Electronics Product Reviews and Ratings

This project was carried out as part of the February 2024 DataDNA Dataset Challenge Organised by Onyx Data.

The purpose of this project was to investigate consumer ratings and reviews on electronics products. The analysis centered on determining the correlation between product ratings and recommendation status, brands that have the highest customer satisfaction ratings, how those ratings vary across different product categories, and lastly, how product attributes such as color, dimension, and weight affect customer reviews and ratings.

Data Analysis with Microsoft Excel:

For this challenge, a dataset of customer reviews and ratings for electronics products was provided, which included information about the brand, product name/category, product attributes, reviews, review rating, and recommendations. The dataset was cleaned and then put through exploratory data analysis to find trends and patterns that emerged.

Visualization with Power BI:

After the dataset was cleansed and examined to determine its scope, it was imported into Power BI for visualization. The rating distribution and average rating, the relationship between emotionality and star ratings and recommendations for enhancing customer experience were the main areas of focus.

Key Takeaways:

Review Summary: The majority of the products that were exhibited met the basic standards of most customers, as seen by the significantly higher percentage of reviews that had five star ratings. Additionally, reviews with ratings above average were recommended, whereas reviews with ratings below average were not. This indicates that higher product ratings are positively correlated with a favorable recommendation status.

Relationship Between Emotionality and Star Ratings: While star ratings give a succinct summary of customer satisfaction, sentiment analysis offers a qualitative evaluation of the quantitative feedback in customer reviews. This is demonstrated by the fact that negative sentiments were present in even reviews with four star ratings, which, fortunately, offers an opportunity to improve even the best brands and products. The majority of customers have had great experiences, as seen by the 78% of positive reviews.

Review Influencers: Review Influencers: Product attributes (colour, dimension,weight) seemed to significantly influence product rationgs

Conclusion:

In conclusion, the project effectively outlined the relationship between product ratings and recommendation status, evaluated the quantitative feedback in customer reviews qualitatively using sentiment analysis, and offered suggestions for how businesses could enhance customer experience.