Accurately predicting what products customers will buy, in what quantities, and at what time is a key part of running a successful retail business.
It’s how you avoid stocking too little and therefore risking losing sales and disappointing shoppers, or stocking too much, which ties up capital and storage space. It also helps minimize challenges like shrinkage in retail store operations, where inaccurate forecasting can lead to excess inventory, theft, or misplaced stock.
However, many retailers still struggle to fully understand what demand forecasting entails, confusing it with related concepts like demand planning. Grasping the retail demand forecasting process requires an understanding of multiple interconnected components.
Below, we’ll demystify retail demand forecasting, explore the most widely used approaches by retailers, and share best practices for improving forecasting accuracy.
Retail demand forecasting is the practice of predicting how much of a product customers will purchase over a specific period. This is typically done by analyzing past sales, current market trends, and other external factors like economic conditions or seasonal shifts. The primary goal is to create a realistic view of future demand so that you, as a retailer, can make informed decisions about purchasing, stocking, and distribution.
As mentioned, demand forecasting is sometimes confused with related practices such as demand planning and inventory forecasting. However, the three are not the same.
Demand forecasting, as noted, predicts what customers will buy. Demand planning takes those predictions a step further, using them to shape broader business strategies around inventory, staffing, and logistics. Inventory forecasting, meanwhile, drills down further by determining the exact timing and quantity of orders needed to meet expected demand. Across these categories, retailers must choose the best forecasting methods to optimize their inventory and bolster sales outcomes.
Retailers typically use four main approaches to forecast demand: qualitative, quantitative, hybrid, and advanced demand forecasting methods (powered by tools and technologies such as machine learning and AI). Here’s how each works:
Qualitative forecasting is a method of predicting demand that primarily relies on human judgment rather than numerical data. Some retailers use this approach when historical sales data is limited, such as for new products, when entering new markets, or responding to rapidly changing consumer trends. Qualitative forecasting draws on human expertise and insights to predict potential demand.
Common qualitative forecasting techniques include expert opinions, market research, focus groups, the Delphi method (structured rounds of expert surveys to reach consensus), and scenario planning.
As an example, a retailer launching a new fashion line might conduct surveys and consult industry experts to estimate consumer interest and anticipated sales. Similarly, a retailer launching a new line of smart home devices may conduct customer focus groups and consult industry specialists to anticipate initial demand.
The key advantage of qualitative forecasting is its flexibility and ability to capture insights that historical data cannot provide, particularly in uncertain or innovative contexts. The downsides? It’s inherently subjective, prone to bias, and less precise than data-driven methods. For these reasons, qualitative forecasts are often used in combination with quantitative models to improve reliability: many companies opt for hybrid models to balance qualitative insights with numerical data.
Quantitative forecasting is primarily data-driven. It uses historical sales data and statistical models to predict future demand. It’s most effective when there is a sufficient history of sales data and when patterns such as trends, cycles, or seasonality can be observed. The approach assumes that these past patterns will continue, which then allows retailers to model demand mathematically.
Common techniques are time series forecasting (ARIMA, Holt-Winters), causal or regression models that link demand to external factors (like promotions or economic indicators), and moving averages.
Quantitative forecasting is generally more objective and precise than qualitative methods, especially for established products with reliable historical data. Its primary limitation is that it depends on the quality and completeness of historical data. Without a sales history to reference, it cannot easily account for unexpected events, sudden market shifts, or entirely new products.
Hybrid forecasting combines qualitative and quantitative methods. Retailers may use historical data as a baseline, adjust predictions with expert insights, and refine results with statistical models.
For instance, a clothing retailer might forecast next season’s demand using historical sales data, then adjust predictions based on expert opinions about upcoming fashion trends, consumer sentiment surveys, or anticipated marketing campaigns.
Hybrid approaches are particularly effective when dealing with products that have partial historical data or when the market is changing rapidly. Some of the advantages of this method are flexibility and improved accuracy. It mitigates the weaknesses of relying solely on human judgment or historical data.
The main challenge is the complexity of integrating multiple methods and ensuring consistent interpretation of combined forecasts.
Advanced demand forecasting generally means using sophisticated tools and technologies like AI and machine learning to predict customer demand. These tools bring greater scalability and adaptability compared with traditional forecasting methods.
