How To Improve Retail Demand Forecasting with Machine Learning

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So your retail business is growing and, naturally, generating enormous amounts of data. The data that could be used as a valuable source of insights. Yet, outdated systems often can’t process it effectively and correctly, which may lead to inaccurate demand forecasts, over-stockings, lost sales, and missed opportunities. In addition, dozens of internal and external factors continuously impact the demand, making it even more challenging to make predictions.

Fortunately, digital transformation made its way into retail, offering multiple ways to make data work for you. One of them is using machine learning for retail demand forecasting. Powered by pure logic and the ability to quickly process terabytes of data, ML can help you tame unpredictability. 

But is it better than traditional statistic forecasting? And can it completely substitute human experts? Read on to find the answers.

Machine learning makes a difference in the retail industry

Analysts notice a rapid growth of artificial intelligence (AI) usage in the retail market, estimating it to reach $19.9 billion by 2027. Improved operational efficiency and enhanced customer interactions are among the leading aspects propelling its growth.

The application of artificial intelligence in retail goes beyond automating processes like customer service, providing recommendations, and creating personalized shopping experiences. Together with its subset, machine learning, AI can assist retailers in supply chain optimization, price optimization, and market forecasting.

Retail companies have tons of sales data, and its amount is constantly growing. Well, machine learning can determine patterns within that haystack of data. So it’s not surprising that Gartner claims demand forecasting to be the number one use case for AI in retail. 

Powered by complex mathematical algorithms, ML can also:

  • Spot relationships between your business decisions, consumption trends, and external factors
  • Capture demand signals and identify products that are getting good customer feedback

No human can handle such amounts of data, but ML can easily cope with these tasks and even predict unexpected demand spikes. That, in turn, helps companies make more reliable retail forecasts, optimize inventory, improve margins, and address market volatility—the central pain point for business executives. 

If you’re eager to discover more about the practical use cases for ML in retail, read our other article here. 

Traditional statistical forecasting vs. machine learning for demand forecasting

For the last several decades, retail companies have been using statistical forecasting to make predictions by looking at historical data. The most popular predictive models include moving average, exponential smoothing, and autoregressive integrated moving average (ARIMA), which give pretty accurate predictions under stable conditions. 

However, being linear by nature, traditional models can’t see subtle patterns in data. They also fall short when uncertainties occur (e.g., random fluctuations, external factors like changes in a store’s neighborhood, or weather). You can add other limitations to the list:

  • Inability to predict a sales turning point in the data
  • Relying on human judgment (often resulting in extrapolating recent trends)

ML offers more flexibility and is free from a priori assumptions. Its forecasting algorithms often use nonlinear techniques that involve more complex features and predictive methods. Compared to traditional methods, using ML allows you to:

  • Accelerate data processing speed
  • Automate forecast updates using recent data
  • Identify hidden patterns in data
  • Increase adaptability to changes

Recent studies prove that ML in retail demand forecasting is superior to traditional methods, minimizing the empirical risk for a loss function—a measurement of how good your model is at predicting the expected outcome. But you need sufficient processing power and reliable data to take advantage of machine learning forecasting. Otherwise, the system won’t be able to generate valuable predictions.

Employing machine learning for retail demand forecasting comes with some twists and turns. If you’re already used to traditional forecasting but want to explore the benefits of ML, here are some tips for embarking on this journey.

How to finetune demand forecasting with ML

Every four out of five supply chain leaders already use or expect to use AI and ML for demand, sales, and operations planning. Yet, adopting machine learning for retail demand forecasting isn’t a cakewalk.

Here’s how you can introduce ML forecasting to enhance your supply chain and planning operations.

  • As an in-build demand forecasting module. The easiest way to reap the benefits of ML predictions is to employ an enterprise resource planning (ERP) or a warehouse management system (WMS) with an in-build demand forecasting module. It will eliminate the need to build internal integrations. Yet, it may offer limited functionality and be hard to customize.
  • As a separate tool. If you aren’t ready to shift to a new ERP/WMS, you can try to use a specialized solution. It allows you to choose the features you need and comes at a more affordable price (compared to ERP). The biggest challenge can be integrating it into your system.
  • As a custom solution. This option is perfect for companies with unique business needs that want their demand forecasting system tailored to their processes. Autonomy, customization, and accuracy are among the benefits this approach can offer. Yet, building such a comprehensive system will require time, a hefty budget, and highly qualified personnel.

Each option requires a clear strategy and understanding of what you want to achieve in short and long-term perspectives. But the game is worth the candle. Here’s why.

Benefits of ML-Enhanced Forecasting for Retailers

Adopting ML for retail demand forecasting can help companies reduce uncertainty by predicting market demand, capacity needs, and the revenue they’ll generate. More opportunities below. 

A better understanding of consumer demand

Machine learning algorithms can help you understand consumer demand more deeply. This is done by analyzing hundreds of potential market influencing factors such as competitor activities, seasonality, trends, promotions, weather, local events, and macroeconomic elements. This data helps you avoid bottlenecks due to unforeseen demand and reduce overprovisioning and food spoilage.

