E-commerce Demand Forecasting: The Ultimate Guide

It’s no secret that e-commerce businesses need to constantly adapt their product offering and marketing tactics to trends in customer behavior if they want to succeed and scale. But keeping up with recent trends isn’t enough to stay ahead of the competition. It requires continuous work to optimize your operations and strategy.

E-commerce forecasting, including the ability to predict future demand, plays a crucial role in future-proofing your operations, enabling you to make timely decisions based on data, instead of relying on hunches or always playing catch up. It allows you to better allocate resources, plan inventory, and adjust your pricing and marketing efforts - all ingredients of a healthy direct-to-consumer and multichannel business.

In this article, we’ll explain the ins and outs of e-commerce forecasting, including its benefits, challenges, and best practices.

| Key Takeaways for E-commerce Brands:

1. E-commerce forecasting predicts demand using historical data, market trends, and other factors. It helps brand owners adjust their pricing, marketing strategy, and cash flow management.
2. Proper demand forecasting has many benefits for e-commerce businesses. It can reduce inventory risks, enable smarter decision-making, and boost your bottom line.
3. Advanced analytics tools, including linear regression and AI-driven models, can help you understand future demand. Choosing the most suitable forecasting method for your business is a core part of routinely refining predictions.

What is E-commerce forecasting?

E-commerce forecasting is a practice used to predict future demand for your products or services in the online marketplace.

It analyzes historical data, market trends, and other relevant factors to estimate your business’s future sales volume. Think of it as a way of trying to foresee the future, but one that relies on actual data rather than guesswork.

Why is e-commerce forecasting important?

Revenues from e-commerce businesses have demonstrated tremendous growth over the past decade. In fact, they're expected to break the $6 trillion mark by 2028. While this data shows the e-commerce field carries great opportunities, it also makes it highly competitive.

DTC business owners who wish to maintain their edge need demand forecasting to future-proof their operations and make them more efficient. Using data-driven forecasting can help you make more informed decisions about your brand.

Types of E-commerce Demand Forecasting

1. Macro-Level Demand Forecasting

Macro-level demand forecasting focuses on external economic and industry trends. By analyzing factors like Gross Domestic Product (GDP) growth, inflation rates, and consumer behavior, ecommerce businesses can gauge the overall market demand for their products or services.

This can help plan major steps, like entering new markets, expanding product lines, or changing product pricing.

A good example of this is the growing focus on sustainability. Many consumers have become aware of their carbon footprint, increasing demand for biodegradable products and less wasteful packaging. This is why some airlines today offer travelers the option of paying a little extra to offset their carbon emissions.

2. Micro-Level Demand Forecasting

Micro-level demand forecasting zooms in on specific products or categories. It examines sales data, customer behavior, and market trends to predict demand for individual product lines.

This information is vital for inventory management, production planning, and promotional activities.

For example, supermarkets are aware that demand for products like turkeys, berries, and frozen rolls increases around the holiday season. To meet this high demand, they typically plan their inventory levels and promotions around it.

3. Long-Term Demand Forecasting

Long-term demand forecasting involves predicting future demand over an extended period, typically several years. It considers factors like technological advancements, demographic shifts, and regulatory changes.

This type of forecasting is essential for making strategic decisions, such as investing in new technologies or expanding to new facilities.

4. Short-Term Demand Forecasting

Short-term demand forecasting focuses on predicting demand in the near future, usually in a few weeks or months. It relies on recent sales data, promotions, and seasonal trends.

This type of forecasting is vital for tactical decisions, such as optimizing inventory levels, scheduling production, and cash flow management.

What Are the Benefits of E-commerce Forecasting?

Reduce Risk

Accurate forecasting helps mitigate the risks associated with overstocking or understocking. Getting stuck with excess inventory can lead to unnecessary costs for your DTC business. Yet, you don’t want to miss out on potential sales and damage customer satisfaction due to stockouts, either.

E-commerce forecasting helps ensure that you have the right amount of inventory on hand to meet customer demand.