AI and machine learning algorithms can analyze massive amounts of data from multiple sources simultaneously to identify patterns and make dynamic predictions. They can uncover complex relationships in the data that humans or traditional statistical models might overlook.
In terms of scalability, they can handle large product assortments and complex supply chains without significant increases in manual effort. This makes them especially valuable for retailers with omnichannel operations or thousands of SKUs. For example, a large online retailer could use AI to forecast demand for thousands of individual products across multiple regions, accounting for a wide range of factors including local trends, seasonality, and real-time promotional effects.
Also, unlike traditional methods that may rely on static historical data, advanced systems can update forecasts in real-time as new data comes in. This allows retailers to respond quickly to sudden changes in demand or market conditions. Furthermore, machine learning models improve over time as they process more data, meaning forecasts become more reliable with each cycle.
However, implementing advanced forecasting requires significant technical expertise, high-quality data, and substantial computational resources.
Despite these challenges, many retailers, including small and mid-sized businesses, are increasingly adopting AI-driven forecasting, recognizing that the long-term benefits often outweigh the upfront costs.
Conducting regular research and updates on demand planning helps maintain forecast accuracy. To improve forecast accuracy, companies may broaden data sources, adopt digital tools, and more.
One important area where demand forecasting has expanded is e-commerce returns management. By accurately predicting return rates and reasons for returns, retailers can better prepare inventory strategies, reduce waste, and create smoother reverse logistics processes.
The more diverse and comprehensive the data included in your forecast models, the more accurate your predictions are likely to be. Don’t rely solely on historical sales. Include other internal data such as promotional schedules, marketing campaigns, and pricing changes, as well as external factors like weather patterns, local events, economic indicators, and competitor activities.
Incorporating these varied data points creates a more complete picture of the factors influencing customer behavior, leading to increasingly reliable forecasts.
Poor data quality will negatively affect the accuracy of your forecasts. Establish consistent processes for data entry and management. Regularly clean your historical sales data to remove outliers or errors that could skew results. Ensure product identifiers like SKUs are consistent across all systems to allow for accurate tracking and analysis over time.
Thanks to technological advancements, retailers now have access to digital tools that make demand forecasting seamless. These tools are available as standalone demand forecasting solutions or as part of a larger inventory or business management software. They are built to automate and streamline the entire process, from data collection to final output. Such tools process data with greater speed and accuracy, often using the advanced techniques we mentioned earlier, such as AI and machine learning.
Not all forecasting solutions are created equal, however, so do your due diligence when assessing options. More specifically, consider whether they offer the following capabilities or features.
Avoid the pitfall of forecasting at too high a level. Instead of predicting demand for a product category across your entire business, create forecasts for specific products at individual store locations or regional clusters. This approach accounts for local variations in taste, climate, and customer demographics, leading to a much more accurate and actionable plan.
Break down internal silos. Create cross-functional teams that include members from sales, marketing, finance, and supply chain management. These teams can share insights, ensuring the forecast aligns with business objectives. For instance, the marketing team can provide details on upcoming promotions, which can be factored into the forecast to create a more accurate picture.
Markets are dynamic, and a forecast created last quarter may not be relevant today. Establish a routine schedule to compare your forecasts against actual sales results. Analyze the root causes of any significant variances and use what you learn to adjust your models and methods. This cycle of continuous improvement ensures your forecasting process becomes more intelligent and reliable over time.
Improving forecast accuracy isn’t just about tools and data. It also depends on having a skilled team. Provide hands-on training that teaches staff how to analyze historical sales patterns, interpret multiple data sources, identify anomalies or outliers, select and apply appropriate forecasting models, and adjust predictions based on changing trends or external factors. Teach them how to evaluate forecast reliability, interpret statistical confidence levels, and make informed adjustments and other related skills.
A well-trained, knowledgeable team can make better decisions, quickly adjust forecasts when conditions change, and ultimately improve the reliability of your demand predictions.
Predicting customer behavior is difficult, especially with the influence of social media and rapidly changing trends. A product can go viral overnight, causing an unexpected surge in demand that far exceeds what historical data would predict. Conversely, a sudden news event or economic shift can cause demand for certain products to drop unexpectedly. These moments of high volatility are difficult to account for in even the most sophisticated demand forecasting models. Recognizing these factors is the first step towards optimization for better results.
