Sales Forecasting for Retailers: A Complete Guide

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🎓 Definition

Sales forecasting is the process of predicting future sales for an existing product or new product. It is an essential tool for Retailers, as it allows them to make informed decisions about inventory management, promotion planning, and pricing.

There are a number of challenges that Retailers face when it comes to sales forecasting. One challenge is the lack of time and expertise. Retailers often do not have the time or resources to collect and analyze the data needed to make accurate forecasts.

🧪 Example of Sales forecast

Let's say a Retailer is planning to launch a new line of coffee mixes. They can use sales forecasting to determine how much product to order and when to launch the product. By forecasting demand, the Retailer can avoid stockouts and overstocks.

The Retailer could also use the forecast to plan promotions for the new coffee mixes. For example, if the forecast shows that demand is likely to be highest during the holiday season, the Retailer could run a promotion during that time to boost sales.

💡Sales Forecast Process

💡Working principle for Sales forecasting

Existing Product

Trend, seasonality, and special events computation: For each product, the sales trend is computed as well as the sales weekly, monthly, and yearly seasonality and the sales response to special events such as national holidays.
Global prediction: The forecast is made using previously fitted components and projecting them into the future. The name of the algorithm used is neuralprophet, a declination of the well-known prophet forecast model (designed and used by Facebook).
Store-per-store prediction: The forecast is multiplied by each store’s share of sales in the item’s category to get a store-per-store prediction.

New Product

Look-alikes for new products: For new products, the same approach is used but using similar product behaviors instead. Similar products are found in the given category, price, brand, and size data.
Forecast for look-alikes: The forecast is then computed for these similar products.
Average: The average from these forecasts is taken as a prediction.

❓The benefits of Sales forecast include:

Optimal strategy planning:

Adapt your inventory management and promotion planning to the specific buying patterns of each product.
Automatically manage the launch of new products.

Improved customer experience:

Avoid out-of-stocks.
Better prices given customer demand for each SKU.
New products to match customers’ demand.

Time-Saving and Efficiency:

Leverage Machine Learning expert algorithm to save 100’s of hours.
Build several scenarios for new products and choose the optimal one.

What are the potential risks or limitations associated with relying solely on sales forecasting for inventory management and promotion planning?

While sales forecasting offers valuable insights for retailers in terms of optimal strategy planning and improved customer experience, relying solely on it for inventory management and promotion planning can present some risks. For instance, unforeseen market fluctuations, changes in consumer behavior, or inaccurate data inputs can lead to inaccurate forecasts. Over-reliance on these forecasts without considering external factors or conducting regular validations can result in stockouts, overstocks, or ineffective promotions.

How frequently should sales forecasts be updated to ensure accuracy, especially for products with high demand variability?

Maintaining the accuracy of sales forecasts is crucial, especially for products with high demand variability due to factors like seasonality or market trends. While the blog post provides details on the sales forecast process for both existing and new products, it doesn't specify the frequency of updates. Generally, it's recommended for retailers to update their sales forecasts regularly, ideally on a weekly or monthly basis, to capture any shifts in demand patterns and ensure the forecasts remain relevant and reliable over time.