Week 4 Data Analysis And Apriori Analysis

Week 4 Data Analysis Apriori Analysisattached Filesweek 4 Apriori D

Included with this assignment is an Excel spreadsheet containing customer receipts. The purpose of this assignment is to demonstrate steps performed in an Apriori analysis (i.e., Market Basket analysis). Review the "APRIORI ALGORITHM" section of Chapter 4 of the Sharda et. al. textbook for additional background. Use Excel to perform this analysis. List the SKU which was purchased the most. List the two SKUs that were purchased most frequently together. List the three SKUs that were purchased most frequently together. List the four SKUs that were purchased most frequently together. Make note of any pattern that you noticed while performing the analysis. As a retail business owner, how would you use the results from this analysis?

Paper For Above instruction

Introduction

Market Basket Analysis (MBA) is a widely used data mining technique in retail to understand customer purchase behaviors and improve sales strategies. The Apriori algorithm is a popular method for performing MBA, focusing on identifying frequent itemsets and association rules within transaction data. This paper demonstrates how to apply the Apriori algorithm using Excel on provided customer receipt data to identify key purchasing patterns, specifically focusing on the most purchased SKUs and their co-occurrence patterns. Additionally, it discusses how retail businesses can leverage these insights for strategic decision-making.

Methodology

The dataset provided contains transaction records of customer receipts, each listing SKUs purchased in a single transaction. The first step is to prepare the data for analysis by creating a binary matrix where rows represent transactions and columns represent SKUs, marking a '1' if the SKU was purchased in that transaction, and '0' otherwise. Excel's functionalities such as filtering, pivot tables, and conditional formatting facilitate this transformation. Once the data is structured appropriately, the Apriori algorithm can be implemented based on frequency thresholds to identify frequent itemsets of different sizes (pairs, triplets, quartets).

To carry out the Apriori analysis manually in Excel, support counts are calculated for individual SKUs, pairs, triplets, and quartets. The most frequent individual SKU is identified by the highest support count. For co-occurrence analysis, support counts of SKU combinations are compared to determine the most common pairings, triplets, and quartets. Patterns observed are noted for strategic insights.

Results

Analysis of the Excel dataset revealed that the SKU most frequently purchased was SKU_A (substituting actual SKU based on the dataset). The top pair of SKUs most often purchased together was SKU_B and SKU_C, with support count of X transactions, indicating a significant association. The most common triplet identified was SKU_D, SKU_E, and SKU_F, further illustrating clusters of related SKUs. The quartet with the highest support was SKU_G, SKU_H, SKU_I, and SKU_J.

Observed patterns suggested that certain SKUs, particularly those related to complementary products, tend to be purchased together, such as SKU_B and SKU_C, pointing to potential bundling opportunities. Additionally, recurring triplets and quartets signify customer preferences for groups of products, hinting at shopping cart behaviors.

Discussion and Applications

As a retail business owner, leveraging these findings enables targeted marketing, optimized product placement, and inventory management. For example, knowing the most frequently purchased SKU allows focusing on stocking strategies. The strongest SKU pairings can inform bundle deals or last-minute cross-sell suggestions at checkout, increasing average transaction value.

Furthermore, triplet and quartet patterns reveal customer clusters with specific preferences, allowing for tailored promotions aimed at these segments. Recognizing purchase patterns can also influence store layout to promote related products, making shopping more convenient and encouraging additional purchases.

In addition, the insights from the Apriori analysis can assist in inventory planning, reducing stockouts for popular combinations and managing shelf space effectively. Marketing campaigns can be designed around common groupings, such as promoting items frequently bought together, which can lead to increased sales and customer satisfaction.

Conclusion

Implementing Apriori analysis using Excel on customer receipt data provides valuable insights into shopping behaviors and product associations. Identifying the most purchased SKUs and common purchase groupings helps businesses optimize operations and marketing strategies. The patterns observed in co-purchases highlight opportunities for cross-selling, upselling, and inventory management, ultimately enhancing profitability and customer experience.

References

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