Machine Learning: A Winning Formula for Sales Forecasting in Retail

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TrueStock Team October 12, 2020

There is no question that demand forecasting is critical to the retail industry.

Retailers need to be able to predict the volume of products or services to be purchased during a defined future period. It is imperative to their business success.

There are a variety of different demand patterns, external factors and even internal business decisions that impact future sales forecasts.

Product demand can be influenced by external factors like weather or holidays and if a product variant or range follows different seasonal patterns.

TrueStock considers the following data and different factors during the Machine Learning (ML) sales forecasts.

Different factors to considered for demand forecasting

Generating highly accurate demand forecasts by analyzing the multitude of demand driving data to make predictions about product requirements based on location, different channels and for the days, weeks, and months ahead is made even easier and faster with Machine Learning.

Seasonality is an important consideration for many retail industries, however business decision such as online flash promotions, adjustments to pricing and even external factors such as the weather or different events can impact the retailers sales forecast.

These different factors result in a plethora of different types of data that needs to be handled carefully to predict demand outcomes for retailers.

Accurate demand forecasts can overcome a range of different pain points such as stock issues (overstock and stockouts), product waste, lack of supply chain resilience and unsatisfactory order fulfilment.

"ML is really a better prediction machine. It uses multiple data sources, and finds the patterns and correlations in that data to improve the accuracy of the prediction. " Gartner (2019)

Using a machine learning data-driven computational approach data from different demand drivers is processed, patterns identified, data is analyzed and with learning over time constantly produces more accurate future forecasts.

Moreover, leveraging machine learning has proven to be a winning formula for reducing forecast errors and closing the gap in achieving greater precision in the sales forecast.


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