Retail trends move quickly. Between ads, social media influencers, and recommendations—customers encounter many products and reasons to buy them. To keep up with demand, businesses must consider retail demand forecasting. Findings from a Retail Industry Leaders Association survey are telling. 87% of retailers consider demand forecasting a top priority for supply chain efficiency. In other words, you risk stagnation if you don’t already have a demand forecasting method.
Let’s dive into why retail demand forecasting is important and how an order management system can help.
What is retail demand forecasting?
When a business sells a product, it must ensure there is enough inventory to fulfill orders. Retailers dread running out of popular products during busy, high-revenue seasons. As such, you must have the right methods to measure stock levels. However, figuring out how to meet customer demand goes beyond maintaining adequate inventory. Retailers want to consider historical sales data, seasonality, and other factors that impact demand for a product.
Tracking every product in your inventory is challenging, and mistakes happen. The market is always changing. It can be difficult to keep track of market trends and new products from competitors. Yet, tracking products and predicting trends are essential to sales and demand forecasting. These strategies help avoid excess inventory or ineffective resource allocation. It also prevents you from running out of high-selling items early in the season.
To understand the most popular items you sell, know your customer base and current stock. It also helps to know which items you must re-order. Retail demand forecasting makes this process more convenient. It uses machine learning algorithms and similar tools to predict the demand for an item. It also looks at data, including market trends and historical sales data. Other aspects that retail forecasting tracks include:
- Business planning systems
- Inventory levels
- Staffing
- Marketing strategies
Demand forecasting involves a calculation. You determine the number of units of a single product that customers buy within a period. For example, when preparing for winter, a clothing store wants to order stock beforehand. Retail demand forecasting helps predict the number of winter clothes sold during that time. This avoids overstocking and leaving excess inventory that won’t sell. It also ensures ample personnel at each store location during busy seasons. Additionally, it avoids overstaffing stores when business slows.
Let’s not forget warehouses and distribution centers. Retail demand forecasting can also help determine staffing requirements for these locations. Needs may fluctuate at different times of the year. For instance, you’ll likely need all hands on deck during the holiday season.
Beyond staffing, insights from retail demand forecasting can inform more strategic layouts. For instance, teams may consider volume order management. This tactic places top-selling products closer to packing areas, with the least popular items furthest away. It helps increase efficiency in warehouses and distribution centers. It can also make it easier to notice when inventory runs low.
What are the four types of demand forecasting?
What are your goals for retail demand forecasting? The answer helps when calculating your business’s forecasting demand. Different strategies and data sets can produce different results. This means you must establish goals and use data and methods that help align them. The four most common demand forecasting models include:
1. Passive Demand Forecasting
Passive demand forecasting allows you to predict stability. It also measures the sustainability of your business. It uses historical data to predict consumer demand. Historical data includes:
- Attitudinal data: This measures a customer’s attitude toward a product, experience, or brand. It also includes their opinions, thoughts, and feelings.
- Identity data: This shows information about the customer. It can include their name, demographics, contact information, and links to social media pages.
- Behavioral data: This evaluates data from previous retail sales and interactions.
- Descriptive data: This provides customer details, like their education, career, or marital status.
2. Active Demand Forecasting
Unlike passive demand forecasting, active demand forecasting helps with company growth versus stability. Alongside sales data, active demand forecasting considers other market aspects. These include external trends and conditions, marketing campaign data, or competing businesses. Active demand forecasting is ideal for helping you expand. It also works to find new marketing techniques.
3. Short-Term Demand Forecasting
Short-term forecasting involves predicting short-term trends, usually within three months to one year. Using real-time sales data, short-term demand forecasting customizes predictions. This helps meet demand and allows you to reach short-term goals. These goals include preparing for in-demand seasons and establishing ample sales policies. It can also involve increasing sales.
4. Long-Term Demand Forecasting
Long-term demand forecasting helps when measuring predictions over longer periods. It involves predicting trends over one year ahead of the market. It assists in finding production capacity, annual patterns, and sale seasonality. When businesses look at long-term demand forecasting, they can establish long-term decision-making for growth. This includes:
- Enhancing production processes
- Increasing market shares
- Expanding customer bases
- Acquiring other businesses
What are the main challenges in retail demand forecasting?
Retail is a fast-moving market, always offering new products. It also involves changing existing products to meet customer needs further. When you introduce new products, you want to ensure they don’t displace sales of an existing product (cannibalization). Omnichannel sales further complicate retail demand forecasting. You must track sales across shopping platforms, from in-store to online, to keep up with customer demands.
These factors can make retail forecasting difficult. As products keep changing, you must update demand forecasting processes. This ensures demand planning is up to date as changes occur. Other challenges you may face in retail demand forecasting include:
Finding Accurate Historical Data
Advanced analytics cannot predict demand forecasting without accurate data. Companies may not know how to utilize their data properly. Understanding data and completing regular audits can help you store accurate, useful customer information. You should consider seasonal shopping patterns, as some products may spike in sales during certain times of the year.
Consider New Products
As stated before, retail is an ever-changing market. The number and variety of products a business sells are always changing. New and improved products can impact demand. So, you want to include them in forecasting systems for greater accuracy. Consider using pilot studies or proxy data to predict new product demand. This allows you to reduce focus on forecasts for less popular products but keep an eye on them.x
You may face other challenges when crafting your retail demand forecasting strategies. For example, some businesses may rely too much on historical data. Historical data is valuable for some aspects of retail demand forecasting. But you can’t use historical data alone to predict accurate forecasts. Consider future demand and external data, such as current trends in the market, as well.
