Analysis performed for an e-commerce company and business which studies the sales and products sold considering some customers characteristics like nationality or gender, using Python, SQL and Looker.
| Described in detail in section 3 (Report), the analyzed data set was collected to better understand the distribution of customers considering their nationality, gender, age group or city. The data was extracted from Google Big Data and didn’t have relevant noise, so the only change performed was to create a new column to classify each customer according to age, splitting them by 6 distinct age groups: teens, twenties, thirties, forties, fifties and elders. | |
| Section 2 (Report) gives more details about the key questions to be analyzed and answered, which pretend to identify possible trends and patterns related with customers' behavior and their gender or nationality, among other characteristics. The main task was performed by asking 15 questions mentioned along the report. | |
| In order to achieve the final results, and implement my analysis, the data was cleaned using python and to perform the visualizations, looker studio was used. Section 4 (Report) shows the main findings of the project. | |
| The results are discussed in section 5 (Report) with more detail, but, generally speaking, it was found that, considering gender, in general, men tend to spend more money and buy more expensive products. The top 3 countries with more money spent are China, United States and Brazil, but France and the United Kingdom are the countries with more money spent by person, for men and women respectively. |
After a intensive and deep exploration and analysis of the data, one can highlight the follow
main conclusions:
- Genders are distributed almost equally
- Regarding the total retail price and number of orders, China, USA and Brazil are the 3 countries
- We have a clear difference between the behavior of men and women regarding products’ categories and brands
- If we estimate the increasement of most popular sold product, the total price has a growth sound 43% for men and 27% for women
- In general the elderly men are the ones who tend to buy more expensive products