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predicting customer demand

Predicting Customer Demand With Machine Learning

Demand is a key indicator of the operational and expansion prospects for retail organisations, and being able to forecast this can be the difference between retailers surviving and thriving in a competitive landscape. The most critical business factors, such as revenue, profit margins, capital expenditure, supply chain management etc., are directly dependent on demand.  

In recent years, retailers have shifted to more data driven approaches and there has been a trend in using machine learning to predict demand through historical data. Determinants include: 

  • Product price 
  • Consumer income 
  • Availability and substitute options 
  • Tastes and preferences 

The equilibrium between demand and supply 

Demand Prediction refers to an evaluation of the products and services that customers will purchase or consume, and the level of engagement they will receive. Correct demand prediction eventually establishes an equilibrium between supply and demand, reducing lead-time in order fulfilment. 

Traditionally, retailers used time-series trend forecasting to predict consumer demand. The time-series trend used to take an inside-out approach and often relied on an organisation’s historical data and patterns to predict demand. While historical data may offer helpful insights towards what the future holds, these metrics failed to incorporate the accelerating change in the external factors continually impacting the market. Traditional forecasting is still considered the most popular approach to predict demand. It considers a single variable and the historical data that influence demand. Such an approach assumes that the historical trends will repeat themselves, however, this assumption is far from being true. This approach often fails to perceive changes in customer preferences or market conditions over a period of time. 

The butterfly effect of customer demand 

Despite retail organisations becoming experienced at predicting demand through historical data, a huge challenge has been achieving this with external factors. An obvious example being the COVID-19 pandemic and the subsequent lockdowns impacting footfall, disposable income and the surge in online traffic. Not only did this affect customer behaviour and demand, it also had an inter-industry butterfly effect throughout supply chains. In the Netherlands, a paper shortage through an increase in home delivery pizza orders led to book production being halted due to supply issues, and in other countries, semiconductor chips were being transferred to video game console and laptop production which resulted in a slow down and supply chain issues in the car manufacturing industry.  

Today it has become all the more important to understand customer preference, buying behaviours and its volatility, and predict demand accurately. Incorrect demand forecasting can result in inefficient supply chain management which then has the knock-on effect of reduced revenue and minimised profit margins.  

Demand forecasting with machine learning 

Over time, retailers have realised that predicting future demand with traditional methods in the face of a shifting market can become a serious barrier to the visibility into what lies ahead. Their aspiration to get predictive insights for better demand forecasting with data and technology gave advent to demand forecasting with machine learning. 

Demand forecasting with machine learning enables retailers to predict consumer demand in a real-world scenario through different platforms, applications, and predictive models. This not only factors historical data, but also macro influences that can have an impact on customer demand further down the line. The algorithms learn rules and identify patterns in demand, considering various other factors also beyond sales. As a result, the forecasts are more accurate and reflective of the real world’s complexity. The effects of AI methods can be seen across the supply chain too. With McKinsey finding that AI forecasting can reduce errors by up to 50% in supply chain networks, this can lead to a host of improvements such as improved transport planning, optimised labour rostering, and the improved ability to negotiate with suppliers. 

Let’s take the example of Walmart, which has been a positive example for retailers. Walmart has been applauded for its willingness to adopt new technology and is betting on its ability to link the online and offline worlds to compete with Amazon. Machine Learning and predictive analytics are at the heart of this drive. Walmart instantly takes sales data from its systems and integrates it in its forecasts to predict the most saleable or outperforming products. Combined with online behaviour patterns, this provides a volume of data points to help Walmart prepare for a rise or fall in product demand.

Nonetheless, the benefits it brings can also be sought by smaller businesses too. As long as external and internal influences are prone to change, there’ll be a need for accurate and dynamic demand forecasting. By incorporating machine learning into your forecasting models, you can reap these benefits for your business and supply chain.  

The value of machine learning can only be truly felt if the information within your forecasts can be interpreted, acted upon and used to make data-driven decisions across your retail business. 

Predict customer demand with Seldon 

Seldon moves machine learning from proof of concept to production, reducing time-to-value so models can get to work up to 85% quicker. In this rapidly changing environment, Seldon gives retailers the edge to autonomously predict, identify and manage trends in customer demand to influence both top and bottom lines, and accelerate business performance. 

Predict and shape customer demand with Seldon by speaking with a member of our team today. 

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