Embedded Files
Tags: Data Cleaning, Data Analysis, Customer Behavior
Seaborn
Pandas
Python
Jupyter Notebook
● GOAL
● GOAL
Analyze product, sales, and marketing metrics to understand customer behavior and interactions on Yandex Afisha. Use these insights to make informed marketing budget allocations that drive revenue growth and foster long-term customer relationships.
Business Question:
How can we allocate our marketing budget to target users with higher purchase intent and lifetime values, thereby optimizing our marketing expenses?
● OVERVIEW
● OVERVIEW
This project seeks to understand customer behavior from multiple perspectives to provide insights to marketing experts on how much money to invest and where. For this analysis, we used a dataset comprising server logs detailing users' visits and order records from June 2017 to May 2018, along with marketing expenses.
The analyses conducted were as follows:
Product
Active Users
Session per Day
Lenght of Sessions
Retention Rate
Sales
Time to Conversion
Number of Orders
Average Purchase Size
Customer Lifetime Value
Marketing
Total Marketing Expenses
Customer Acquisition Cost (CAC)
Return on Investment (ROI)
● RESULTS
● RESULTS
Identified key patterns in user activity, cohort retention rates and conversion rates.
Detected significant variations in spending behaviors among cohorts.
Found correlations between marketing costs and user acquisition.
Discovered variations in the return on investment across different marketing channels.
Provided recommendations regarding resource allocation and marketing initiatives.
To see the code, please visit my GitHub.
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