Predicting Footfall to Boost Retail Sales: A Poisson Regression Strategy

Author: Phil Biju

In this article, we’ll explore a real-world case study on predicting customer footfall using Poisson regression, implemented with Python. The model was designed to help businesses improve decision-making by forecasting foot traffic, which is crucial for retail operations, staffing, and promotions.

This simple yet powerful approach can lead to smarter business strategies by identifying patterns in customer behavior based on time, weather, and promotions. Here’s how we implemented the solution and why it creates significant business value.

Why Footfall Prediction is Important?

• Maximizing Sales Opportunities: Predicting footfall helps businesses plan promotions and staffing during peak hours, directly influencing sales.
• Resource Allocation: Optimizing staff schedules and inventory based on expected footfall reduces waste and enhances customer service.
• Marketing Efficiency: Aligning promotions with footfall trends boosts campaign effectiveness and ROI.
• Data-Driven Decision Making: Accurate predictions empower management to make informed decisions, enhancing overall store performance.

Poisson regression is a great fit for predicting count-based outcomes like foot traffic. It’s tailored to model events (like customers entering a store) that occur within a fixed period, making it an ideal choice when your target is an integer, as it accounts for the randomness in footfall while considering external factors (day, time, promotions, etc.).

def run_poisson_regression(df, target):
    df = pd.get_dummies(df, drop_first=True)  # One-hot encoding
    X = sm.add_constant(df[df.columns.difference([target])])  # Predictors
    y = df[target]  # Target variable
    poisson_model = sm.GLM(y, X, family=sm.families.Poisson())
    return poisson_model.fit()        

This function handles the regression, leveraging StatsModels to fit a Poisson regression model. The results allow us to measure the effect of each factor on footfall, and the model fits well due to the nature of count data.

Predicting with the Model

Once the model is trained, it becomes a powerful tool to predict footfall under various scenarios. By adjusting parameters like day of the week, time of day, weather conditions, and promotions, the business can forecast customer inflow and tailor operations accordingly.

This small snippet shows how businesses can input custom scenarios into the model to predict footfall. For instance, if it’s a sunny Thursday evening and there’s a promotion, the model will predict the expected footfall, allowing for proactive decision-making.

Conclusion

By integrating Poisson regression into their operations, businesses can turn raw data into actionable insights. With this tool users can upload data, view predictions, and adjust parameters in real-time, making it a dynamic tool for decision-makers.

This approach exemplifies how leveraging data-driven models not only optimizes operational efficiency but also drives growth by making smarter, evidence-based business decisions.