How we helped a QSR Brand Improve its Seasonality Prediction through RBF Technique
Encoding Seasonality Accurately In Marketing Mix Models
Objective:
A leading Quick Service Restaurant (QSR) brand approached us with a unique challenge. While the brand experienced a seasonal peak in sales during November and December, largely due to holiday shopping and festive promotions, they also observed a recurring dip in sales during February.
Their main goals were to make the most of the peak sales period to increase revenue and launch new products while also working to reduce the sales drop in February to keep business steady.
Conclusion:
- Our findings demonstrate that RBFs, when properly tuned, provide a more nuanced and robust representation of seasonality, significantly enhancing model performance and interpretability. They are a superior alternative to traditional one-hot encoding for capturing seasonality.
Statistical Impact
80% Reduction In Bias Of The Model
25% Improvement in mape During the seasonality Months.
Business Impact
56% Seasonality accuracy improved For november-december peaks
17% Seasonality accuracy improved For february dips
Download Case Study and Listen to Case Study Podcast
Download The Full Case Study
Case Study podcast
Also Read:
Note : This Podcast is generated through Notebook LM. Curated and Verified by Aryma Labs for Accuracy.
