Harvard Case - Predicting Purchasing Behavior at PriceMart (A)
"Predicting Purchasing Behavior at PriceMart (A)" Harvard business case study is written by Srikant M. Datar, Caitlin N. Bowler. It deals with the challenges in the field of Accounting. The case study is 17 page(s) long and it was first published on : Aug 23, 2018
At Fern Fort University, we recommend PriceMart implement a multi-pronged approach to improve its understanding of customer purchasing behavior and enhance its profitability. This approach involves leveraging data analytics, refining its cost accounting system, and implementing a customer segmentation strategy.
2. Background
PriceMart, a large-scale discount retailer, is facing challenges in predicting customer purchasing behavior. Despite its success in attracting customers through low prices, PriceMart struggles to pinpoint specific customer needs and preferences. This lack of understanding hinders its ability to optimize product selection, inventory management, and marketing efforts.
The case study focuses on the efforts of PriceMart's management team, particularly the Chief Financial Officer (CFO), to address this issue. The CFO aims to improve the company's profitability by better understanding customer behavior and implementing targeted strategies.
3. Analysis of the Case Study
The case study highlights several key issues affecting PriceMart's profitability:
1. Lack of Customer Data: PriceMart lacks a comprehensive system to collect and analyze customer data. This limits its ability to understand customer preferences, buying patterns, and the factors influencing their purchasing decisions.
2. Inefficient Cost Accounting System: PriceMart's current cost accounting system relies heavily on traditional methods, making it difficult to accurately assess the profitability of individual products and customer segments. This hinders decision-making regarding pricing, inventory management, and product mix.
3. Limited Customer Segmentation: PriceMart operates under a one-size-fits-all approach, failing to recognize the diverse needs and preferences of its customer base. This limits its ability to tailor its offerings and marketing campaigns to specific customer segments.
4. Lack of Data-Driven Decision Making: The absence of robust customer data and a sophisticated cost accounting system limits PriceMart's ability to make informed decisions regarding product selection, pricing, and marketing.
5. Insufficient Employee Incentives: The current employee incentive structure does not adequately reward employees for understanding and catering to customer needs. This can lead to a lack of focus on customer service and understanding customer behavior.
4. Recommendations
1. Implement a Data Analytics Platform: PriceMart should invest in a robust data analytics platform to collect, analyze, and interpret customer data. This platform should capture data from various sources, including point-of-sale systems, loyalty programs, website interactions, and social media.
2. Refine Cost Accounting System: PriceMart should implement an activity-based costing (ABC) system to accurately allocate costs to individual products and customer segments. This will provide a more accurate picture of profitability and enable informed decision-making regarding pricing, product mix, and inventory management.
3. Develop Customer Segmentation Strategy: PriceMart should develop a customer segmentation strategy based on demographics, purchase history, shopping behavior, and other relevant factors. This will allow PriceMart to tailor its product offerings, marketing campaigns, and customer service strategies to specific customer segments.
4. Implement Employee Incentives: PriceMart should implement an employee incentive structure that rewards employees for understanding and catering to customer needs. This can be achieved through bonuses, commissions, or other performance-based rewards tied to customer satisfaction metrics.
5. Enhance Data-Driven Decision Making: PriceMart should integrate data analytics into its decision-making processes. This includes using data to inform product selection, pricing, marketing campaigns, and inventory management.
5. Basis of Recommendations
1. Core Competencies and Consistency with Mission: These recommendations align with PriceMart's core competency of offering low prices and its mission of providing value to customers. By understanding customer needs and preferences, PriceMart can optimize its product selection and pricing to further enhance its value proposition.
2. External Customers and Internal Clients: The recommendations directly address the needs of external customers by providing them with a more personalized shopping experience. They also benefit internal clients, such as the management team, by providing them with better data-driven insights to make informed decisions.
3. Competitors: By implementing these recommendations, PriceMart can gain a competitive advantage by becoming more customer-centric and data-driven. This will allow them to better understand customer needs and preferences, differentiate themselves from competitors, and potentially attract new customers.
4. Attractiveness ' Quantitative Measures: The implementation of these recommendations is expected to increase PriceMart's profitability by improving its understanding of customer behavior, optimizing its product mix, and enhancing its marketing effectiveness.
5. Assumptions: The success of these recommendations relies on the accurate collection and analysis of customer data, the effective implementation of an ABC system, and the willingness of employees to embrace data-driven decision making.
6. Conclusion
By implementing these recommendations, PriceMart can significantly improve its understanding of customer purchasing behavior, enhance its profitability, and gain a competitive advantage in the discount retail market.
7. Discussion
Other Alternatives:
- Partnering with a third-party data analytics firm: This could provide PriceMart with access to advanced analytics capabilities and expertise. However, this approach may be more expensive and could lead to data security concerns.
- Implementing a loyalty program: This could help PriceMart collect more customer data and build customer relationships. However, this approach may require significant investment and could be difficult to manage effectively.
Risks and Key Assumptions:
- Data quality: The accuracy and completeness of the collected data are crucial for the success of the recommendations.
- Employee resistance: Some employees may resist the changes required to implement the recommendations.
- Cost of implementation: The implementation of these recommendations may require significant investment.
8. Next Steps
Timeline with Key Milestones:
Month 1:
- Initiate the selection process for a data analytics platform.
- Begin training employees on data privacy and security.
Month 3:
- Implement the data analytics platform.
- Begin collecting and analyzing customer data.
Month 6:
- Implement the ABC system.
- Develop customer segmentation strategy.
Month 9:
- Implement employee incentives.
- Begin using data-driven insights to inform decision making.
Month 12:
- Evaluate the effectiveness of the implemented changes.
- Make adjustments as needed to optimize the process.
By following these steps, PriceMart can effectively implement the recommendations and achieve its goals of improving its understanding of customer purchasing behavior and enhancing its profitability.
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Case Description
This case follows VP of Marketing, Jill Wehunt, and analyst Mark Morse as they tackle a predictive analytics project to increase sales in the Mom & Baby unit of a nationally recognize retailer, PriceMart. Wehunt observed that in the midst of the chaos that surrounded a new baby, parents' shopping habits became quickly ingrained. She hypothesized that if she could get households expecting a new baby to make PriceMart a part of their routines before becoming parents, she might keep them as customers for the next several years, winning significant additional revenue. Technical topics covered: Collecting data and preparing a dataset; constructing training, validation, and holdout sets; cross-validation; Linear regression as a modelling technique; statistical tests; Logistic regression as a modelling technique for estimating predictions between 0 and 1; maximum likelihood estimation; log likelihood; Comparing model outputs.
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