Demand forecasting is often seen a complex task, yet, in a data-driven world it is easier than you think. You may need to know how much revenue and profit your pricing campaign is expected to generate. Likewise, how much your dynamic pricing requires inventory and how often you need to replenish stock. Demand forecasting answers all of these. This is especially important in e-commerce where competitor pricing is fierce.
We’ll discuss all you need to know about demand forecasting and provide you with tips to help with the process. Jump directly to a topic:
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What is demand forecasting?
Demand forecasting is the process of understanding and estimating future customer demand over a specific period. Generally, it will involve the use of historical sales data with seasonal variances and other information to provide the most accurate predictions.
Examples of demand forecasting
To give you a fundamental understanding, think of an e-commerce store that sold 100 winter jackets in November, 150 in December, and 200 in January. As humans, we can assume that there will be a demand for approximately 250 units in February if the market conditions remain the same. But your prediction might be much more accurate when you have historical seasonal data. For example, earlier years might show that February people already focus more on buying spring clothing. Thus, the sales expectation would be 90 units.
A more complex example is a grocery store preparing its inventory for this year’s Thanksgiving week. They are looking at sales data from previous years for seasonal products like turkeys, cranberries, and potatoes. Last year was a good year, generating high sales. But six months ago, another grocery store close by just opened and used dynamic pricing software to respond to our pricing.
In addition, many families have continued to move into the neighborhood, which has increased the store’s average sales increase by 1% month by month. Furthermore, they plan to launch more ads with a higher budget this year. Calculating all these factors (and other relevant ones), the demand forecast has estimated a 5% increase in sales compared to last year.
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Why is demand forecasting important?
There are several reasons why demand forecasting is essential for companies:
- It supports pricing strategies, business planning, goal setting, budgeting, and profit margin estimation. By anticipating future sales, companies can create informed, strategic plans. It also enables more effective production and capacity planning, ensuring that supply aligns with demand.
- It allows companies to optimize inventory, increase turnover rates and reduce warehousing costs.
- It provides valuable insights into future cash flow, enabling more precise budgeting for suppliers and operational expenses. This foresight also facilitates better allocation of resources, allowing companies to invest more in growth opportunities, such as capital expenditures.
- It helps identify and address potential issues within the sales pipeline and supply chain, ensuring smooth operations. For e-commerce companies, this is especially important, as excess cash tied up in stock can hinder operational efficiency.
- By anticipating demand, companies can also better plan for staffing and resource needs, ensuring they are adequately prepared for peak times. This proactive approach keeps operations running smoothly, even during periods of high demand.
Without demand forecasting, businesses risk making poor pricing decisions, leading to higher warehousing costs, decreased customer satisfaction, and inefficiencies in supply chain management. Ultimately, this negatively impacts profitability.
Benefits of demand forecasting
Demand forecasting is vital for e-commerce companies, particularly those using dynamic pricing or promotional campaigns. Understanding demand and its relationship with pricing enables smarter business decisions. Here are some key benefits of demand forecasting:
Identify seasonal trends
Analyzing past monthly sales performance can help identify seasonal fluctuations, such as peak seasons. However, it is equally important to identify the seasons with less customer demand. Lowering demand can be an opportunity to present pricing campaigns or do a markdown pricing strategy to increase customer purchases.
Strategic pricing adjustments
Anticipating demand fluctuations enables timely pricing changes, maximizing profitability without alienating customers.
Manage cash flow
Cash is king. Your capabilities to release cash or invest more in growth will significantly improve when you know how your pricing affects the demand.
Enhance supply chain planning
Accurate forecasts help anticipate demand spikes, ensuring sufficient inventory levels and avoiding rush charges and backorders.
Evaluate external factors
Integrating external data—such as macroeconomic trends—into forecasts enhances agility, enabling businesses to adapt to changing market conditions.
Prepare for the future
Effective demand forecasting equips companies to face uncertainties, from natural disasters to emerging competition, ensuring readiness for various scenarios.
