What Is Demand Forecasting and How to Use It in Ecommerce

Running an eCommerce business often comes with uncertainty, so many business owners would love to know what’s in store for them or what is going to be the next big thing. While crystal balls are hard to come by, demand forecasting can provide valuable insights.

In this article, we’ll be exploring just how demand forecasting affects success and how it minimizes risks and maximizes operations. From the forecasting types and methods to software available, you will learn how this process can play a crucial role in eCommerce prosperity.

Although the future may be unclear, forecasting can offer some clarity. Let’s get started.

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What Is Demand Forecasting?

Demand forecasting is the process of using historical data and predictive analytics to estimate future customer demand. Although not a perfect science, forecasting can help companies to allocate resources properly, evaluate market resilience, and strategize for growth.

For eCommerce stores, accurate demand forecasts means optimized inventory levels, production plans, workforce allocation, and more.

How Demand Forecasting Benefits Your eCommerce Business?

Demand forecasting can be super helpful to eCommerce businesses by:

1. Optimizing inventory levels. Knowing what sales volumes to expect for different products lets you make smarter choices about how much inventory to keep. Too much inventory ties up your cash while running low on stock means potentially losing sales. Demand forecasts help you figure out the ideal stocking levels.
2. Setting pricing strategies. Understanding when you’ll see peak demand versus slower sales allows you to adjust prices to maximize revenue. Raising prices when demand is high and lowering them to stimulate sales when forecasts show slower periods can boost profits.
3. Highlighting promotions. Demand forecasting can highlight upcoming periods of weaker demand where promotions could give sales a much needed boost. This allows you to plan your promotions accordingly.
4. Improving supply chain efficiency. Sharing demand forecasts with suppliers helps coordinate deliveries to be on time. Forecasts also enable better production planning and resource allocation.
5. Enhancing (new) product launches. Looking at the demand of related products can help a business owner forecast when to launch a (new) product. This allows right-sized initial inventory orders and optimized marketing spend.

The 6 Demand Forecasting Types

Demand forecasting covers a lot of different approaches, models, and formulas.There are 6 types of forecasting but in reality most real-world forecasting combines elements of each:

Passive Forecasting

With passive forecasting, companies create a “set and forget” system that’s pretty much fully automated. The forecasts use historical data and patterns that have been reliable over time. This works for companies with steady sales in stable markets, but not as well when things start changing quickly.

Active Forecasting

Active forecasting is the opposite – forecasts are customized for each situation instead of using a default automated method. This involves picking statistical techniques and data sources tailored to each forecast. Active forecasting is better for fast-growing companies and dynamic markets where the past doesn’t predict the future.

AI Forecasting

New AI and machine learning forecasting methods are technically active but can automate adaptable active forecasts. It sits somewhere between fully passive and fully active.

Short-Term Forecasting

Short-term forecasts predict demand over shorter periods of time, usually up to a year. The specific time frame varies by company. It may be used to forecast an upcoming holiday season based on sales data from past years. Short-term forecasts can also be pretty accurate.

Long-Term Forecasting

Long-term forecasts look years ahead and won’t be as precise. Any assumptions made may be proven wrong, and unexpected internal decisions will alter outcomes. But long-term forecasts are still useful. They explore possible scenarios and help prepare for different future outcomes.

Internal (Micro-Level) Forecasting

Micro-level forecasting uses company and customer data to predict demand for specific products and services. This includes past sales and sales team projections. Granular forecasts for the company itself.

External (Macro-level) Forecasting

Macro-level forecasting incorporates big picture trends, like industry growth, customer demographics, competition, and the economy. Not every company has all this macro data though. Thankfully there are research groups that publish detailed sector forecasts. Macro forecasts of overall consumer demand can guide expansion plans and new product decisions.

Demand Forecasting Methods

There are many sophisticated forecasting methods out there. The key is picking approaches that fit your business model, types of products, available data, and resources. Often layering a few complementary methods together brings the best results.

The methods that generally work best foreCommerce businesses are:

• Historical sales analysis. Looking at past sales data and trends is a go-to starting point. The goal is to spot patterns to inform forecasts. Works best with consistent sales history.
• Regression analysis. Fancy name, but basically uses statistics to identify variables strongly linked to demand. Things like price, discounts, spending, etc. It quantifies their impact on sales to forecast based on those factors.
• Time series forecasting. Analyzes sales data over time to detect patterns, seasonality, trends, and cycles. Uses historical correlations to make projections. This helps with super short-term forecasts but can be thrown off by major disruptions.
• Predictive analytics. Applies machine learning algorithms to sales data to identify complex patterns not visible to humans. Neural networks continually improve forecasts as more data becomes available.
• Surveys. Asking customers about planned purchases provides direct demand insights. But people don’t always follow through. Short email surveys for instance can offer helpful signals.