What are the best practices for improving demand forecasting accuracy in retail?
Accurate retail demand forecasting helps enhance the customer experience. It also helps you maintain the right inventory. It can optimize supply chain management and keep items from going out of stock. The research is telling. Demand forecasting can achieve a 15-20% higher forecast accuracy.
To ensure an accurate demand forecast, think about internal and external factors. These include competitor actions, economic conditions, and market trends. Other tips for improving accuracy in retail demand forecasting include:
Consider Your Customers
Do you understand your customer base? Their behaviors help predict what upcoming products might be popular. Data analysis helps companies find customer information to understand behavior. Key data sources include:
- Social media
- Point-of-sale (POS) systems
- Inventory records
- Online marketplaces
Advanced analytics look at aspects like customer purchase history. It provides insights into the most popular, best-selling products from this data. Another tool for understanding how customers think about your business is social media. See customer reviews and feedback in comments, direct messages (DMs), and tagged posts. Many businesses use this information to enhance their retail demand forecasting strategy.
Create Forecasting Goals
Set clear and measurable goals before establishing a retail demand forecasting strategy. Do you want to improve customer satisfaction? Enhance supply chain planning? Understanding your customer base can guide you in creating these goals. Answering these questions can help streamline a targeted forecasting strategy.
Then, check which data sets will be most beneficial for your forecast. Data options include:
- Order records
- Point-of-sale (POS) systems
- Social media analytics
- Online marketplaces
Update Forecasts Regularly
You can’t rely on the same retail demand forecast for long-term success. You should update your forecasts as trends change throughout the season. Your forecast should consider changing marketing conditions and new data. You should also check your forecast and note changes to ensure accurate information.
Communication
Communication is essential to any business process, including retail demand forecasting. Demand forecasting may seem technical and data-driven. But, communication between departments and colleagues can also help streamline forecasting efforts. This keeps all your teams aligned with your forecasting strategy goals.
Why care about demand forecasting now?
It’s never too late to consider retail demand forecasting, even for established businesses. It provides many advantages, from reducing excess product waste to increasing revenue. A report from McKinsey & Company states that retailers with accurate demand forecasting can reduce lost sales by up to 65%. It also says they can decrease inventory levels by up to 20%. In other words, retail demand forecasting can improve resource allocation and customer satisfaction.
Implementing retail demand forecasting in your strategy can also enhance customer satisfaction. Retail offers consumers many opportunities for shopping. In-store and ecommerce channels continue to be the most popular. But, some social media applications are implementing their own shopping markets. With more sales channels, retailers face even more competition.
If customers are not satisfied with your company, they will likely turn to your competitors. That means you want to provide unique value and meet customers’ needs. This helps ensure they keep coming back. With a demand forecasting solution, you can cater to your customers better.
For instance, the data you gather for demand forecasting helps retailers understand their customers’ motivations for buying. It reveals trends in shoppers’ preferences and behaviors. You can align your strategies around these shifting motivations, meeting customers’ needs before or as preferences and behaviors change. This can give you a competitive advantage even in oversaturated markets.
On the same note, providing better customer experiences can increase cash flow. Demand forecasting helps give customers what they desire and can boost marketing efforts.
Businesses can predict the types of products they might sell during a season. Then, they can tailor their marketing efforts to highlight these items. A successful demand forecasting strategy lets you plan ahead of trends. The result is abundant and easy product selection.
Implementing a retail demand forecast strategy can streamline business processes. For example, it allows you to predict popular products all year long – not just during busy seasons. This ensures you keep stock at the right levels in-store and at distribution centers. In turn, this streamlines deliveries. This means products reach stores or customers’ doorsteps more quickly. The in-store and online shopping experience improves.
Why having an order management system is critical to demand forecasting in retail
An order management system is a vital tool. Order management systems (OMS) tell businesses which products they have in stock. They also reveal their location within a store, warehouse, or other location. An OMS also tracks returns and new products as they move in and out of stock. This helps your businesses remain organized and updated about stock levels. It also provides you with an accurate view of inventory across all channels and locations within your business.
Accurate demand forecasting is essential to running a smooth business. If you have too little stock, you can lose out on sales and leave customers wanting more. However, too much stock can lead to wasted product and loss of revenue. You must order the right amount of product, hitting a sweet spot for your customers. Now, you may be wondering, “What are my solutions?”
One solution is having an advanced, agile order management system. A modern OMS will offer you an accurate, single view of inventory to avoid out-of-stocks and canceled orders. They help the business understand inventory across all locations. This includes what’s in stock and what items are being processed for orders. What’s more, order management systems ensure consistency of inventory data across all systems, such as your ERP, POS, and WMS systems.
Knowledge of inventory is a critical component of retail demand forecasting. Achieve consistent monitoring of stock levels by implementing an order management system.
Organizing business processes with technology from Fluent Commerce
Retail sales forecasting begins with demand forecasting. It helps you understand what impacts demands and put strategies in place to meet them. With the right approach to demand forecasting, your business can increase customer satisfaction. It may also help boost profitability.
Businesses rely on Fluent Commerce to improve and prioritize their business processes. Fluent Order Management is a flexible, agile order management system designed to meet your business needs. This ensures profitable order fulfillment and positive customer satisfaction. Learn more about retail demand forecasting with order management systems. Request a demo today.
