Customer feedback is one of the most valuable assets for any e-commerce business. Reviews not only influence buying decisions but also provide deep insights into customer satisfaction, product quality, and service efficiency. However, analyzing hundreds or thousands of reviews manually is impossible. That’s where Power BI dashboards come in.
In this blog, we’ll walk through an E-Commerce Reviews Sentiment Analysis Dashboard, created by Ankit Srivastava, Data Analytics Trainer at SlideScope Institute, who has trained over 10,000 students on Udemy. If you’re a beginner in Power BI, this breakdown will help you understand how to read dashboards, extract insights, and apply them in real-world business scenarios.
👉 Check out Ankit’s Udemy Profile
👉 Connect with Ankit on LinkedIn
1. High-Level KPIs – The Dashboard’s Quick Snapshot
At the top, you see five Key Performance Indicators (KPIs):
- Total Reviews (250): The dataset has 250 customer reviews.
- Avg Days Since Purchase (172.72): On average, reviews are written 173 days after purchase, showing a mix of long-term and short-term customer experiences.
- Helpful Votes (25K): Customers have given 25,000 helpful votes, suggesting how valuable these reviews are to other buyers.
- Avg Rating (2.98): The average customer rating is below 3, indicating mixed or slightly negative sentiment overall.
- Avg Review Length (252.33): The average review contains 252 words, which means customers are providing detailed feedback.
👉 For businesses, these KPIs act like a health check—they tell you how engaged your customers are and whether their experiences are positive or negative.
2. Average Rating by Customer Location
On the left side, there’s a map visualization showing customer ratings by location (e.g., Europe, Asia, North America).
- Businesses can instantly see geographical differences in customer satisfaction.
- For instance, if ratings in Europe are higher than Asia, the company may need to improve product quality or service in Asian markets.
👉 This is useful for global e-commerce businesses to tailor strategies region by region.
3. Average Rating by Product Category
The tree map in the center highlights product categories and their average ratings:
- Grocery (3.27) and Books (3.04) score relatively higher.
- Electronics (3.03), Fashion (3.03), and Beauty (2.97) fall in the mid-range.
- Sports (2.82) and Home (2.74) have the lowest ratings.
👉 Businesses can use this insight to prioritize improvements in underperforming categories. For example, the Home category needs quality checks or better customer service.
4. Ratings by Review Sentiment
The bar chart shows how ratings align with customer sentiment (Positive, Neutral, Negative):
- Positive Reviews (3.19 avg rating) are slightly above 3, showing satisfaction but with room for growth.
- Neutral Reviews (2.90 avg rating) suggest average customer experiences.
- Negative Reviews (2.64 avg rating) are predictably low.
👉 Businesses can identify whether negative reviews are dragging down the overall score and focus on addressing them.
5. Verified Purchase Ratings
This donut chart separates ratings between verified and non-verified purchases:
- Verified Purchases (3.06 avg rating) are rated higher.
- Non-verified Purchases (2.90 avg rating) are lower.
👉 This shows that authentic customers rate products slightly better, while non-verified reviewers may leave harsher or biased reviews. E-commerce platforms often use this to build trust by highlighting verified reviews.
6. Reviews Distribution by Rating
The histogram (bar chart) shows how many reviews fall into different rating bins (1–5 stars):
- A large number of reviews are 1 or 2 stars, which lowers the overall average rating.
- Moderate distribution exists across 3 to 5 stars.
👉 This tells us the product set has polarizing feedback—many customers are unhappy, while others are satisfied.
7. Days Since Purchase vs. Rating
The scatter plot connects days since purchase with rating behavior:
- Ratings are spread across both early and late reviews.
- Customers may initially give high ratings, but long-term users often downgrade their ratings if they face durability or quality issues.
👉 This visualization helps companies track product lifecycle satisfaction. If ratings drop after 200 days, the business might need to improve long-term product quality.
8. Filters for Deeper Analysis
On the right, the dashboard provides interactive slicers (filters):
- Customer Location → Filter ratings by country or region.
- Product Category → Drill down into categories like Electronics, Fashion, or Grocery.
- Review Sentiment → Filter only positive, neutral, or negative reviews.
- Verified Purchase → Separate real buyers from unverified ones.
👉 These filters allow managers to ask specific business questions like:
- How do Electronics reviews in Asia differ from Europe?
- Do verified buyers leave more positive reviews than non-verified buyers?
9. Why This Dashboard Matters for Beginners
For beginners learning Power BI:
- KPIs → Give a summary of overall performance.
- Maps & Tree Maps → Show geographical and category insights.
- Sentiment Analysis Charts → Break down emotions behind ratings.
- Scatter Plots → Reveal long-term vs. short-term satisfaction.
- Filters → Allow users to play with data and discover their own insights.
By studying this dashboard, you’ll learn how different visualization types complement each other to form a complete story.
10. Final Thoughts & Next Steps
This dashboard is a brilliant example of how customer reviews can be turned into actionable insights. Businesses can:
- Identify underperforming product categories.
- Track customer trust through verified vs. non-verified reviews.
- Compare satisfaction levels across regions.
- Predict long-term issues by analyzing reviews over time.
For beginners, this is not just about reading charts—it’s about understanding the story data tells.
Learn From the Expert
This dashboard was created by Ankit Srivastava, an experienced Data Analytics Trainer at SlideScope Institute. With over 10,000 students on Udemy, Ankit has helped learners master Power BI, Data Analytics, and Business Intelligence through practical, hands-on training.
👉 Visit Ankit’s Udemy Profile to Explore Courses
👉 Connect with Ankit on LinkedIn
If you’re serious about learning data analytics and Power BI, I highly recommend exploring his Udemy courses. They’re designed for beginners and professionals alike, with clear explanations and real-world projects.
✅ Call to Action for You
Want to analyze your own e-commerce or business data with Power BI? Start by recreating this dashboard using a sample dataset. Then, take Ankit’s Udemy course to learn step-by-step how to build advanced dashboards like this one.
📊 Turn your raw data into business insights—just like this!