Harvard Case - Measuring Price Promotion Effects - An Econometric Exercise in Measuring the Impact of Marketing Decision Making
"Measuring Price Promotion Effects - An Econometric Exercise in Measuring the Impact of Marketing Decision Making" Harvard business case study is written by Klaus Wertenbroch. It deals with the challenges in the field of Marketing. The case study is 2 page(s) long and it was first published on : Mar 1, 2006
At Fern Fort University, we recommend a comprehensive approach to measuring the impact of price promotions on sales, utilizing a combination of econometric modeling and customer behavior analysis. This approach will enable Fern Fort University to optimize its pricing strategy, maximize sales, and improve profitability.
2. Background
This case study focuses on Fern Fort University, a private university facing declining enrollment and increasing competition. The university is considering implementing a price promotion strategy to attract new students. The primary challenge is to accurately measure the effectiveness of price promotions and determine if the potential benefits outweigh the costs.
The main protagonists are the university's marketing and finance departments, who are tasked with developing and evaluating the price promotion strategy. They need to understand the impact of price promotions on student enrollment, revenue, and overall profitability.
3. Analysis of the Case Study
To analyze the case study, we can utilize a framework that integrates marketing and financial perspectives. This includes:
a. Marketing Analysis:
- Market Segmentation: Identify target student segments based on demographics, academic interests, and financial situations.
- Consumer Behavior Analysis: Analyze the factors influencing students' decision-making process, including price sensitivity, brand perception, and value proposition.
- Competitive Analysis: Assess the pricing strategies and promotional activities of competing universities.
- Value Proposition Development: Define the unique value proposition offered by Fern Fort University, considering academic quality, student support services, and career prospects.
b. Financial Analysis:
- Pricing Strategy: Analyze the current pricing structure and explore different price promotion models, including discounts, scholarships, and payment plans.
- Cost Analysis: Determine the costs associated with implementing price promotions, including marketing expenses, financial aid programs, and potential revenue loss.
- Profitability Analysis: Calculate the potential impact of price promotions on revenue, expenses, and overall profitability.
- Return on Investment (ROI) Analysis: Evaluate the financial return on investment for different price promotion scenarios.
c. Econometric Modeling:
- Regression Analysis: Develop a regression model to quantify the relationship between price promotions and student enrollment, controlling for other factors influencing enrollment.
- Time Series Analysis: Analyze historical data on enrollment, price promotions, and other relevant variables to identify trends and patterns.
- A/B Testing: Conduct controlled experiments to compare the effectiveness of different price promotion strategies.
4. Recommendations
- Conduct comprehensive market research: Fern Fort University should conduct thorough market research to understand the target student segments, their needs, preferences, and price sensitivity. This research should include surveys, focus groups, and competitive analysis.
- Develop a targeted pricing strategy: Based on the market research, Fern Fort University should develop a targeted pricing strategy that aligns with its value proposition and attracts the desired student segments. This strategy should consider different price points, discounts, and payment options.
- Implement a robust econometric model: To measure the impact of price promotions, Fern Fort University should implement a robust econometric model that accounts for various factors influencing enrollment. This model should be regularly updated and refined based on new data.
- Utilize data-driven decision-making: Fern Fort University should leverage data analytics to track the performance of price promotions, identify trends, and optimize its marketing strategies. This includes analyzing enrollment data, website traffic, and social media engagement.
- Develop a comprehensive marketing campaign: Fern Fort University should develop a comprehensive marketing campaign to promote its price promotions and communicate its value proposition to potential students. This campaign should utilize a mix of traditional and digital marketing channels, including print advertising, social media, email marketing, and search engine optimization.
5. Basis of Recommendations
These recommendations are based on the following considerations:
- Core competencies and consistency with mission: The recommendations align with Fern Fort University's mission to provide quality education while remaining financially sustainable.
- External customers and internal clients: The recommendations address the needs of both prospective students and internal stakeholders like the marketing and finance departments.
- Competitors: The recommendations consider the competitive landscape and aim to differentiate Fern Fort University from its competitors.
- Attractiveness: The recommendations are expected to generate a positive return on investment, improve enrollment numbers, and enhance the university's financial performance.
Assumptions:
- The market research accurately reflects the target student segments and their preferences.
- The econometric model is robust and accurately captures the relationship between price promotions and enrollment.
- The marketing campaign is effective in reaching the target audience and communicating the value proposition.
6. Conclusion
By implementing these recommendations, Fern Fort University can effectively measure the impact of price promotions, optimize its pricing strategy, and achieve its enrollment and financial goals.
7. Discussion
Alternatives:
- No price promotions: This option would maintain the current pricing structure but may lead to continued enrollment decline.
- Broad-based price reductions: This option could attract a wider range of students but may not be as targeted and could negatively impact profitability.
Risks:
- The market research might not accurately reflect the target student segments.
- The econometric model might not be accurate or robust enough to capture the true impact of price promotions.
- The marketing campaign might not be effective in reaching the target audience.
Key Assumptions:
- The market research is accurate and reflects the target student segments.
- The econometric model is robust and accurately captures the relationship between price promotions and enrollment.
- The marketing campaign is effective in reaching the target audience and communicating the value proposition.
8. Next Steps
- Phase 1 (Months 1-3): Conduct market research and develop the econometric model.
- Phase 2 (Months 4-6): Implement the targeted pricing strategy and launch the marketing campaign.
- Phase 3 (Months 7-12): Monitor the performance of price promotions, refine the econometric model, and adjust the marketing campaign based on data analysis.
By following these steps, Fern Fort University can effectively measure the impact of price promotions, optimize its pricing strategy, and achieve its enrollment and financial goals.
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Case Description
One of the key problems in marketing decision making is how to measure the effectiveness of marketing actions, for example, the effect of temporary price promotions on sales. Many outcomes of marketing decisions are multiply determined and involve both short- and long-term effects that are hard to pin down. So marketing managers are often unable to specify the precise effects of what they are doing, and instead rely on intuitive estimates of the effects of their decision making or, worse, on commonsensical arguments to complement ill-conceived cost-plus pricing approaches. Scanner data-based records of customer purchase decisions provide a wealth of data that managers could use, but how can they get useful input for their decision making out of thousands and thousands of purchase transactions?
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