Optimize marketing strategies
Understanding demand patterns allows for targeted marketing campaigns, increasing their effectiveness and ROI.
Why machine learning and AI are shaping the future of product pricing
The most common issues with demand forecasting
To perform effective demand forecasting, you need accurate and robust data. Unfortunately, issues faced by most companies relate to missing data. Here are a few significant problems that can hinder your demand forecasting process:
Lack of historical sales data
Even well-established companies often lack comprehensive sales history. To enhance demand forecasting, gather and organize all past sales data in a user-friendly format.
Poor supply chain management
A well-crafted demand forecast is ineffective if the supply chain is mismanaged. Understanding lead times for sourcing materials, production, and shipping is crucial for addressing any production bottlenecks promptly.
Lack of inventory control
Some people say good inventory management is the foundation of good demand planning. Indeed, if you don’t clearly understand what you have in stock, it might lead to an overestimated or underestimated production plan.
Large SKU assortment
Processing a large SKU assortment using only spreadsheets and manual calculations takes substantial time and resources. Instead, demand forecasting tools use machine learning and artificial intelligence that can automatically calculate numerous amounts of data and variables, which is much more helpful and resource-saving.
What factors influence demand forecasting?
Several factors affect customer demand, and understanding these influences is crucial for accurate forecasting. Here’s a look at how various elements can shape demand:
1. Seasonality of product sales
Seasonality refers to a high variation of product demand throughout the year. A highly seasonal product may serve only for a specific period or event, leading to a high sales volume during this peak period. Outside these peak periods, sales will return to a regular state / steady or slow down.
Some demand forecasting examples based on seasonality are products sold:
- during specific seasons: grilling equipment in the summer and snow sleds in the winter
- during specific events: chocolates on Valentine’s day
- during specific periods: school equipment in August before the start of a new school year
Thus, during the off-season, companies must reduce inventory for seasonal products but increase production during peak seasons.
2. Competition
Competition has a direct impact on demand. As a result, customer demand can either drop or spike whenever competition enters or exits the market.
When a new player enters the scene, an existing player may suffer as customers have more options. But when the opposite happens (when a business closes), existing players may have greater demand from consumers.
3. Type of goods
Different products and services of different natures affect demand forecasting. For example, perishable goods with a short product life cycle/expiration date must have an exact demand forecasting number. Otherwise, a lot of stock is in risk of going to waste. On the other hand, demand forecasting for monthly magazine subscriptions can be less exact.
4. Geography
The geographical location of your customers and where you manufacture, store and deliver orders can significantly impact inventory forecasting, shipping cost, and delivery time. Shipping costs are one of the driving forces that make e-commerce pricing more critical than ever.
So, it is essential to be strategic when choosing geographical locations for your retail supply chain. For example, choosing a warehouse close to where most of your customers reside can help you fulfill your customer orders faster. Also, this comes at a more affordable cost, as you don’t have to store products in a faraway place.
5. Economy
When the economy falls into recession and fewer people work, the demand for luxury products will decline. But, at the same time, demand for affordable products will likely increase.
To sum up
Demand forecasting provides essential information about future customer demand; it assists companies in making more intelligent decisions about production planning, inventory management, and supply chain operations.
It also helps with profit margins, cash flow, capital expenditures, future capacity planning, and more. Demand planning based on sales data, market research, and economic factors will help any business thrive.
FAQ
To collect accurate data for demand forecasting, companies should implement robust data-gathering techniques, such as integrating sales data from multiple sources (offline and online), ensuring data quality through regular audits, and employing advanced analytics tools to identify trends.
Real-time adjustments to forecasts can be achieved by leveraging dynamic pricing strategies, continuously monitoring market conditions, and utilizing software that integrates real-time data inputs.
Recommended tools for effective demand forecasting include specialized software that employs machine learning and AI algorithms for predictive analytics, such as Sniffie. Our cutting-edge price simulator has a 95% forecast accuracy rate.