How a Small Business Can Use Data Analytics to Drive Revenue and Maximize Profits

Summary

In this project, data science student Roy Edgar addresses declining sales at a bakery facing increased competition. By conducting market basket analysis on 75,000 transaction records, he identifies key product combinations, such as apricot Danish and pear cake, that can be strategically displayed to enhance sales. Adjusting analysis parameters reveals even more product pairings, offering actionable insights without major marketing efforts. This case exemplifies how data analysis can provide practical solutions for businesses, highlighting the importance of strategic placement and product bundling in boosting customer purchases.

Takeaways

• 😀 The speaker, Roy Edgar, is a data science and machine learning student focused on real-time projects.
• 😀 A bakery store in the U.S. is facing losses due to increased competition.
• 😀 Market basket analysis is proposed as a solution to improve the bakery's sales.
• 😀 Data from various bakery stores was compiled into a CSV file for analysis.
• 😀 A frequency plot was created to visualize item purchases and their support levels.
• 😀 Support is set at 4%, meaning items are purchased in 4 out of 100 transactions.
• 😀 Confidence is set at 60%, indicating a strong likelihood of item combinations being purchased together.
• 😀 The first model identified three frequent item combinations to enhance sales strategies.
• 😀 A second model with different parameters generated 49 potential item combinations.
• 😀 The analysis provides actionable strategies to boost sales without specific marketing or investment.

Q & A

What is the main problem faced by the bakery store?

The bakery store is experiencing losses due to increased competition, despite previously good sales.

What analytical method does Roy Edgar propose to address the bakery's issues?

Roy proposes using market basket analysis to understand customer purchasing patterns and improve sales.

How many rows of data were compiled for the analysis?

Roy compiled data from the city's bakery stores, totaling 75,000 rows in the CSV file.

What are the support and confidence thresholds set for the first model?

The first model is set with a support threshold of 4% and a confidence level of 60%.

What items were identified as frequently purchased together?

The items identified include apricot Danish, para cake, and cherry tart.

What strategy does Roy suggest for increasing sales based on the analysis?

Roy suggests placing the frequently purchased items together on the shelf and offering discounts or packages.

How many rules were generated in the second model with different parameters?

The second model generated 49 rules with a support threshold of 3% and a confidence level of 40%.

What does the increased number of rules indicate?

The increased number of rules suggests a wider variety of logical item combinations that can enhance sales strategies.

What key benefit does Roy highlight about using data analysis for the bakery?

Roy highlights that specific strategies derived from data analysis can be implemented immediately to help increase sales.

Who does Roy thank at the end of the presentation?

Roy thanks America's Learning for providing him with the opportunity to work on a live project and showcase his skills.