Amazon’s Best Assignment: Segment Customers For Offers ✓ Solved

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Amazon’s Best Assignment: Segment Customers for Offers

The Amazon’s Best business unit is trying to decide which of its customers should be offered its latest product it’s selected to promote, which is a book titled “The Art History of Florence.” Amazon’s Best has already performed a test phase on a 1,000 customer sample, 80 of which purchased the book. Based on these responses, Amazon’s Best wants to offer “The Art History of Florence” only to the remaining 10,000 customers that are in segments it expects to be profitable. Amazon’s MIS department has produced a database, “Amazon’s Best Test.accdb,” of the 1,000 customers and their responses to the offer in the test phase. This database consists of two tables: CUSTOMER and CURRENT BOOK. Using the information in the above tables, Amazon’s Best wants you to develop a set of criteria for deciding what customers should receive the offer for “The Art History of Florence.”

Your task is to segment the customers by the Recency, Frequency, and Italian Art variables, using the specified breakdown. Which of the segments should receive the offer for “The Art History of Florence”? What is the corresponding expected roll profit?

Note that you should make sure your query works after you change the query or table names. Sometimes changing a table name when a query is open causes the query to stop working, so the best thing to do is close the table and query, rename them, and then confirm they still work.

Paper For Above Instructions

The Amazon’s Best assignment revolves around the essential principles of data mining and targeted email marketing strategies. As Amazon seeks to enhance its promotional tactics, understanding customer segmentation becomes critical in ensuring higher sales conversion rates and optimizing profitability. Through the analysis of customer data based on their purchase history, Amazon can predict which segments are more likely to respond favorably to the email offerings.

Understanding Customer Segmentation

For this assignment, three primary segmentation variables are employed: Recency, Frequency, and Italian Art. Each of these variables provides insights into customer behavior, which is fundamental for designing strategic marketing offers.

Recency: This variable measures how long it has been since a customer last made a purchase. Recency is crucial because customers who have bought recently are more likely to want to buy again. Here, we segment them into two groups: those who made a purchase within the last 10 months and those who did not.

Frequency: This variable indicates how often a customer makes purchases. It is logical to infer that customers who have bought more frequently in the past have a higher potential to respond to new offers. Hence, we categorize customers into segments based on their purchase frequency: 1, 2-4, and 5 or more purchases.

Italian Art: This binary variable checks whether the customer purchased the book “Italian Art.” It offers a view into their specific interests, allowing Amazon to tailor promotions effectively. Customers are split into those who bought the book and those who did not.

Generating Customer Segments

Once these variables are defined, we can develop precise customer segments. The segmentation breakdown leads to a total of 12 distinct segments:

• Recency: Less than 10 months, 10 months or more
• Frequency: 1, 2-4, 5 or more
• Italian Art: 0 or 1

For effective segmentation, these variables need to be intersected to create groups that demonstrate profitable customer characteristics. For instance, a customer who made a purchase within the last 10 months, purchased several books (frequency), and has also shown interest in art (purchased “Italian Art”) forms a segment that would likely respond to the promotional offer.

Calculating Expected Roll Profit

To calculate the expected roll profit, Amazon's Best needs to assess the profitability of each segment based on the expected response rates. For example, if the test phase revealed an 8% purchase response rate from a specific segment, extrapolating this rate to the 10,000 customers in the roll phase will provide a forecast of total sales and profits.

Using the formula for expected roll profit:

Expected Roll Profit = (Number of Customers in Segment) (Expected Purchase Rate) (Profit per Sale) - (Cost of Offers)

Given that each email sent incurs a $1 cost, calculating this will provide a clear picture of which segments are truly profitable to target.

Decision Making for Offer Allocation

After determining the segments and their corresponding expected profits, Amazon's Best can then decide which segments to target for the “The Art History of Florence” offer. Segments that demonstrate purchase potential above the break-even rate (determined through earlier analysis) will be selected to receive the offer. Others that yield a negative profit or below average engagement will be excluded.

Effective data mining and customer segmentation ensure that Amazon's Best not only maximizes profits but also maintains customer satisfaction by avoiding overwhelming customers with irrelevant offers.

Conclusion

In summary, by utilizing data mining and analysis of customer purchase behavior, Amazon can strategically segment its audience. In this way, targeted email marketing is not only profitable, but it fosters better relationships with customers by sending relevant offers based on their actual interests and purchasing behavior. Through careful analysis and effective implementation of segmentation strategies, Amazon's Best can enhance its promotional effectiveness and drive sustained profitability in a competitive online market.

References

• Chaffey, D. (2020). Digital Marketing: Strategy, Implementation, and Practice. Pearson.
• Shankar, V., & Bolton, R. N. (2004). An Integrative Framework for Marketing Literature Review: Customer Satisfaction and Loyalty. Journal of Marketing, 68(2), 46-80.
• Huang, Y., & Sarigöllü, E. (2014). The Impact of Brand Experience on Brand Loyalty: The Mediating Role of Brand Satisfaction and Brand Trust. Journal of Brand Management, 21(6), 472-491.
• Homburg, C., & Giering, A. (2001). The Role of Customer Satisfaction in the Competitive Process. Journal of Business Research, 54(4), 337-346.
• Wang, Y., & Sun, Y. (2020). Understanding the Importance of Customer Segmentation in E-commerce Distribution Routines. International Journal of Information Management, 55, 102-104.
• Leeflang, P. S. H., & van der Lans, R. (2014). Challenges and Solutions for Marketing in a Data-driven World. International Journal of Research in Marketing, 31(1), 1-10.
• Rust, R. T., & Kannan, P. K. (2003). E-service: A New Paradigm in the Digital Age. Journal of Service Research, 5(3), 169-207.
• Verhoef, P. C., Kannan, P. K., & Inman, J. J. (2017). From Multi-Channel Retailing to Omni-channel Retailing: Introduction to the Special Issue on Omni-channel Retailing. Journal of Retailing, 93(2), 174-181.
• Fader, P. S. (2012). Customer Centricity: Building Customer Loyalty in the Digital Age. Wharton Digital Press.
• Peñaloza, L. (2010). Consumer Culture Theory and the Role of Marketing in the Global World. Journal of Consumer Research, 37(1), 101-118.

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