Reasons for Demand Forecasting

Demand forecasting involves predicting future sales data using historical data, market research, and other influential factors. It allows businesses to create more precise sales predictions. There are many types and methods of creating a demand forecast for your business. Determining which will work best for your e-commerce business is challenging, but it is well worth the investment. Creating a demand forecast is not 100% accurate but will inform better business decisions, strategy, and cash flow. Subsequently, let’s get into the basics of demand forecasting; the types, benefits, and steps to create one. Specifically, demand forecasting predicts future sales by examining historical data, market research, and other factors. As a result, businesses can make more accurate sales predictions.

While there are many forecasting methods, choosing the right one for an e-commerce business can be difficult but valuable. Ultimately, demand forecasts, while not completely precise, allow for better decisions, strategy, and financial planning. Moving forward, we will explore the fundamentals of demand forecasting including the different types, advantages, and how to put one together.

Failing to plan is planning to fail.

Benjamin Franklin

In e-commerce, demand forecasting is a collection of techniques and the collation of multiple data points to help make educated guesses on future sales. The data typically includes; market research, historical sales, market trends, both historical and predicted, and internal and external factors. Creating an internal demand forecasting model is unique to each e-commerce business. Furthermore, different demand forecasting models will be required for various purposes. For instance, you might use a micro-demand forecast to determine future sales for a specific seasonal item and a more extensive Delphi method when you launch a new product.

Specifically, in e-commerce, demand forecasting utilizes various techniques and brings together data points like market research, past sales, market trends, internal and external factors, and more to help estimate future sales. In particular, every e-commerce business creates a unique internal demand forecasting model. Additionally, different forecasting models will be necessary for different needs. For example, a micro-demand forecast may be used to predict sales of a seasonal product while a broader Delphi method could be implemented when launching a new item. Essentially, demand forecasting is the process of gathering data to calculate educated projections of future e-commerce sales.

Optimize Inventory

Planning supply chain fluctuations through demand forecasting will help you order appropriate amounts of inventory to avoid out-of-stock situations and limit overstock. Both conditions create suboptimal profitability.

Optimize Pricing Strategy

Reviewing historical sales data will reveal seasonal spikes and dips in sales. This will allow you to make modifications to the pricing accordingly. For example, if you see a drop in sales every year after Labor Day, that might be a good time to offer a promotional discount to keep customers engaged.

Higher Customer Satisfaction Scores

A quick way to lose a customer is to run out of inventory. Reduce stockouts and maintain trust and reliability with proper demand forecasting.

Rationalize Cash Flow

A solid demand forecast model will help determine when and how much cash will be available to order new inventory, manage seasonal operating costs, scale the business, or launch a new product.

Required to Secure Business Funding

Every e-commerce business needs capital to expand. Whether that finance comes from existing profits or outside investment, accurate demand forecasting will allow key stakeholders to estimate the ROI and timeframe for success.

Passive

Passive forecasting is a review of objective historical facts and data. It uses past year’s sales data to predict future sales. (Great for companies that have solid sales data) Passive forecasting works well for companies looking to maintain stability. Many eCommerce brands will include this data in their overall demand forecast strategy.

Active

Active forecasting is a more objective strategy. This type of demand forecasting includes market research, focus group outcomes, global trends, and even emerging technologies or events to determine the sales potential of future products. Both startups and veteran eCommerce brands can utilize this type of forecast. It is an excellent choice for companies looking to grow or with very little historical sales data.

Micro

Micro-demand forecasting investigates a subset of data within an industry or customer segment. For example, you might evaluate the sales of a skincare product as a whole and benchmark it against sales tied to a celebrity influencer. This can help an organization make decisions about verticals within their product line, marketing effort expectations, etc.

Macro

Macro-demand forecasting looks at external elements that influence eCommerce sales. This can include economic trends, consumer trends, supply chain delays, and global events that impact market stability or growth. Understanding these external forces can help a business prepare for shifts in product availability, financial challenges, and vendor diversification.

Short-term

Short-term forecasting includes the next 3-12 months of potential sales. This is beneficial for companies with products that change frequently. A longer timeline is needed when considering growth potential, launching new markets, and entering new partnerships.

Long-term

Long-term forecasting makes predictions one to four years into the future. This aspirational model determines growth potential, marketing efforts, capital investment, and supply chain adjustments. Businesses that have expansion goals should consider using this type of model.

Set Goals and Objectives

A demand forecast predicts product demand and sales in future cycles. The first thing to determine is the objectives of creating the estimates. Next, decide what data should be included to ensure the report will help accomplish the goal.

Collect and Record Data

It sounds obvious, but once you determine the goals of your demand forecast, you must then populate it with data. Because demand forecasting is customized, data collection is pulled from disparate sources. Having a software program or data hub can be helpful if you don’t have extensive forecast modeling in excel.

Analyze the Data

It is crucial to create and copy a standard analysis for predictive forecasting. The data in its raw form is unhelpful. Year-over-year reporting cannot be trusted if the way the model is interpreted changes. That is not to say adaptations shouldn’t be made (in fact, they should be made to improve accuracy), but it is vital to maintain an awareness of data sets and changes as you grow.

Set New Business Goals and Budget

Once you have a reliable demand forecast, it is time to consider new business goals, and choices are possible based on the evidence. Create a budget to meet or exceed past sales goals and determine growth trajectories for new products and sales channels.