AI in the retail market is expected to reach USD 14.03 billion in 2025, and is projected to grow up to USD 62.64 billion by the end of 2034, if Precedence Research is to be believed. ML in retail demand forecasting is a significant part of this growth, as in a modern competitive market, it becomes more of a necessity than an optional feature. Traditional methods of forecasting in retail can no longer compete with advanced, AI-powered approaches that uncover hidden patterns and provide far more precise insights.
However, implementing machine learning for demand forecasting in a particular organization requires a strategic and professional approach to achieve outstanding results from this technological initiative. Success is based on a combination of high-quality data, the vendor’s deep domain expertise, and continuous monitoring and improvement of ML models. In this article, we will share our insights on using machine learning in retail demand forecasting, with a focus on the latest technological advancements.
The Rising Importance of Retail Demand Forecasting Machine Learning
Several prominent trends are shaping how AI is utilized in retail, putting pressure on retailers to implement proactive demand forecasting solutions in their processes:
- Stockouts and Overstocks Become Pricier: every stockout represents a lost sale opportunity, while overstock ties up operational budget. The consequences of inventory mismanagement are now higher than ever, and the proper implementation of ML models can help mitigate this.
- Customers Expect Instant Availability: nowadays, customers expect 24/7 availability of a desired product, instantly switching to competitors if a certain retail brand fails to deliver an item. Demand forecasting using machine learning allows for more precise inventory decisions.
- Market Conditions Are Highly Volatile: market changes have become common for everything from global supply chain disruptions to shifts in customer expectations. Retail demand forecasting methods using ML models are far more agile compared to traditional methods, as they are able to adjust to changes.
- Data and AI Adoption Is Now the Norm for Retail: Back in 2023, 79% of retailers were actively using AI, according to IBM Institute for Business Value study, as reported by Number Analytics. Now, the AI adoption rate continues to grow steadily in retail, transforming customer service, improving operational efficiency and creating a significant competitive edge. Staying relevant in this innovative environment is becoming a standard for winning organizations.
- Product Lifecycles Become Shorter: trending products come and go lightning-fast in the modern world. Machine learning in retail demand forecasting allows for spotting changing patterns very early, helping to adjust product planning and inventory levels just in time for market shifts.
- Sustainability and Eco-Awareness Pressures: last, but definitely not least, the ecology-related issues become critical. Overstocking leads to hazardous waste, so more intelligent forecasting allows for minimizing the negative impact on the environment.
Statistical Forecasting vs. ML: Why Old Methods Fall Short
For decades, retailers have relied on proven statistical forecasting models, such as exponential smoothing, linear regression, and ARIMA (AutoRegressive Integrated Moving Average). Unfortunately, in a modern, complex, and fast-paced environment, they are not as effective anymore.
First and foremost, statistical models are aimed at linear and straightforward patterns. When there is a demand for addressing complex and non-linear dynamics that define customer behavior and market trends, they fail to be as effective.
Dmytro Tymofiiev
Delivery Manager at SPD Technology
“ML models are a go-to approach for demand forecasting, as they are not limited in the number of variables and can easily scale across thousands of items to detect intricate, non-linear relationships. Retail demand forecasting machine learning solutions work with massive amounts of real-time and historical data, can self-improve without constant manual intervention, and easily incorporate additional variables to sharpen accuracy.”
Traditional forecasting methods are limited to managing a certain number of variables while remaining stable. Retail demand forecasting requires an analysis of dozens, sometimes even hundreds, of unrelated factors like weather, social trends, or economic indicators, so having limitations in possible variables is a significant obstacle.
Finally, with traditional methods, every product, sales channel, or region, more often than not, requires a separate adjustment. As a business scales with thousands of items across multiple stores, this becomes a massive burden.
Manual Methods vs. Machine Learning in Retail Demand Forecasting
Key Data Inputs to Get Started with ML-Based Demand Forecasting
A truly efficient retail demand forecasting machine learning solution is built on deep, high-quality, and diverse data. Based on our experience, we can say that you will need a combination of rich internal and external datasets for accurate and adaptive models. For the successful retail demand prediction using machine learning, you will require the following types of data:
Internal Data:
- Sales History: detailed historical information on sales serves as a foundation for retail demand prediction using ML, showing the essential metrics on the performance of the product over time.
- Inventory Levels: having access to both historical and current data on inventory positions allows the model to properly predict constraints, stockouts, and replenishment cycles.
- Pricing and Promotions Data: as an essential part of predictions, any changes in pricing, discounts, and past promotional events must be added to form accurate forecasts.
- Store and Warehouse Locations: another critical piece of information, geographical and logistical factors that allow for determining proximity to customers and regional purchasing activities.
