Introducton
Jumia is an e-commerce platform where different sellers list their products and customers purchase them online. The platform generates data as sellers list products and customers interact with it and purchase products.
In this project, we analyze 112 products listed on Jumia that would give Jumia and its sellers a better understanding of how price, promotions and customer's feedback influence product performance.
Dataset Overview
The dataset contained 6 key fields: Products, current price, Old price, Rating, Reviews and Discount.
I identified several data quality issues like missing values especially on the Rating and Review columns, Some columns such as Current price and Old price were in string/text form and not in their true data type which is supposed to number/currency.
Ratingd header is misspelled.
Data Cleaning and preparation
Identifying and removal of duplicates is esential before data analysis this helps to evade inaccuracy.
The other step is to remove Kshs, commas and extra spaces from Current and Old prices columns. Then convert to a number.
=Value(SUBSTITUTE(SUBSTITUTE(B2,"ksh",""),",",""))
On Review column we change the negative values to absolute values
IF(E2="","",ABS(VALUE(E2)))
In Rating we remove "out of 5" to chage its data type to a decimal number.
=IF(F2="","",VALUE(SUBSTITUTE(F2,"out of 5","")))
Data Enrichment
To make our dataset more complete and valuable we add a few columns;
Discount Amount- The amount that a customer saves when a product is sold at a reduced price.
(Old price- current price)
Rating Category- Poor<3, Average- 3-4.5, Excellent>4.5
=IF(F2="","Missing",IF(F2<3,"Poor",IF(F2<=4.5,"Average","Excellent")
Discount Category- Low Discount<20%, Medium Discount=20%-40%, High Discount>40%
=IF(D2="","Missing",IF(D2<20%,"Low Discount",IF(D2<=40%,"Medium Discount","High Discount")))
Price Category- We categorize price based on Quartiles
Q1-493, Q3-1670
And named their cells as Price_Q1 and Price_Q3
=IF(B2="","Missing",IF(B2<=Price_Q1,"Low Price",IF(B2<=Price_Q3,"Medium Price","High Price")))
Data Analysis
After cleaning and preparing data we moved to analyzing it
Deriving KPIs
| Variable | values |
|---|---|
| Total products | 112 |
| Average Current Price | Ksh 1,187 |
| Average Old Price | Ksh 1,811 |
| Average Discount | Ksh 624 |
| Average Rating | 3.9 |
| Total reviews | 723 |
| Most Expensive Price | Ksh 3750-32pcs portable codeless drill |
| Least Expensive Price | Ksh 38- Single head knitting crothet sweater needle set |
Relationship analysis
I created 3 scatter charts and derived R-squared and their correlations.
Discount vs Reviews
The analysis shows weak negative correlation.
Correl= -0.137
R2 = 0.0187
Current price vs Rating
Week positive correlation
Correl= 0.1101
R2 =0.0121
Rating VS Reviews
Week Positive correlation
Correl= 0.0572
R2= 0.0033
Dashboard Creation
The complete workbook, including the raw and cleaned data, analysis sheets, PivotTables, charts, and final interactive dashboard, is available in my GitHub repository below.
https://github.com/tonnymuthuri6-lang/JUMIA-PRODUCT-PERFORMANCE-DASHBOARD
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