Optimized inventory forecasting and cash flow management also reduce the risk of needing to take last-minute loans to meet customer demand - which usually comes with elevated interest rates.

Optimize Inventory Supply Levels

Smart inventory management can make all the difference. Especially if you consider e-commerce businesses typically have 70%-80% of their cash tied up in inventory.

By knowing what to expect, you can increase stock levels of more promising products. This helps to maximize return on investment (ROI) and allows you to scale your operations.

Improve Strategy Allocation

Informed forecasting can help you effectively allocate resources like marketing budgets and workforce. This ensures that resources are directed towards the most promising opportunities.

This also goes for marketing and sales efforts. Understanding demand helps you focus on the right products, at the right time.

Refine Pricing and Sales Strategy

Understanding your financial health by reviewing your Profit & Loss (P&L) is essential for any business. In some cases, your P&L can reveal pricing issues that are affecting your bottom line. To avoid these issues, you can use e-commerce forecasting to adjust your pricing strategy according to demand.

Use it to implement dynamic pricing strategies, and to provide targeted discounts and promotions at the right moments.

What Are the Drawbacks of E-commerce Forecasting?

Methodology Errors

E-commerce forecasting requires processing a large amount of complex data through forecasting models. However, choosing the wrong model can significantly impact the accuracy of predictions. These mistakes are commonly made in choosing a model:

• Overfitting: Using overly complex models that fit historical data too closely, leading to poor predictions for future trends.
• Underfitting: Using overly simple models that fail to capture underlying patterns in the data.
• Ignoring seasonality or trend: Failing to account for seasonal fluctuations or long-term trends in demand.

Using Inaccurate or Incomplete Data

Poor-quality data can lead to biased and unreliable forecasts. Data errors, missing values, and outliers can distort the analysis. That’s why data cleaning and preprocessing are essential to ensure data accuracy and consistency.

Not Accounting for Market Variables

Ignoring external factors like economic conditions, competitive landscape, and consumer behavior can lead to inaccurate forecasts. For example, a sudden economic downturn or the emergence of a new competitor can significantly impact demand.

How to Conducting an E-commerce Forecast in 5 Steps

1. Define Your Goals

Start by clearly outlining the purposes of your forecast. Are you aiming to optimize inventory levels, improve marketing strategies, or make long-term strategic decisions? Understanding your specific objectives will help you choose the forecasting methods, models, and data sources you’ll need to use.

2. Identify Variables

Determine key factors influencing your demand, such as seasonality, promotions, and competitors’ activity.

Consider external factors like economic conditions, industry trends, and technological advancements.

3. Collect Data

To keep track of market changes, you need to know your customer base and target audience. Start by studying your website traffic to learn user habits. You can also review demographic information about your customers or potential customers.

It would also be helpful to gather relevant historical data, including sales figures and marketing expenses.

You can collect data from your e-commerce platform, CRM system, and analytics tools.

4. Perform Data Cleaning

Data cleaning is the process of identifying and correcting errors, inconsistencies, and inaccuracies. It's a crucial step before analysis, since dirty data can lead to misleading insights.

This will usually include:

• Handling and replacing missing values
• Identifying and correcting outliers that can skew the analysis
• Standardizing data formats to ensure consistency
• Removing duplicates to avoid overcounting
• Fixing typos, incorrect data entries, and other errors

By cleaning the data, you ensure that the subsequent analysis is based on accurate and reliable information.

5. Conduct Analysis

Once the data is collected and cleaned, the next step is to analyze it using the appropriate forecasting techniques.

Qualitative Forecasting

This method relies on expert judgment, opinions, and surveys to make predictions. It's particularly useful when historical data is limited or unreliable.

While you can perform qualitative research on your own by sending out email surveys to your customer base, there are benefits to using a service that specializes in this field. Professional researchers have a deep understanding of qualitative research methodologies, ensuring that the survey is designed and executed effectively. They can also provide valuable insights into future trends.

Quantitative Forecasting

This method uses statistical techniques and historical data to make predictions. It's suitable when there's sufficient historical data available.

