How to Forecast Customer Demand

You can’t change the weather, but knowing which way the wind blows can help you make better decisions that can help your business survive and thrive during weak and more favorable economic conditions. Just like the weather is outside human control, predicting demand for products and services can be precarious.

Just think about the onset of the COVID-19 pandemic and related supply chain disruptions in 2020. Few companies saw that coming, and those that got wind of the large-scale disaster fared better than those that were taken by surprise. It was certainly a cautionary tale about how planning and forecasting can mean the difference between sinking or swimming.

Demand forecasting can shape business and economic growth at scale. Anticipating customer demand is how organizations can find the right balance between supply and demand, while informing inventory management. Overall, demand forecasting can lead to better decision-making.

What is demand forecasting?

Business leaders look at the context of historical data, market trends, and stats to estimate future customer demand for their offerings. After all, stakeholders, especially those in the retail sector, need to anticipate customer demand patterns so they can have the right amount of inventory on hand. They also need to have a reliable workforce and marketing resources on deck to meet or exceed customer needs. Demand forecasting is not a luxury but a necessity to stay in business.

4 Demand Forecasting Benefits

Companies can be prepared by relying on demand forecasting. Here are a few ways it can support smoother operations:

Improved Inventory Management

These insights can help company leaders get in front of potential stockouts and excess inventory. This can help to curb costs and improve customer satisfaction.

Reduced Costs

Demand forecasting helps decision-makers fine-tune production schedules, dedicate resources to the most critical areas, and reduce waste. All of these can positively impact a balance sheet.

Enhanced Customer Satisfaction

Customers want products today, not tomorrow. By shoring up resources, demand forecasting can make for happier and more loyal customers.

Supported Strategic Planning

Accurate demand forecasts can contribute to product development, market growth, and more informed investment decisions.

Effective demand forecasting is more than the sum of its parts. The process is informed by the following elements:

• Historical Data: Analyze past sales data to better predict trends, buying patterns, and seasonal upticks.
• Market Trends: Consider dynamics like economic outlook, competitor activity, and industry trends.
• Statistical Models: Use statistical techniques like time series analysis, regression analysis, and causal modeling to better align supply with demand.
• Qualitative Factors: Incorporate qualitative elements such as market research trends, customer feedback, marketing campaigns, and new product launches.

How To Forecast Demand: A Step-by-Step Guide

Company leaders can employ the principles of demand forecasting to predict customer needs so they can make operational shifts. Having an accurate handle on expected demand can lead to better inventory management, more efficient production planning, and more effective marketing strategies.

Here’s a step-by-step guide on how to approach this process:

1. Identify the Goal(s) of the Forecast

Before digging into the forecasting process, you have to have a firm understanding of your goals. For example, perhaps they’re rooted in matching inventory levels with demand, planning resources more effectively, or pushing out marketing campaigns that hit the right notes at the right time.

2. Determine What Information You Have and What You Can Get

Look at your records and conduct external research to see what insights might be ripe for optimized forecasting. Company leaders will want to look for data analytics related to sales figures, customer demographics, market trends, economic indicators, and any other forces that could shape demand.

3. Set and Execute on a Data Collection Plan

Plot out a data collection plan that accounts for the types of data you need, how to get it, and the general timeline for completion. Above all, the data needs to be accurate, consistent, and relevant to your forecasting goals or it’s a waste of time and energy.

4. Apply Appropriate Forecasting Methods to the Information

After data collection, it’s time to choose a forecasting method that makes the most sense for your operation. Time series analysis is one go-to tactic. In this case, business leaders look at data from past quarters to hone in on patterns and trends. Causal modeling looks at the relationship between demand and how it’s impacted by price, promotions, and the broader economy. Market surveys are a way to glean insights directly from customers and can indicate future demand. Lastly, some business leaders lean on their judgment. Years in the field, not to mention their experiences, can help them anticipate sales activity.

5. Interpret Results in Context and Adjust

Review the forecasted results and approach them through the lens of your overall business goals. You’ll also need to account for seasonal highs and lows, promotions, and economic tailwinds that may affect demand. It’s always a good idea to be nimble and adjust your forecast as new information becomes available.

Other ways to master the art and science of demand forecasting include:

• Incorporating qualitative factors: Study how customer sentiment, new product releases, and marketing campaigns move the needle in terms of demand.
• Using multiple methods: Combine different forecasting methods, so you’re not putting too much stock into one approach that could prove to be flawed.
• Reviewing and updating your forecasts regularly: Adjust forecasts when new information comes to light to ensure they remain accurate.
• Leveraging forecasting software: Test tools and solutions marketed to help make the forecasting process easier. It’s important that advanced analytics are part of the platform.

The steps in this guide can drastically improve your ability to predict future customer demand in e-commerce and make better business decisions that lead to sustainable growth.

Methods of Demand Forecasting

Demand forecasting plays a crucial role in effective business planning and decision-making. This accuracy is how stakeholders can speculate demand, prompting them to ramp up or scale back operations, fine-tune inventory management, and better manage resources.