Modern retailers operate across multiple channels, including in-store, online, mobile apps, and third-party marketplaces. Each channel generates its own data, and sometimes in different formats. Integrating this fragmented data into a single, comprehensive view of demand is a significant technical challenge. Without a unified view, a retailer might have a skewed understanding of true demand, leading to inefficient inventory management. Systemizing data collection and analysis is crucial to effective integration.
Events beyond a retailer's control, such as natural disasters, geopolitical tensions, supply chain disruptions, and even competitor actions, can cause sudden and significant changes in demand. These elements highlight the need for companies to plan for scenarios that could impact demand. Furthermore, unforeseen circumstances can quickly render a forecast inaccurate.
Advanced forecasting models require a combination of data science expertise, supply chain knowledge, and business insight. Many organizations struggle to find or develop talent with this specialized skill set.
Without the right expertise, businesses may be unable to fully leverage forecasting tools or interpret model outputs, creating a gap between technological capability and practical execution.
Small retailers operate with tight budgets and limited space, making every inventory decision critical. Demand forecasting offers them a way to compete with larger businesses by making smarter, more efficient choices.
For a small business with limited resources, every square foot of storage space and every hour spent managing stock counts. Demand forecasting provides a clear picture of what inventory you need, allowing you to carry the right amount of products without overstuffing your back room. This precision helps you manage stock with fewer resources and reduces the time your team spends on manual inventory checks.
Retail demand forecasting allows small retailers to anticipate the amount of stock they will need over a given period by analyzing historical sales data, seasonal trends, and market patterns. This ensures that popular products are available when customers want them, reducing the risk of stockouts.
At the same time, it prevents overstocking, which ties up capital in items that may not sell and increases storage costs in stores. According to McKinsey, incorporating an advanced technology like AI into demand forecasting can lower inventory levels by 20-30%.
Perhaps nothing is more disappointing to a customer than not being able to find what they want. Accurate demand forecasting ensures that popular items are consistently available and in stock at your store. This creates a reliable and positive shopping experience, which cultivates customer loyalty and encourages repeat business. Customers are less likely to turn to competitors when they know they can always find their favorite product at your store.
Forecasting also allows you as a retailer to plan marketing campaigns and promotions around expected demand, creating timely and relevant offers that resonate with shoppers. It enables you to respond quickly to changes in consumer preferences, which helps your brand remain competitive and maintain a positive reputation.
Accurate demand forecasting helps small retailers reduce operational costs while maximizing profits. Minimizing excess inventory saves on storage and handling expenses and avoids losses from markdowns needed to clear unsold products. Forecasting also enables strategic pricing decisions, as retailers can adjust prices based on anticipated demand to optimize profit margins and make more efficient use of resources.
Retail demand forecasting provides data-driven insights that support long-term business growth. By understanding which products are likely to perform well, small retailers can make informed decisions about expanding product lines, entering new markets, or optimizing resource allocation.
It also informs marketing strategies, enabling retailers to focus efforts on high-demand products and refine their product assortment by identifying bestsellers and discontinuing slow-moving items.
Accurate demand forecasting allows retailers to limit unsold inventory, particularly perishable goods, which reduces waste. Fewer disposals translate to lower operational costs and a smaller environmental footprint. Sustainable inventory practices not only benefit the planet but also enhance the retailer’s brand image by demonstrating responsibility and efficiency.
Demand forecasting improves the efficiency of a small retailer's entire supply chain. Accurate projections allow suppliers to plan production and deliveries more reliably. This prevents last-minute, expensive rush orders and helps build stronger relationships with vendors.
It also allows retailers to adjust sourcing strategies based on anticipated demand. Furthermore, it enables better logistics and staffing planning. For example, if forecasts indicate an upcoming surge in demand, retailers can ensure they have enough employees on hand.
Accurate demand forecasts are essential for retail businesses. They ensure products are available when customers want them and help them make smarter decisions about inventory. This can lead to higher sales, lower operating expenses, improved customer satisfaction and loyalty, and stronger business performance overall.
Cin7 offers an all-in-one inventory management solution that enables smarter, more reliable demand forecasting. With real-time data integration, AI-powered forecasting models, advanced analytics, and collaborative tools, Cin7 helps you predict demand accurately so you can maximize sales and keep your customers satisfied.
Request a demo of Cin7 today to learn more.