Project Overview:
The project analyzed ShopEasy customers, products, reviews, and engagement datasets. Aiming to solve the problem of a decline in customer engagement, conversion rates, and increasing ROI on marketing spend, and analyzing customer feedback to boost customer engagement and conversion rates.
Key Metrics:
Revenue, Spending, ROI, Impressions, CTR, Conversions
Data Models:
Analytical Tools:
SQL Server, Power BI, Tableau
Project Description:
This project examined customer, product, review, engagement, and customer journey data to identify why customer engagement, conversion rates, and marketing performance were declining. The dataset was restored from a SQL Server backup database, then cleaned, transformed, and analyzed using SQL Server, Python, Tableau, and Power BI.
I used SQL to extract and prepare customer, product, review, engagement, and journey tables, including enriching customer data with geography, categorizing products by price, cleaning review text, and normalizing engagement and journey data. I also used Python VADER sentiment analysis to convert customer review text into sentiment scores.
In Tableau, I built exploratory worksheets and dashboards to analyze engagement trends, CTR, conversion rate, drop-off rate, sentiment patterns, repeat purchases, and correlations between sentiment, views, likes, and conversions. Finally, I created Power BI dashboards to summarize key findings and gave recommendations.
SQL Code:
Tableau Analysis:
