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Sales Forecasting Analysis

Introduction to Sales Forecasting

Sales forecasting is an essential practice that helps businesses anticipate their future revenue and make informed decisions around inventory management, resource allocation, and strategic planning. Utilizing historical data, statistical algorithms, and machine learning techniques, sales forecasting provides an evidence-based projection of what a company can expect to sell in a future period. This is vital for optimizing operations and enhancing profitability.

Example Using Sales by Store by Date Dataset

In the following example, we use the dataset named sales_by_store_by_date, derived from the Store Item Demand Forecasting Challenge on Kaggle. This dataset provides comprehensive details about sales figures broken down by individual stores and specific dates. Such granularity allows for precise analysis and more accurate forecasting.

To follow along with the analysis, you can download the corresponding raw dataset here.

SQL-inf Query for Sales Forecasting

Two queries are utilized to extract insights and forecast sales.

  1. The first query creates a view which calculates the average sales per store for each date:

    SELECT 
      date, 
      store, 
      AVG(sales) as ForecastedStoreSales 
    FROM 
      sales_by_store_by_date 
    GROUP BY 
      date, store

  2. The second query employs machine learning to forecast ForecastedStoreSales:

    SELECT * FROM store_sales_forecast_view FORECAST(ForecastedStoreSales, time=date)

Key Insights

  • Seasonal Trends: The data reveals strong seasonal trends both on a weekly and yearly basis, making seasonality a significant factor in sales fluctuations.

  • Upward Trajectory: Over a more extended timeframe, an upward trend is observable, indicating a general growth in sales across the stores.

Strategic Conclusions and Recommendations

  • Capitalizing on Seasonality: Knowing that sales follow seasonal patterns, strategies such as inventory optimization and targeted marketing campaigns can be applied more effectively during peak periods.

  • Long-Term Planning: The upward trend in sales should be taken into account for long-term planning, whether it's expanding store locations or increasing product offerings.

  • Resource Allocation: The forecast data can be used to make informed decisions about resource allocation, helping to match staffing levels and inventory to the predicted demand.

  • Risk Mitigation: The strength and predictability of seasonal trends offer a safety net. During low seasons, resources can be redirected to other revenue-generating areas, thereby mitigating risks.

By applying these insights and strategies, businesses can make more informed decisions, optimize sales opportunities during peak periods, and better prepare for slower sales cycles.