Demand forecasting has some variability in terms of its execution. Several methods exist, and each one has its own pros and cons. For instance, you might encounter one or more of the following:

Passive Demand Forecasting

Passive demand forecasting leverages historical data and statistical methods to anticipate future sales. It’s most relevant for companies in consistent markets and reliable sales patterns.

Active Demand Forecasting

Market trends, economic forces, and competitor performance can help company leaders predict demand. This method makes the most sense for companies in industries that face frequent changes.

Short-Term Demand Forecasting

As the name suggests, this model looks at the immediate future, weeks or months out, instead of a longer-term projection. This forecasting can be particularly helpful in determining inventory and production activity.

Long-Term Demand Forecasting

This model is concerned with scoping demand fairly far out into the future, particularly years out. Decision makers might find value in using it for big-picture planning and investment decisions.

Demand Forecasting Techniques

Company leaders can approach demand forecasting from a few different angles. Qualitative methods are less concerned with numbers and rely more on intuition and experience. Conversely, quantitative techniques use more objective criteria to come to a determination.

Qualitative Methods

The Delphi Method

This method emphasizes the significance of surveys and feedback from experts to gauge future demand.

Salesforce Composite Forecast

Business leaders can lean on data from multiple sales representatives to form an approximation of demand.

Expert/Insider Knowledge

There’s a lot to be said for tapping into the discretion and experience of industry experts to calculate demand.

Quantitative Methods

Moving Averages

Business leaders can use this one to produce a number that reflects average demand, which might be indicative of future results.

Trend Projection

By calling out trends in sales data, business leaders can wrap their hands around potential future demand.

Seasonal Averages

This method factors in seasonal fluctuations in demand so business leaders can be better prepared to handle seasonal highs and lows.

Effective demand forecasting is the means by which businesses can succeed with operational efficiency, inventory management, and resource allocation. While traditional methods like time series analysis and causal modeling are valuable, advanced tools can significantly enhance the accuracy and efficiency of demand forecasting.

Demand Forecasting Tools

Demand forecasting is both an art and a science. Furthermore, business leaders might be comfortable with managing day-to-day operations but are likely not economists or data scientists. Fortunately, they don’t have to go it alone. Several tools on the market can help them calculate demand so they can focus on what they do best. These three solutions might be worth looking into:

SAP Integrated Business Planning

Business leaders can use this technology stack for tasks like demand planning, supply chain planning, and financial projections. In other words, it’s a great tool to help you understand how all these moving pieces inform each other.

Oracle Demantra

This software solution democratizes forecasting in that it uses statistical methods to predict demand and help business leaders match inventory accordingly.

IBM Planning Analytics

This one is a favorite for demand forecasting, financial planning, and staffing.

Artificial intelligence (AI) has transformed demand forecasting in that business leaders can crunch a lot of data to tease out sophisticated patterns that wouldn’t be possible with conventional methods.

Modern forecasting tools offer the following advantages:

Improved Accuracy

AI algorithms comb historical data and can account for changing market conditions. As a result, business leaders benefit from more accurate forecasts.

Volume

Automated tools can handle a large volume of incoming data, including social media feeds, customer reviews, and consumer sentiment, to generate a more reliable prediction.

Identify Hidden Patterns

One use case for artificial intelligence (AI) is that it can sift through data and uncover patterns to which humans aren’t always privy.

Automate Forecasting Processes

AI tools are useful in that they handle the heavy lifting of demand forecasting. When they don’t have as much on their plates, analysts can invest their time and resources into strategic decision-making that can shape a company’s trajectory.

Demand forecasting is an important endeavor, and it’s one that requires accurate and reliable data for it to be worth a company’s time and resources. Advanced technology like AI can give business leaders a leg up in improving forecasting accuracy. As a result, they’re uniquely positioned to make informed decisions about production, inventory, staffing, and marketing.

Speaking of making AI-assisted decisions, Podium can help business leaders and business owners tap into the goldmine that is customer insights and data. Podium isn’t marketed as a demand forecasting tool. However, it can produce insights that can inform your forecasting models. Here’s how:

Customer Feedback and Sentiment Analysis

Among its many features, Podium’s messaging functionality makes it easy and painless to gather customer feedback and call out trends in sentiment. This data can help you understand customer frustrations, identify trends and opportunities, and anticipate future demand.

Sales Data

Podium offers an intuitive way to track sales inquiries and appointments. These data points can possibly inform your demand forecasts and help you know when to scale up or scale back your workforce.

Customer Location Data

You might operate a business with a sizable geographic territory. Customers in one region might not behave the same way as those thousands of miles away. Looking at customer location information can help you better anticipate geographic demand patterns and balance inventory.

Integration With Other Tools

Podium plays nicely with CRM and marketing tools. Using Podium is a great way to merge data from multiple sources and create reports that provide in-depth details about customer behavior and demand.

In short, Podium can be one tool in your operational toolbox. Many business leaders use it in tandem with other forecasting and market research tools to maximize impact. After all, information is power. Unlocking insights around buying patterns, trends, economic predictors, and other factors can position you for success. Watch a demo and explore the ins and outs of this powerful platform.