reviews is overwhelming.

That’s where TernoAI comes in. I conducted a comprehensive data science journey on the Women’s E-Commerce Clothing Reviews dataset, not by coding for hours, but by simply asking a few key questions.

Why TernoAI? Turning Curiosity into Analysis

TernoAI is designed for individuals who seek data-driven insights without requiring code. It translates plain English prompts into the heavy lifting of data science: cleaning data, exploring it, building models, and surfacing insights.

All I had to do was ask the right questions. Here’s what happened.

Step 1: Understanding the Data

Prompt: “Perform exploratory data analysis on the dataset, including summary statistics, missing values, and key visualizations for ratings, departments, and recommendations.”

TernoAI came back with a full EDA in seconds:

  • 23,486 reviews, average reviewer age 43 years (18–99).

  • Mean rating: 4.2 stars, with a noticeable skew toward 5‑star ratings.

  • Recommendation rate: ~82% of customers would recommend the products.

  • Most-reviewed department: Tops (10,468 reviews).

  • Missing data: Titles (3,810), review texts (845), and division details (14).

    • Terno | EDA on Customer Review Analysis
    Terno | EDA Results Customer Review Analysis

    Visualization generated by Terno AI

    Terno | Distribution of Ratings
    Terno | Review by Department
    Terno | Reccomendations counts

    Step 2: What Customers Really Feel

    Prompt: “Run sentiment analysis on the review text and show the distribution of positive, neutral, and negative sentiments.”

    TernoAI classified all 23,486 reviews:

  • Positive: 17,882

  • Neutral: 5,057

  • Negative: 547

    • Terno | Sentiment Analysis on Review
    Terno | Result Sentiment Analysis on Review
    Terno | Sentiment Distribution

    The sentiment analysis showed that most reviews were overwhelmingly positive, with the word cloud highlighting frequently used expressions of satisfaction such as ‘perfect,’ ‘comfortable,’ ‘beautiful,’ ‘love,’ and ‘amazing.’

    Step 3: Predicting Recommendations

    Prompt: “Build and compare models to predict whether a review recommends the product (‘Recommended IND’) using review text, ratings, and other features. Evaluate with accuracy, precision, recall, and F1‑score.”

    Terno | Model to Predict a Review Recommend a Product or not

    TernoAI built and compared two models:

    ModelAccuracyPrecisionRecallF1‑score
    Logistic Regression0.93990.96160.96520.9634
    Random Forest0.92960.94260.97330.9577

    Logistic Regression emerged as the winner, with the highest F1‑score, proving especially effective in predicting whether a customer would recommend a product.

    Step 4: What Drives Positive (and Negative) Recommendations?

    Prompt: “Summarize the insights: which factors drive positive recommendations and what patterns are common in negative reviews?”

    Terno | Summary Customer Review Analysis
    Terno | Summary 2 Customer Sentiment Analysis

    TernoAI’s findings were clear:

  • Rating is the strongest driver—4–5 star ratings almost always lead to recommendations.

  • Positive feedback count matters—reviews marked as helpful slightly improve the likelihood of a recommendation.

  • Textual cues: Words around fit (“true to size,” “comfortable”), quality (“excellent,” “beautiful”), and enthusiasm (“love,” “amazing”) dominate positive recommendations.

  • Negative reviews often cite inconsistent sizing, poor fit, or fabric quality.

    • The No‑Code Advantage

    In just four prompts, I accomplished:

  • Full EDA with clear visualizations

  • Sentiment analysis with distribution insights and visualisations

  • Predictive modeling with side‑by‑side algorithm comparison.

  • Actionable findings to guide product improvements

    • All of this—without a single line of manual coding. No debugging, no setup headaches, just fast and accurate results.

    Why This Matters

    For fashion retailers, understanding what customers genuinely love—and where they feel let down—is mission‑critical. With TernoAI, teams can:

  • Identify the features that drive recommendations

  • Quickly uncover recurring issues in negative feedback

  • Refine products, sizing, and design based on authentic customer voices

  • Strengthen loyalty by turning feedback into action

    • Final Takeaway: Turning Feedback into Action

    TernoAI transforms data science into a simple conversation. With a few well‑chosen prompts, I uncovered powerful insights from thousands of e‑commerce reviews—insights that can immediately influence product strategy, marketing, and customer experience.

    The next time you’re buried under an avalanche of unstructured customer feedback, remember: with TernoAI, actionable answers are just a prompt away.

    Chat source: https://nishtha.app.terno.ai/chat/share/1ef8ca0d-2080-406c-aed6-b6dd7bf9c644