What is AI demand forecasting? Use cases for e-commerce (2024)

Imagine being able to predict which products to stock, how to avoid supply chain disruptions, and setting the ideal price for your goods. Traditional demand forecasting methods rely on historical demand trends and sales data, but often miss the mark when confronted with unpredictable variables such as social media trends, natural disasters and the changes. consumer sentiment.

But times are changing. Today’s AI tools are supercharging the demand forecasting process. They instantly aggregate and analyze internal and external factors to inform strategic e-commerce decision-making.

Say goodbye to excess inventory and inefficiencies, and hello to streamlining supply chain management and more satisfied customers. Here’s how AI can simplify demand planning.

What is demand forecasting?

In e-commerce, demand forecasting predicts future demand and sales trends for your products or services using key metrics, including:

• Sales data. Information about your total production includes total sales, average quarterly sales, and sales rates for individual products.

• Consumer purchasing habits. Information about customer habits includes product preferences, purchase frequency, and transaction timing.

• Market dynamics. Factors such as supply and demand, regulation and competition vary by industry and may influence forecast results.

Demand forecasting provides valuable information for strategic planning in inventory management, production and personnel. The goal is to maximize on-demand preparation time, optimize resources, prevent overstocks or shortages, and ensure competitive price to protect profit margins.

The challenges of demand forecasting

Traditional forecasting methods, such as manual data collection or relying on historical market trends, have proven useful in the past. In today’s rapidly changing e-commerce landscape, they are quickly losing relevance. Trying to predict demand with traditional methods can lead to the following challenges:

Inefficiency

One of the main drawbacks of traditional demand forecasting models is the inefficiencies inherent in manual data collection and analysis. Accumulating measurements from year to year and distilling valuable information to anticipate demand is a significant challenge. Compiling real-time data from sources like social media, web traffic, and sales requires significant effort, especially for small businesses with limited teams and resources.

For example, predicting the future consumer behavior and market changes in your business may require daily analysis of social media engagement, trending styles and sales data to find actionable patterns. Achieving this may require strong communication and synergy between departments, which can be difficult to establish and maintain.

Linear forecast results

Previous forecasting models tend to rely on linear regression: predicting one variable (like demand) based on the outcome of another (like historical sales over time). Although useful in isolation, this approach neglects external factors such as seasonal fluctuations in demand or unforeseen events. supply chain issues. Today, accurate demand forecasting requires considering multiple variables at once, because demand rarely follows a simple cause-and-effect pattern.

Isolated data points

Isolated data points, also known as data silos, are a major drawback of traditional forecasting methods. Business areas, like marketing and sales, often have separate metrics, leading to conclusions that only apply to one department. It is difficult to obtain accurate forecasts without a holistic view of supply chain operations. customer datasales trends and marketing efforts within your organization. Manually collecting data points from each department to identify patterns is time-consuming and difficult to reproduce consistently.

Reliance on historical data

For decades, businesses have used historical sales data to predict future demand for products or services. However, relying solely on past sales data can produce inaccurate forecasts because it overlooks changing consumer preferences. There are also real-time external factors, such as social media trends and natural disasters, that can quickly and dramatically affect customer demand and disrupt supply chain logistics.

AI and demand forecasting

Artificial intelligence transforms business operations, including demand forecasting. Using machine learning technology, AI leverages data such as current sales, competitor prices, inventory, and industry trends to identify demand patterns in real time. This information can inform strategic planning efforts, such as increasing inventory of specific products and launching targeted products. marketing campaigns.

Another benefit of AI-based demand forecasting is its adaptive learning capabilities, meaning it can continually improve its accuracy over time. The more you use AI-based analytics models to forecast demand, the better they are at identifying pain points and precise improvements.

Let’s say you run a ski clothing company, for example. AI can analyze historical sales data, weather forecasts, social media trends, and real-time sales trends to predict product demand for the upcoming winter season. As the season progresses, AI discovers the impact of these factors on consumer behavior and supply chain logistics, optimizing preparations for the next peak season.

Ecommerce Use Cases for AI Demand Forecasting

Whether you run a large fitness clothing brand or a small jewelry store, here are three practical ways to use AI-driven demand forecasting to your advantage:

Improve operational efficiency

Operational efficiency – the ability to produce high-quality goods and services using as few resources as possible – is essential to a company’s success. AI improves the demand forecasting process by analyzing a wide range of data, optimizing business operations and enabling more efficient allocation of resources through automation.

For example, you can minimize unnecessary expenses by maintaining optimal staffing, tracking inventory needs in real time, and automating simple tasks such as loyalty program emails.

Simplify supply chain planning

Supply Chain Management is the complex process of providing products or services to consumers. This involves raw material sourcing, manufacturing, shipping logistics, inventory management and production planning. AI forecasting can streamline these processes by simultaneously tracking real-time inventory, sales targets, delivery estimates and market trends.

AI tools can quickly alert you to potential disruptions, helping you prevent inventory shortages or production delays due to unforeseen factors like bad weather. This way, you are informed of costly disruptions as they occur, allowing you to adjust shipping carriers or manufacturing suppliers in time.

Improve customer satisfaction

Customer satisfaction is the goal of every business transaction because it generates repeat purchases and strong Net Promoter Scores (NPS), which in turn protects your reputation and bottom line. AI forecasts can identify consumer demand using sales data, market trends, competitor pricing, web traffic and audience behavior.

It offers actionable insights to better serve your customers, such as:

• Personalizing Marketing Efforts

• Implement dynamic pricing strategies for optimal pricing

Let’s say you own a running shoe company. AI reveals that your audience is interested in blue marathon shoes in sizes 8-10, competitors are offering similar models at lower prices, and your carriers are experiencing delays. To improve customer satisfaction, you can increase stock of specific models, lower your prices, change carriers to avoid delays, and tailor your message to speak to marathon runners.