Overview
Sales Probability Analysis for Returning Customers
November and December, the peak shopping months. The primary goal was to evaluate returning customers’ purchase rates, identify behavioral correlations, and estimate the probability of achieving ambitious sales targets under a new marketing campaign. Using Python (pandas, seaborn, scipy), the analysis showed that with a 15% boosted conversion rate, the expected sales were ~75 out of 500 sessions, while achieving 100+ sales remained statistically rare. The results suggest that increasing total traffic, improving conversion rates beyond 15%, and optimizing targeting are the next steps.