Effective Strategies for Ecommerce Demand Forecasting
Whether you sell on a major platform like Amazon, have your own online store, or do both, ecommerce demand forecasting is essential for every online seller. Mastering this forecasting method is particularly helpful for online entrepreneurs, because it doesn’t really matter whether you are a small business owner aiming to expand, or an already established Amazon veteran – when done correctly, ecommerce demand forecasting always improves your bottom line.
In this article, we have put together proven strategies for demand forecasting in ecommerce, and explain why it’s so vital for growth. We’ve also added practical tips you can easily add to your business strategy.
What is Ecommerce Demand Forecasting?
Ecommerce demand forecasting is a way of predicting future customer demand for products. You do this by using the historical sales data, market trends, and other relevant information your business has shown in the past. If the forecast is done correctly, this data will enable you to optimize your inventory, improve customer satisfaction, and, what it all comes down to in the end of course, increase sales and revenue.
Why Is Demand Forecasting for Ecommerce so Important?
One of the worst things that can happen as a seller is going out of stock, when there is still demand for the product. Not only do you miss out on revenue, but your seller rating takes a huge hit as well – especially on Amazon. Demand forecasting is an extremely effective way to circumvent this. Apart from that, it also allows you to anticipate market trends, avoid overstock situations (the opposite of what was just described), and allocate resources efficiently. Optimally, it will enable you to make informed decisions about inventory, marketing, and pricing strategies, ultimately leading to increased profitability.
Key Methods for Demand Forecasting in Ecommerce
There are several demand forecasting methods used in ecommerce, each with its own advantages and limitations. Here’s a list of the most commonly used methods.
Time Series Analysis
Time series analysis requires you to examine historical data over a specific period to identify patterns and trends. If you are looking to identify seasonal patterns and make short-term forecasts, this is the way you want to do it. However, it may not account for sudden market changes or emerging trends.
Example: You sell sports equipment. Looking at your sales data over the past three years, you see that there is a spike in rock climbing equipment during the summer months of April to August. Accordingly, you increase the prices for rock climbing equipment during these months in the current year to make more revenue.
Causal Models
Causal models require you to take external factors into consideration. Examples for external factors are economic indicators, marketing campaigns, or competitor actions. Analyzing these factors will allow you to make accurate predictions about future demand. This approach is most effective with comprehensive data and sophisticated analytical tools.
Example: You sell fitness trackers. Using a digital marketing solution like Semrush or Ahrefs, you are able to research that the queries for “buy fitness tracker” spikes in January because of everyone’s New Year’s resolutions. You also notice that the queries drop dramatically after two months, when everyone has given up on their resolutions. To boost sales, you plan more ads in January and stock up on inventory during these times. After February, you cancel the ads and lower the price of the items again.
Machine Learning Algorithms
Machine learning algorithms use AI to analyze vast amounts of data and identify patterns that are impossible for humans to see in the same amount of time. These algorithms can provide your business with highly accurate forecasts, but do require a certain amount of knowledgability in data science and access to advanced technology. Not something every seller has the capacity and time to acquire.
Example: You sell fashion items. By using machine learning algorithms, you analyze large datasets to find patterns in customer buying habits and trends. This helps you predict which items will be popular next season and stock accordingly. In ecommerce, you can use several machine learning algorithms to improve demand forecasting and optimize various operations.
Qualitative Methods
Rather than rely on numerical data, qualitative methods in ecommerce demand forecasting are based on subjective insights such as expert opinions or market research. These methods can be valuable when your quantitative data is limited or when you’re predicting demand for new products. The main drawback is that they are often less precise than quantitative methods.
Example: Your online clothing store is going well. In order for it to run even better, you will try to make a forecast demand for your new summer collection. You do this with customer surveys and focus groups. You gather feedback on design preferences, colors, and styles. Based on this qualitative data, you make decisions on inventory and marketing strategies, making sure your customer demands are met.
Demand Forecasting Automation for Ecommerce
No matter in what line of work you are, automations will always streamline processes and improve the accuracy of your efforts. Demand forecasting in ecommerce is no exception in this case. Consider these steps when automating your ecommerce demand forecasting.
Step 1: Gather and Organize Data
Collect data from all sources you have access to and that are relevant. E.g. sales history, market trends, and customer feedback. Organize this data in a format that’s structured and easy for you to work with.
Step 2: Choose the Right Software Solutions to Scale
Once you are already established and are looking to scale, find software solutions that align with your business needs and technical capabilities. These do not necessarily have to be paid options. Platforms like Amazon are known to offer built-in analytics and forecasting tools. The drawback of using freeware is that these tools are not sophisticated enough to drive real growth. Professional sellers should therefore consider investing in solutions that were designed for scale.
SELLERLOGIC Business Analytics tracks your business numbers in real-time and up to two years into the past, starting from the point of your registration. This ensures you have all the data you need to outsmart your competitors. Not only do you get all your Amazon business information visualized in easy-to-understand charts, but you also receive all relevant data such as KPIs and sales numbers shown in one place – on global, marketplace and product levels. This and more information will not only give you all the information you need to perform a solid ecommerce demand forecast, but also set you up for success when deriving action items.
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Step 3: Test and Validate Models
Before fully implementing automated forecasting, test your chosen models using historical data from your company to see if they are accurate enough to work with. Validate the models by comparing predicted results with your actual outcomes.
Step 4: Continuously Monitor and Adjust
Demand forecasting is not a one-time task; you need to monitor your results consistently and make adjustments when needed. Review your forecasts regularly and update your models based on new data and changing market conditions.
Practical Tips for Improving Your Ecommerce Demand Forecasting
Here’s an overview of tips that will support your demand forecasting efforts.
Leverage Historical Data
Historical sales data is maybe the most valuable resource you can use for demand forecasting. Analyze past sales patterns, seasonality, and trends to make informed predictions about future demand.
Stay Informed about Market Trends
Industry trends, consumer behavior, and competitor actions are always worth keeping an eye on. Staying informed will help you anticipate changes in demand and adjust your strategies accordingly.
Stay close to Stakeholders
Keep in close contact with marketing and supply chain management to keep your data up-to-date. Align your forecasting efforts with the company’s overall business goals.
Use Scenario Planning
Create multiple demand forecasts based on different scenarios. This approach will come in very handy when preparing for various outcomes and developing contingency plans when addressing potential challenges your business is bound to face at some point.
Wrapping Up
Ecommerce demand forecasting is an important aspect of a successful online selling strategy. Using the right methods, tools, and techniques, you can optimize your inventory, improve customer satisfaction, and increase sales for your online shop and on platforms like Amazon.
FAQs
Use historical sales data, monitor market trends, and consider external factors like seasonality and promotions. Leverage AI-powered tools for accuracy, collaborate with suppliers for better insights, and adjust forecasts regularly to avoid overstocking or stockouts, ensuring optimal inventory levels.
An ecommerce demand forecast predicts future product demand using past sales data, market trends, and consumer behavior. It helps businesses plan inventory, manage supply chains, and reduce costs while ensuring products are available when customers need them, boosting sales and customer satisfaction.
Yes, software tools improve your demand forecasting because they are able to analyze vast datasets, identify patterns, and provide actionable insights better and quicker than the human mind. They also improve accuracy, save time, and help businesses adapt to market changes. While manual forecasting is possible, software tools are essential for scaling and reducing human error.
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