Product performance forecasting predicts future sales volumes—at what price, during which time, and in which market. In effect, it enables businesses to make informed decisions in both the short term and the long term.
Nowadays, companies rely on machine learning software for product performance forecasting as these programs continuously improve, providing businesses with faster and more reliable estimates all the time.
A good machine learning (ML) model provides a more accurate forecast through a number of different means. It 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 improves the following forecasting methods:
Product performance forecasting relies on good quality data, so companies require a wide range of dependable data. 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.
Essential external data for a good forecasting model includes both factors specific to the company itself and larger trends. Local, microeconomic indicators may include customer shipping receipts, click streams, and local news or weather reports. Larger, macroeconomic indicators, on the other hand, may include market trends, census or demographic trends, or global supply chain data.
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 proves itself 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. Even experienced data scientists find it difficult to determine the exact influence these labile outside factors have on historical sales patterns, much less 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.
Included as part of a software service, Edge by Ascential’s Retail Insight tracks retail data on thousands of retailers in nearly 200 markets to help you analyze your competition, measure your customers’ engagement, track market trends, and predict product performance.
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