A/B Testing Analysis on Fast Food

Fries Don't Sell Themselves, But Data Can Help

The project goal was to analyze the effectiveness of fast-food marketing campaigns in promoting a new menu item, utilizing A/B testing methods. This involved examining the relationship between marketing promotions, market size, location characteristics, and sales performance. A critical aspect of this analysis was the extensive data preparation and statistical modeling used in Excel. Descriptive statistics, including mean, variance, and standard deviation, were calculated to assess sales trends and distribution patterns. Linear regression was used to measure the correlation between variables such as market size and sales, while multiple regression analysis explored the combined impact of promotion type and market size on revenue. Furthermore, visualizations, such as dashboards and scatter plots were created to interpret and communicate the findings in a more effective manner. By integrating data insights with statistical modeling, I was able to pinpoint the strongest predictors of sales performance.

This project highlights my ability to analyze real-world data using statistical and visualization tools, as well as draw actionable insights from complex datasets. By integrating Excel, I was able to design clear and concise points that answered the research questions but also delivered clear ways forward for marketing strategies. This demonstrates by abilities in data analysis, storytelling, and providing value-driven insights to make informed business decisions.

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