External Data:
- Seasonality and Holidays: ML models should learn information about all public holidays, seasonal events, and local traditions, as they all influence fluctuations in demand.
- Weather Conditions: for categories like food or apparel, weather plays a deciding role in consumer demand, so it’s vital for ML algorithms to have access to information about it.
- Market Trends: retail demand prediction using machine learning must also leverage real-time indicators of current customer preferences, the perception of the brand, and social media, as well as the strategies and pricing of competitors.
- Economic Indicators: another factor that influences the purchasing power and customer behavior, macroeconomic data should also be considered while training ML models.
Dmytro Tymofiiev
Delivery Manager at SPD Technology
“We begin all our AI/ML projects by cleansing, unifying, and optimizing our clients’ data into ML-ready datasets. Whether it is an entirely new project or the modernization of an existing system, preparing the data infrastructure is a critical step in effective retail demand prediction using machine learning.”
How to Make Sense of Retail Data for Demand Forecasting
Converting raw retail data into an actionable foundation for demand forecasting is far more than plugging the correct numbers into the models, as it requires a holistic and structured approach to data management. Here at SPD Technology, we specialize in working with complex data environments, like in our case of global commerce platform development, so here is our guide for managing data for sales-forecasting of retail stores using machine learning techniques.
1. Consolidate Data Across Systems
The first step in this process is to eliminate any data silos by connecting data from all departments into one unified source of truth. It may require collecting information from various systems, including sales, inventory, marketing, CRM, and supply chain management.
It is crucial to capture data from both online channels and physical stores to gain a comprehensive omnichannel view. To achieve this without manual intervention, automated ETL (Extract, Transform, Load) pipelines and data integration tools are used.
2. Clean & Preprocess the Data
Next comes the cleaning and preprocessing of data, which involves correcting missing values, outliers, and duplicates, while standardizing product identifiers, pricing structures, units of measure, currencies, and time zones. It’s equally important to align time-series data properly, choosing daily, weekly, or monthly granularity based on the business context.
At this stage, irrelevant records are filtered out, including employee test transactions, system glitches, to ensure that only meaningful information is fed to ML models.
3. Categorize & Enrich Product Data
To help models accurately detect category-level trends alongside individual product behaviors, it is important to organize SKUs (Stock Keeping Unit) into logical groupings. Furthermore, adding contextual information like historical promotions, store location details, and weather conditions significantly enriches the dataset. It is also important to make personalized recommendations happen by enhancing product and customer profiles with demographic or behavioral attributes.
4. Use Data Visualization to Spot Patterns
Leveraging data visualization, particularly dashboards, helps highlight ongoing trends, seasonal demand fluctuations, promotional impacts, and inventory turnover rates in relation to demand. Visual analytics not only allows for the detection of patterns but also acts as an effective tool for data quality checks.
5. Segment Data for More Accurate Machine Learning in Retail Demand Forecasting
It is a smart move to separate datasets by different factors, including product categories, store locations, sales channels, and customer types. This segmentation enables models to fine-tune predictions for each specific group, significantly enhancing forecasting accuracy across various business areas. Segmentation is used for any ML model, just as we did in our high-load support AI chatbot for an online fashion store project, to derive reliable and high-quality data.
6. Establish a Feedback Loop
Finally, sales-forecasting of retail stores using machine learning techniques will only benefit from setting up a feedback loop that opens opportunities for continuous improvement. Constant evaluation of gaps between predicted and real outcomes helps engineers to adjust data inputs and ML models. There is also the possibility of automating forecast accuracy reporting and flagging consistent discrepancies to maintain the highest level of forecast reliability.
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Overcoming Challenges of Retail Demand Forecasting Machine Learning
Here, at SPD Technology, we have hands-on experience in delivering advanced AI/ML projects, from sophisticated eCommerce fraud detection systems to versatile customer service solutions. Retail demand prediction using machine learning is one of our fortes, and in this section, we will discuss how we overcome the most common challenges in this area.
Cannibalization Effects Between Products
Adding a new product may not directly increase overall sales, as it may lower demand for another popular product. Cannibalization is a blind spot for traditional forecasting methods, especially when multiple products are targeted at the same customer base.
Our ML models can detect and monitor these product relationships thanks to the rigorous analysis of historical patterns, product similarities, and customer purchase behavior. We empower our clients with machine learning in retail demand forecasting, enabling them to anticipate shifts in demand across their entire portfolio and make more informed pricing, promotion, and inventory decisions.
Incorporating Unstructured External Data
While retailers have access to numerous external signals, from social media sentiment to product reviews, most of this data remains unstructured and underutilized.