Prediction of inventory needs

Historical consumer behavior data, day of the week, season, and nearby events can come in handy for ML algorithms to predict inventory needs in real-time. The system can then generate a suggested orders list and send it to a purchasing manager.

Pricing optimization

Properly set pricing is a critical aspect of determining profitability. Machine learning can offer an optimal product price based on examining the correlation between market demand and offer, supply, seasonality, and external events related to the business (e.g., holidays or local events). ML algorithms also consider key pricing variables and past experiences to define an automatic pricing strategy with dynamic, real-time prices.

Customer behavior prediction

ML algorithms can predict how buyers will behave in the future by using large volumes of previous data (purchasing history, buying trends, domain-specific knowledge). You can use this data to plan inventory, marketing campaigns, and sales.

Costs and carbon footprint reduction

Using machine learning for retail demand forecasting can help companies minimize the chances of producing excess inventory and avoid allocating unnecessary resources or wasting perishable goods. This, in turn, helps to reduce the carbon footprint and production costs.

Marketing campaign optimization

Machine learning models can help you with customer segmentation based on their buying history. You can use this information for personalized marketing campaigns that increase loyalty and retention. The data is also helpful for optimizing recommendations.

Now that you know how machine learning can help you forecast retail demand better, let’s look at the challenges that may hinder its adoption.

Challenges of using machine learning for retail demand forecasting

If you’re serious about implementing machine learning into your retail business processes, be prepared to invest time and resources to make ML algorithms work properly. Also, consider the following roadblocks if you want everything to run smoothly.

  • Lack of sufficient data. Machine learning algorithms need massive amounts of data to spot patterns, train, and provide accurate results. If training takes place in a dataset that’s too limited, it can make and generalize biased predictions. So, if you’re just at the beginning of your business journey or lack historical data, implementing ML algorithms for retail demand forecasting may not provide reliable results.  
  • Time needed. Machine learning algorithms require time to train and learn to function efficiently. The more data it has to process, the more time this will take. 
  • Wrong objectives and wrong metrics. Performance metrics are a part of every machine learning pipeline. They help you judge the performance of a model (during training and testing). Setting wrong objectives or using improper metrics can lead to false results, which may negatively impact your business.
  • Data ordering and data cleaning issues. Precise forecasting with ML is impossible without accurate data sources. So before feeding data to the system, you should perform data cleaning, i.e., identify and correct errors in the dataset.
  • Lack of qualified professionals. Though machine learning algorithms learn from data without much programming, their development, configuration, and training require highly qualified experts. Given the booming market demand for machine learning, finding skilled professionals can be challenging.
  • High testing infrastructure requirements. Machine learning infrastructure includes the resources, processes, and tools needed to develop, train, and operate machine learning models. So, if you train your models on other businesses’ or publicly open datasets, make sure you have laid out a proper infrastructure to test the results.

But don’t worry. Partnering with a reliable company like Unicsoft can help you address all these challenges and minimize roadblocks and bottlenecks along the way.

Do you want to launch your demand forecasting solution?

Approaching ML in Demand Forecasting the Right Way

Implementing ML-based demand forecasting provides a solid foundation for addressing a wide array of typical planning challenges like inventory optimization, price setting, and effective goods handling in stores. Yet, applying ML algorithms can’t solve all your forecasting problems overnight. It requires a systematic approach. 

Here are some tips and best practices to help you correctly implement ML in retail demand forecasting.

Understand your data sets

The first thing any company should do is to get to know the data it has. Ordering, structuring, and ensuring data consistency is a must to achieve candid results. Using external data could augment the results, but make sure it meets your requirements.

Mind external factors

External factors like weather, macroeconomic trends, economic shifts, and competitors’ activities may significantly impact your business. So, in addition to feeding your ML algorithms with internal data, it’s necessary to employ external data sources relevant to your business needs.

Leave some data aside

Validating how well your machine learning algorithms work is necessary to check how well they perform. So here’s a tip: don’t feed it with all your data at once. Keep a few months of demand aside from the dataset.

Leverage human expertise

Machine learning algorithms might draw conclusions without external help, but  it’s up to live experts to analyze the output and decide which prediction may have the most impact on your business. 

Finding a suitable technological approach to copious amounts of data might be an arduous task. Still, it’s a worthy investment to avoid unexpected inventory storage costs, stock-outs, and dissatisfied customers.


Accurate demand forecasting is a combination of anticipating customers’ needs, preferences, and purchasing intent with factors like seasonality, competitive situation, local events, and even weather. Machine learning algorithms can spot correlations between all those factors, building patterns and anticipating the demand so you can deliver the right products in the right volumes to the right locations.

But achieving forecasting accuracy is impossible without reliable data, clear goals, and data science experts. They lay the foundation and choose ML models that will help you achieve the best results. 

Contact Unicsoft, and our machine learning experts will help you improve your competitive advantage with ML-powered demand forecasting.