Case Study: Optimizing Menu Pricing for a Full-Service Breakfast Restaurant Chain
A full-service breakfast restaurant chain faced a critical challenge: maintaining profitability amid changing customer price sensitivities across its menu. Multiple price increases over the previous couple of years negatively impacted sales volumes across categories, with significant losses observed in the second year due to the compounding effect. Two major menu categories, in particular, proved highly price-sensitive, while smaller categories demonstrated resilience. The company needed a data-driven pricing strategy to ensure sustainable revenue growth while minimizing volume losses.
Approach
The project adopted a comprehensive analytical framework:
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Price Elasticity Analysis
Price Elasticity Analysis
- Conducted a national and store-level elasticity analysis for items covering 97% of sales.
- Evaluated the impact of the last two years of price increases on volume and revenue.
- Identified price-sensitive and non-price-sensitive categories to guide pricing decisions.
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Market Basket Analysis
Market Basket Analysis
- Analyzed product affinities to uncover purchasing patterns and category relationships.
- Assessed “lift” metrics to inform cross-category promotional opportunities, such as bundling or targeted offers.
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Strategic Recommendations
Strategic Recommendations
- Segmented items by price sensitivity and growth trends to prioritize pricing actions.
- Proposed pricing alignment strategies and seasonal adjustments for specific categories.
- Suggested targeted Limited Time Offers (LTOs) based on insights from basket analysis.
Results
The implementation of these strategies yielded significant improvements:
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Optimized Pricing Decisions
Optimized Pricing Decisions
- Categories with low price sensitivity were identified as strong candidates for price increases, used to maintain profitability.
- High price-sensitive items were recommended for stable pricing to prevent volume erosion.
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Revenue Growth with Minimal Volume Loss
Revenue Growth with Minimal Volume Loss
- Non-price-sensitive categories saw controlled price increases, leading to incremental revenue without significant guest count losses.
- Declining categories with high elasticity were excluded from price hikes, stabilizing their sales volumes.
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Enhanced Promotional Strategies
Enhanced Promotional Strategies
- Market basket insights drove innovative LTOs, such as “2-for” offers on highly segmented menu categories (e.g. kids menu).
- Avoided ineffective promotions, like those involving top-selling items, which showed minimal cross-category affinity.
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Data-Driven Decisions
Data-Driven Decisions
Created a repeatable framework for pricing adjustments, ensuring informed decisions that balance revenue and customer retention.
Conclusion
By leveraging advanced analytics, the full-service breakfast restaurant chain transformed its pricing strategy, achieving a more nuanced understanding of customer behavior. The data-driven approach not only improved profitability but also aligned pricing actions with customer preferences, positioning the brand for sustained growth in a competitive market.
