Predicting Holiday Retail Demand with ML/AI

The stakes are high for retailers during the holiday season. According to the National Retail Federation, holiday sales in the United States account for nearly 20% of annual retail sales.

Add changing consumer preferences, omnichannel retailing, and increased competition from new market entrants into the mix makes predicting holiday demand akin to trying to hit a moving target. In response, retailers are increasingly leveraging machine learning (ML)-driven approaches to better predict consumer demand and maximize holiday sales.

What is Machine Learning (ML)?

Machine learning is a subset of artificial intelligence and is a data-driven methodology that uses algorithms to identify patterns and make predictions. These algorithms, which are sets of automated instructions, form part of a model trained on massive amounts of data, continuously learning and improving their performance over time. When applied to a use case such as forecasting holiday retail demand, retailers can analyze historical and forward-looking leading indicator data to generate more accurate demand forecasts.

Overcoming Data Challenges

If good data are fuel for better predictions, machine learning is the engine. Today, retailers’ obstacles to generating more accurate demand forecasts are the diversity of data sources and inconsistent data quality. Trying to manually stitch together data from disparate data sources from disconnected applications in different formats can be tedious and, worse, futile. Machine learning-driven planning software can quickly ingest data from various internal sources like ERP systems and external sources like weather APIs and then algorithmically cleanse, standardize, and categorize the data, making it much easier and faster to glean actionable insights.

Use Cases: Feature Engineering

Feature engineering is one capability of ML-driven planning software that allows users to quickly combine features such as sales volume, weather, and assortment to understand what drives holiday demand. One example could be a retailer wanting to understand what drives the demand for Legos. Much like a person’s weight provides an incomplete picture of a person’s metabolic health (the BMI, an industry standard of weight measurement, also considers height), simply looking at sales volumes would provide an incomplete picture of demand. But if users were to feed a new combination of features like seasonality, sales volume, product location within the store, and the premier of a recent Lego movie into the platform’s machine learning model, the model could generate a much more accurate picture of what drives holiday demand for Legos.

Transforming how Retailers Forecast Demand

Machine learning-driven demand forecasting may become table stakes for retailers looking to maximize holiday sales. With demand becoming more volatile and consumer behavior less predictable, the technology is quickly reshaping how the industry forecasts demand, helping ensure that its customers get the products they want when they want them.