Problem Statement
Quick Quack Car Wash (QQCW) faced significant challenges in revenue forecasting due to:
- High Demand Variability: External factors like holidays, weather conditions (rain, pollution, pollen), and other regional events significantly impact demand.
- Member and Non-Member Forecasting: Predicting both member car washes and non-member walk-ins was a complex task.
- Low Forecasting Accuracy: Traditional time-series models struggled to provide accurate forecasts, hindering resource planning across 200+ stores in the West Coast and Central U.S.
Solution Implementation
To address these challenges, QQCW partnered with Polestar to implement an advanced forecasting solution leveraging Anaplan Plan IQ and AccuWeather data.
Key Steps:
- Data Integration:
- Polestar's Wormhole integration tool was utilized to seamlessly connect Anaplan Plan IQ with AccuWeather's API.
- Historical and forecasted weather data at the zip code level was extracted and enriched with over 80 data variables.
- Feature Engineering:
- Polestar's data scientists identified 12 key weather-related factors that significantly impact QQCW's revenue.
- Model Development:
- Advanced machine learning models were deployed within Anaplan Plan IQ to predict both member and non-member car washes, as well as associated revenue.
- Forecasting:
- The model generates accurate 90-day forward-looking revenue forecasts, enabling QQCW to optimize resource allocation and operational planning.
Technology and Benefits
By adopting this innovative approach, QQCW has:
- Enhanced Data Precision: Leveraged real-time, location-specific weather data from AccuWeather to improve forecasting accuracy.
- Improved Cross-Functional Collaboration: Aligned data science insights with sales and operations teams to drive better decision-making.
- Optimized Resource Allocation: Enabled efficient resource planning by providing reliable, data-driven forecasts.
This solution empowers QQCW to navigate the complexities of the car wash industry and achieve sustainable growth.
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