You don’t have to guess which products will be in demand and when. Demand forecasting can tell you.
This type of inventory forecasting uses data to anticipate consumer behavior around products and services. It’s more sophisticated and reliable than ever, thanks to the rise of artificial intelligence and machine learning.
Here’s how to use demand forecasting to track — and predict — customer behavior.
What is demand forecasting?
Demand forecasting estimates future demand for services or products to help businesses plan inventory accordingly. It informs reordering decisions, helps you adjust stock levels during busy or slow seasons, and minimizes the risk of overordering.
Here’s more about the benefits.
1. Better inventory management
Demand forecasting offers insights about what products are gaining traction with customers, losing popularity, and holding steady. This helps avoid stock outs, which could lead to poor customer relations and low loyalty, or dead stock that costs you time and money. Inventory forecasting is also important for companies that deal with perishable goods, like those in the food and beverage sector, because overstock could cause spoilage.
2. Improved financial health
Accurate demand forecasting also means you only spend on what you know will sell. Strike a better balance between under and over-ordering to optimize your inventory turnover ratio and save money while doing it.
3. Optimized reordering strategies
When you know what products you need and when, create a consistent reordering strategy to always know what to expect. For example, you’ll know to order more every year before the holidays and order less during the new-year lull.
Factors that impact demand forecasting
Forecasting isn’t one and done. You have to revisit and recalculate often because so many factors affect both the market and the perception of your brand. The factors impacting demand forecasting fit into four categories:
- Factors within your control: These variables are easiest to predict because they arise from your own actions. They include pricing, marketing, and the quality of customer service you provide. For example, a huge annual sale could boost demand during that period.
- Customer-specific factors: Customer-driven factors include their preferences, needs, and perception of product and service quality. A competitor might have thousands of loyal customers, but if people think your company has a higher-quality product, they might flock to you instead.
- Macro trends: Macro trends are the largest factors, impacting large populations or even an entire nation. High inflation, global supply chain disruptions, and widespread material shortages are a few examples.
- One-off occurrences: These are difficult-to-predict events that aren’t likely to repeat. For instance, if a celebrity or well-known social media influencer uses your product, it could lead to a temporary surge in sales.
6 types of demand forecasting
The different types of demand forecasting provide a piece of the puzzle, pulling data together to ultimately offer a holistic view of market conditions. Here are the six approaches to know.
1. Passive demand forecasting
The passive demand forecasting model is the simplest. It uses historical sales data to estimate future demand. This is a good framework if your goal is revenue stability rather than rapid growth.
When using this model, make sure to compare apples to apples with sales data. For instance, if you want to predict sales for summer, use figures from the same reporting period in previous years — past summers. Pulling sales data from the whole year or a different season would skew the forecast and likely lead to incorrect estimates.
2. Active demand forecasting
Active demand forecasting is a more dynamic model that accounts for variables like marketing campaigns, market research, and expansion plans. If you run a retail chain and plan on opening a new location next quarter, it’s safe to assume that regional product demand will increase.
The active model also considers outside factors, such as the economic outlook, projected savings from optimizing the supply chain, and growth estimates for the industry. This makes the model particularly useful for businesses in dynamic markets or startups that don’t have much historical data to rely on.
3. Short-term demand forecasting
The short-term demand forecasting model focuses on the upcoming 3–12 months. This model is best for companies that rely on just-in-time (JIT) supply chain management strategies. By looking at short-term demand, you can adjust projections based on real-time sales figures, so there’s time to change inventory management strategies if demand fluctuates mid-year.
4. Long-term demand forecasting
Long-term demand forecasting looks 1–4 years ahead. It requires an abundance of high-quality historical sales data to make accurate projections, making it most valuable for established companies with a consistent customer base.
Think of this model as a roadmap to sales goals. You can use it to set milestones and make sure your business is on track to hit revenue targets over an extended period.
5. External (macro-level) demand forecasting
Macro forecasting incorporates broader economic trends into projections. Factors like market stability, inflation, and the geopolitical climate shape the predictions of a macro forecast. Raw material availability also has a major impact.
6. Internal business forecasting
Increasing customer demand is a major driver of business growth. But to fulfill that high demand, you need the internal capacity to support it.
The internal business forecasting model reveals infrastructure, supply chain, and resource limitations that might hinder company growth, offering the information you need to stay steady while scaling. This model can also identify resource utilization deficiencies that may hold you back.
5 demand forecasting methods
Within the types above, there are a few different techniques to gather and analyze relevant data. Here are a few of the most common demand forecasting models.
1. Trend projection
The trend projection framework uses past sales data to estimate future sales. It’s a simple and easy-to-implement approach, and it’s a key player in passive demand forecasting. That said, it requires you to account for any unusual spikes in sales and adjust the forecast accordingly.
2. Delphi method
The Delphi method is a qualitative approach that uses expert opinions to create a market forecast. Send a questionnaire to demand forecasting experts, summarize their responses, and repeat the process until the group reaches a consensus. It’s a helpful way to get multiple perspectives and a holistic view of future demand.
3. Market research
Market research uses customer surveys to gauge consumer sentiments about your brand, products, and pricing. This method is time-consuming — it involves sending surveys, consolidating data, and analyzing the results. But it provides some of the best insights into the market and general consumer sentiment.
4. Econometric
The econometric method is one of the most number-intensive forecasting strategies. When using this quantitative method, you combine sales data with information about outside forces that influence demand. Then, create a mathematical equation to predict future demand.
5. Sales force composite
The sales force composite approach gathers feedback from your sales team to estimate customer demand. First, gather managers and executives together and have each participant provide insights about market conditions and consumer behavior. They then work together to create a detailed forecast for the business. This is an effective method, but it only works if you have a strong sales team.
How to forecast demand
While your company’s goals may be unique, the basic forecasting process holds true — regardless of which method or type of forecast you want to create. Here are the general steps.
1. Identify your goals
Specify what questions you’re trying to answer with the forecast and decide how you’ll use the insights to inform decision-making. That way, you know what your goals should be. For instance, if you’re considering launching a new product category, forecasting can help you determine whether there’s enough demand to justify such a move.
2. Take stock of your data
Next, identify what data you have available and what information you can reasonably collect. Remember, some forecasting models rely more heavily on quantitative data (hard numbers), whereas others rely on qualitative insights (expert opinions or consumer reviews). It might be tempting to go for an intensive strategy, but a simpler one might be more realistic for your resources.
3. Execute your data collection plan
If you already have the necessary data, this step is quick and easy — simply download the data into a modeling tool or hand it to the person responsible for analysis.
But there’s a good chance you won’t have all the information needed to create a comprehensive model yet. Systematically collect data from your chosen sources until you have enough to plan.
4. Apply your preferred forecasting methods
After gathering the information necessary, generate a forecast. Remember, no one model tells the whole story — no matter how strong it is. Use multiple models and methodologies to gather a more complete view of what the future holds.
5. Interpret the results
Analyze the results of the forecast and put the information into a usable context. Where possible, convert the analytics data into visuals like charts or graphs. Include supporting information to optimize the digestibility and usability of your forecasts, and think about what potential biases or miscalculations could be at play. While forecasts are useful, they’re never perfect, and acknowledging those imperfections prevents hasty decisions.
Supercharge your demand forecasting accuracy with Fishbowl
The success of your demand forecasting efforts hinges on the quality and depth of your data.
With Fishbowl, you can optimize stock levels, align your business strategy with predictive insights, and stay ahead of the competition. It integrates with platforms like QuickBooks to consolidate data and access the full power of demand forecasting. Book a demo today to learn more.