Factors That Impact Demand Forecasting

Demand forecasts are only helpful when they align with the realities of a business. The process needs to be dynamic, constantly adapting to changing circumstances.

Here are some of the key factors that impact demand forecasting for eCommerce:

• Seasonality and trends. Seasonal fluctuations and changing trends can significantly impact demand forecasting.
• Customer behavior. Understanding customer preferences, buying habits, and other factors that influence demand can help businesses predict future sales.
• Market size and demographics. The size of the market and its demographics can also impact demand forecasting. For instance, a business targeting a niche market may have different demand patterns than one targeting a broader audience.
• Existing competition. Businesses need to keep an eye on their competitors and their pricing strategies and promotions as well as whatever else affects customer behavior.
• Economic conditions. Economic conditions such as inflation, recession, and unemployment can significantly impact demand forecasting. For instance, during a recession, customers may be more price-sensitive and less likely to purchase luxury goods.
• Product lifecycle. The stage of a product’s lifecycle can also impact its future demand. For instance, products in the introduction or growth stage may have different demand patterns than those in the maturity or decline stage.

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How to Forecast Demand?

Accurate demand forecasting helps companies align their inventory with their marketing operations. Follow these key steps to forecast demand:

Start with Historical Data

Start by digging into at least 2-3 years of past sales data. Identify trends and seasonal peaks for different products and customer segments. Look at how external factors like market growth and competitor actions may have impacted demand. The goal is to uncover any useful patterns that might be used in the future.

Determine Key Variables

Determine specific variables that greatly impact demand, like priceshifts, promotions, holidays, product availability, reviews, etc. Use statistical regression to quantify the relationships between these factors and sales.

Choose Forecasting Methods

Simple time frame forecasts based on sales history alone work adequately for stable, predictable demand. Causal forecasting is better for modeling marketing and promo impacts. Machine learning predictive analytics handle complex nonlinear patterns. Choose the approaches that fit your products and its data best.

Create Scenarios

Create different scenarios to stress test the demand forecast. Vary the assumptions around the economy, competition, budgets, supply chains, new products, etc. When you are finished, compare the results to see what works best.

Get More Granular

To get a clearer picture of the demand for different products, customer types, purchase frequency, demographics, and geographic regions, we need to analyze the data in more detail. By breaking down the data into smaller segments, you can improve the accuracy of our forecasts and make the information more useful.

Regularly Review Accuracy

Forecast errors are inevitable. However it is a good idea to keep track of them so that you can improve your forecasting approaches or avoid what doesn’t work. Review techniques, data sources, variables, and assumptions.

Course Correct Forecasts

Tweak forecasts based on new promotions, events, economic changes,or unforeseen competitor actions. Continual adjustments, however minor they may be, keep forecasts realistic.

The combination of leveraging sales history, modeling key variables, employing advanced analytics, and continuous review helps eCommerce businesses predict demand with reasonable accuracy to drive profitability.

Demand Forecasting Software for eCommerce

Just knowing in general how to forecast demand is not enough. Manual demand forecasting is extremely hard and error-prone. It might work to a certain extent for small eCommerce businesses but usually involves collecting vast amounts of data, identifying trends and variables, creating complex statistical models, running calculations, and regularly updating forecasts.

Even if you can deal with all of this by yourself, the static models often would miss crucial patterns and relationships in the data, leading to inaccurate forecasts.

The “demand” for demand forecasting software is ever growing as it has been proven successful in helping businesses automate a once tedious process using algorithms and AI.

Popular options include:

• Oracle Demand Management Cloud. This software uses machine learning algorithms to analyze historical data and predict future demand. It also has a user-friendly interface that allows companies to easily visualize and analyze their data.
• SAS Demand-Driven Planning and Optimization. It uses advanced analytics and machine learning algorithms to forecast demand and optimize inventory levels. It also provides real-time updates and alerts to help businesses stay on top of demand fluctuations.
• Vanguard Predictive Planning. Vanguard uses AI to run a Best-Fit analysis on forecast records at the beginning of each forecast cycle. It uses real-time updates and alerts to help businesses stay on top of demand fluctuations.
• Blue Ridge Supply Chain Planning. Machine learning algorithms are used to analyze historical data and predict future demand. It also provides real-time updates and alerts to help businesses stay ahead of demand fluctuations.
• Demand Works Smoothie. Here advanced analytics and machine learning algorithms to forecast demand and optimize inventory levels. It also provides a user-friendly interface that allows businesses to easily visualize and analyze their data.

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The Wrap Up

Demand forecasting gives eCommerce businesses a real competitive advantage. By proactively predicting customer demand, you can make smarter strategic decisions and stay one step ahead of the game.

While it may seem daunting at first, modern technologies have made it easier and more achievable with better outcomes. Don’t hesitate to unlock its benefits, whatever the size of your online business.