Simon Kucher Case: Unlocking Revenue at AlpenGlide

The candidate proposes their approach:

1. Understand the underlying causes of the revenue stagnation (external vs. internal factors)
2. Conduct a detailed analysis of AlpenGlide’s revenue streams to identify underperforming areas
3. Identify and evaluate growth options
4. Present actionable recommendations and outline next steps

AlpenGlide approves Simon-Kucher’s approach and asks the project team to begin.

• Average snowfall has remained stable in the region over recent years.
• Touristic inflow during the winter season has been stable across regional ski resorts.

• The candidate should request benchmarking data for similar ski resorts / competitors in the region to assess AlpenGlide’s revenue performance relative to the market.
• The candidate’s first finding (1) is that until recently, AlpenGlide and its competitors have experienced similar modest revenue growth of 0-1% annually. However, over the past two years, competing resorts have accelerated revenue growth to approximately 6% annually, whereas AlpenGlide’s growth rate has remained unchanged.
• The candidate should request visitor volume data to verify whether traffic has changed drastically.

Conclusion: Since the touristic traffic and snowfall have remained stable, and competing resorts have recently achieved significantly higher revenue growth in recent years, the revenue stagnation of AlpenGlide appears to stem from internal factors – likely related to pricing. The candidate should now proceed to analyze AlpenGlide’s internal revenue streams to determine which areas are underperforming and assess where revenue optimization is possible.

• Total number of visitors on the slopes during the 2024 winter season: 2 million

• The candidate should ask for revenue breakdown by business line.
• The candidate second's finding (2) is that ski lift tickets constitute the largest share of total revenue in 2024, although the exact figure is not disclosed. Other revenue streams, such as equipment rental, ski school and restauration account for €46 M. The candidate chooses to focus on further investigating the ski lift ticket segment.
• The candidate should ask for pricing information on ski lift tickets / passes and calculate the current revenue of AlpenGlide.

• The candidate should suggest different revenue growth options. Potential ideas may include raising prices – while acknowledging the risk of losing market share and shifting market positioning – implementing a dynamic pricing model based on customer willingness-to-pay and perceived value, increasing volume through promotions and partnerships, expanding other revenue streams (e.g. equipment rental and ski school lesson).
• The candidate's third finding (3) is that two daily rate options have been included. The candidate should elaborate on the different key variables that could influence price adjustments (e.g. weather / snowing conditions, weekdays vs. weekends, peaks vs. high vs. off-seasons, holidays, etc.).
• The candidate should calculate the new daily rate revenue of AlpenGlide.

Conclusion: While dynamic pricing boosts revenue by 3%, it falls short of the targeted 5%.

Recommendations and next steps:
The stagnation of AlpenGlide’s revenue compared to competitors primarily stems from underoptimized pricing model on ski lift tickets. Transitioning to a dynamic pricing model, responsive to weather conditions, shows promise by increasing daily ticket revenue by 6%, lifting total revenue by 3%. Despite the positive impact, this approach alone will not fully achieve the targeted 5% growth.

Additional growth levers – such as adjusting the dynamic pricing levers and levels, optimizing pass-holder pricing, expanding ancillary services, or enhancing customer segmentation  – should be explored to close the remaining gap.

Risks:

• Customer acceptance: Price adjustments, especially dynamic pricing, risk negative customer reactions and potential brand erosion. This risk here is nevertheless minimize since the key competitors of AlpenGlide already use this pricing model.
• Revenue volatility: In season with fewer goo weather days, the dynamic pricing model could result in lower-than-expected revenues, increasing year-on-year revenue fluctuation and risk exposure.