• Time series analysis: Time series analysis is a statistical technique used to analyze historical data points collected over time. By examining these data points, analysts can identify patterns such as trends, seasonality, and cyclical patterns. 

These patterns can then be used to predict future values. Common time series models include ARIMA and Exponential Smoothing.

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• Regression analysis: Regression analysis is a statistical method used to model the relationship between a dependent variable and one or more independent variables.

For example, imagine you're running a lemonade stand. You want to know how the weather (independent variable) affects how many cups of lemonade you sell (dependent variable). Regression analysis will tell you that on hotter days, you’ll probably sell more lemonade.

E-commerce businesses may use this model to understand how factors like price changes and marketing affect sales.

Common types of regression models include:

1. Linear regression
2. Multiple linear regression, which factors in several variables
3. Polynomial regression, used for examining more complex relationships between variables
4. Logistic regression, which gives yes or no outcomes

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• Machine learning: Machine learning is a subset of artificial intelligence (AI) that involves training algorithms on large datasets to make more accurate predictions.

Machine learning techniques typically used in e-commerce forecasting include Random Forest, Gradient Boosting, Neural Networks, and Time Series Forecasting with Neural Networks.

To determine the most suitable technique, it's often beneficial to experiment with multiple methods and compare their performance on a validation dataset.

Make sure to regularly review and adjust your forecasts to account for changing market conditions and unexpected events.

Variables that Impact Forecasting

There are several factors that can influence the accuracy of e-commerce forecasts.

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Supply Chain Delays

Supply chain disruptions can significantly impact e-commerce forecasting. Factors such as transportation delays, supplier shortages, and natural disasters can lead to stockouts and delayed deliveries.

Pricing Strategy and Promotions

Changes in pricing can also have a direct impact on demand. Price increases may lead to decreased demand, while price reductions may stimulate demand. Marketing campaigns and promotional activities can also influence consumer behavior and demand.

Changing Customer Habits

Consumer preferences and buying behavior are constantly evolving. Factors such as economic conditions, technological advancements, and social trends can influence consumer choices. 

For example, Gen-Z shoppers have been shown to be very deliberate about where they shop, prioritizing factors like quality and sustainability over price.

Global Events

Global events, such as pandemics, natural disasters, and geopolitical tensions, can also affect consumer sentiment and impact demand.

Seasonality

Seasonal variations in demand can have a significant impact on e-commerce sales. By understanding seasonal trends, ecommerce businesses can optimize inventory levels, production schedules, and marketing campaigns.

New Competition

New competitors may not always be taken seriously or seen as a threat, but they can significantly impact your market share and sales. Incorporating competitive intelligence into forecasting models can help DTC businesses stay ahead of the curve.

Conclusion 

E-commerce businesses need demand forecasting to stay ahead of the competition. By using data to future demand, you can manage inventory better, plan marketing moves, and meet customer needs more effectively. Of course, accurate forecasting starts with accurate data.

Finaloop provides hands-off accounting solutions for e-commerce brands, combining automation with an expert bookkeeping team. Our software gives you real-time financial data, including P&L statements and inventory management insights, so you can stay on top of your finances and make smarter decisions.

FAQs

What are the most popular e-commerce forecasting methods?

E-commerce forecasting uses various methods to predict future demand. These include time series analysis, which studies past sales patterns, regression analysis, which links sales to other factors, and machine learning, which uses advanced algorithms to learn from data.

How can demand forecasting help your business?

Demand forecasting helps ecommerce businesses optimize inventory levels, improve supply chain efficiency, and make informed decisions about production, pricing, and marketing strategies. By accurately predicting future demand, DTC businesses can reduce costs, improve customer satisfaction, and drive growth.

How is E-commerce forecasting done?

E-commerce forecasting involves collecting historical sales data, cleaning and preparing the data, selecting appropriate forecasting models, training the models on the data, generating forecasts for future periods, evaluating the accuracy of the forecasts, and refining the models as needed.