We are fully aware of this problem and leverage powerful Natural Language Processing (NLP) models to extract and analyze external information. Our solutions leverage both unstructured and structured data sources to analyze ongoing trends that influence customer behavior.
Intermittent Demand & Zero Sales Days
There are certain items, such as luxury or seasonal products, that experience irregular demand or even periods with zero sales. For traditional forecasting methods, these scenarios can be confusing, and they may struggle to predict demand for them accurately.
Our experts employ a range of techniques, including Croston’s method, to develop models that accurately predict slow-moving and low-frequency products.
Price Elasticity & Promotion Sensitivity
Customers are responding to specific events, leading to changes in demand. This response may differ significantly from price drops, bundling, or flash sales, as well as their timing and the market segment in which they occur.
We deliver models that take into account historical promotional performance, as well as dynamic sensitivity to the consequences of specific promotional campaigns. This is achieved through frequent retraining and contextual adjustments, ensuring the highest level of precision.
Returns and Reverse Logistics Impact
According to Statista, clothing, shoes, and accessories are the top three categories of returned products in e-commerce in the United States. This is a common problem for retailers, which can distort demand signals if not appropriately addressed.
We set up our models to differentiate between gross and net sales, adjusting forecasts with historical return patterns in mind. By doing this, we prevent costly overstocking and work with an accurate representation of customer demand.
Supply Chain Lead Time Variability
Unfortunately, even with the most precise demand predictions, suppliers can still fail to deliver goods on time. Factor in shipping delays, supplier issues, and geopolitical disruption, and inventory planning could suffer. According to McKinsey, AI-driven forecasting can reduce supply chain errors by 20% to 50%, resulting in a 65% increase in efficiency through fewer lost sales and fewer unavailable products. Furthermore, according to the same research, integration of AI forecasting engines can save up to 15% of operational costs on automating workforce management tasks.
As experienced ML developers, we are fully aware of this and always integrate supply chain data directly into our models. We help our clients develop robust and responsive planning systems that are prepared for any unforeseen events that may occur.
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Consider SPD Technology as Your Strategic Tech Partner
We, at SPD Technology, have decades of proven experience in various business verticals, including retail and eCommerce development services. Our experts possess a deep understanding of the intricacies of retail operations and the dynamics of the supply chain, enabling them to deliver tangible business value precisely.
Our engineers know how to optimize and unify data from entirely different sources and in various formats, to ensure a strong foundation for ML models to predict personalized demand retail data. Fully understanding the business impact of Big Data, we design future-proofed systems that integrate with cloud platforms, unlocking limitless scalability and impressive flexibility.
With hands-on experience in AI/ML projects, our experts ensure that your custom ML models can learn and adapt easily to changing market conditions and customer behavior. We are committed to the full cycle implementation of machine learning-based demand prediction solutions, covering all aspects of this process, from initial assessment to post-release support.
Take a look at one of our recent projects to get a better understanding of what results we can achieve in the real-world business environment. In this project for an online retail business, we managed to achieve 3x product expansion enhanced by AI/ML:
Conclusion
Demand forecasting is one of the most prominent machine learning use cases in retail. It has already proven its effectiveness and supremacy over traditional methods when done right. As advancements in Artificial Intelligence and machine learning continue at an impressive pace, we can expect the next step in the evolution of retail demand prediction using ML to arrive sooner, rather than later.
Here, at SPD Technology, we have extensive experience in eCommerce, from building marketplaces to providing POS software development services. We always stay ahead of the curve by keeping our finger on the pulse of all innovations that deliver business value to our global customers. Demand forecasting in retail with ML is no exception. Our team specializes in data analytics in retail, delivering a strong foundation for building our advanced demand prediction solutions tailored to the particular needs of each client. Contact us and let’s drive the industry forward with game-changing solutions!
FAQ
- What are the benefits of using ML for demand forecasting in retail?
Retail demand prediction using machine learning offers several tangible business benefits, enabling organizations to identify complex data patterns that are often overlooked by existing methods. This results in more precise predictions, improved inventory management, reduced waste, increased customer satisfaction as related to forecasting experience in retail, and higher revenues.
- What’s the biggest mistake people make when using ML for demand forecasting?
Probably the most harmful suggestion one can make is to think that machine learning in retail demand forecasting can deliver outstanding results, regardless of the quality of data. The truth is that clean and precise data is a foundational element for effective ML models, as the lack of it leads to inaccurate predictions and dangerous business decisions.
- Should I build my own ML forecasting system or use a third-party tool?
It heavily depends on available resources and your business needs. A custom demand planning machine learning solution offers complete control and customization opportunities, but requires significant time and financial investments. Off-the-shelf tools, on the other hand, are faster and cheaper to implement. However, they may lack specific functionality and be hard to scale.