It’s commonly known that attracting new clients is much more expensive than retaining existing ones. But how do we know how many clients we are retaining and how effectively?
Cohort Analysis of sales with Python is a powerful tool that allows businesses to gain a deeper understanding of their customer behavior and make informed decisions based on data-driven insights. By grouping customers into cohorts based on the time they made their first purchase, businesses can evaluate how these cohorts contribute to revenue over time.
With Python’s robust libraries such as pandas and matplotlib, analysts can easily aggregate and visualize the data, enabling them to identify patterns, trends, and similarities across different customer groups. These findings may reveal valuable information such as customer lifetime value, retention rates, and user engagement metrics within specific cohorts.
In addition to providing actionable insights for marketing strategies, cohort analysis with Python empowers companies to optimize acquisition efforts by targeting specific customer segments that generate higher long-term value. Overall, this sophisticated analytical technique allows businesses to make informed decisions that drive growth and maximize profitability in today’s dynamic marketplace.
At the link below you can check out the Python code with cohort sales analysis of sales of one large online store.
