Sales forecasting is the system by which future sales volumes are estimated—at what price, during which time, and in which market. Product performance forecasting enables businesses to make informed decisions in both the short term and the long term. Machine learning software is continually improved, providing businesses with better analysis all the time—faster and more reliably than manual methods.
A good machine learning (ML) model provides a more accurate forecast, accelerates data processing speed, automates forecast updates based on the recent data, analyzes more data, identifies hidden patterns in data, creates a robust system, and increases adaptability to changes.
Machine learning is geared to improve the following forecasting methods:
Product performance forecasting relies on good quality data, so a wide range of dependable data is curcial. E-commerce sales data, sales transactions, purchase orders, inventory, your own POS information, loyalty cards, customer service, websites, reviews, marketing campaigns, apps, in-store devices, texts, and some CRM data should be used to build a good prediction model.
External data that is essential for a good model includes: weather, customer shipping receipts, macroeconomic indicators, government census, and click streams.
The following external data may prove useful: third party syndicated data, customer POS information, household panel data, geolocation devices, and social media.
Almost all forecast tools rely on historical data to predict future outcomes, so estimating forecasts for new products can be challenging. This problem is particularly evident in industries like consumer electronics, fashion, and books, where new product introductions account for the bulk of sales. To tackle such problems, companies usually resort to historical data of similar products to build a forecast.
Tackling variance in sales volume reveals another challenge: sales revenue is dynamic, influenced by economic, cultural, and legal factors. It is very difficult to determine the exact influence these labile outside influences have on historical sales patterns, and whether they will continue to influence trends.
[Additionally,] at a multinational food company, more than 30% of items are sold on promotion—accounting for nearly 70% of forecast error. The global foods company wanted to predict promotional lift to baseline demand to get timely production and balanced inventory deployment for channel and store supplies. Using machine learning, the company lowered forecast error 20% and lost sales by 30%. It increased service level to 98.6%, and realized a 30% reduction in product obsolescence. It also cut demand planner workload in half, allowing planners to focus on more value-added activities.
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