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What are the advantages and disadvantages of each demand forecasting method for E-Commerce?

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Demand forecasting is the process of estimating the future demand for products or services based on historical data, market trends, customer behavior, and other factors. It is essential for E-Commerce businesses to plan their inventory, pricing, marketing, and distribution strategies. However, there is no one-size-fits-all method for demand forecasting, and each one has its own advantages and disadvantages. In this article, we will compare and contrast four common methods of demand forecasting for E-Commerce: qualitative, quantitative, causal, and machine learning.

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Bhakti Tanna

Entrepreneur | Engineer | ISB | SPJIMR
Ram Dev

Global eCommerce | Full-Stack Marketer, Tech Consulting
Afsar Saman M A

Digital Marketer & AI Enthusiast | Helping Businesses Succeed Online

1 Qualitative methods

Qualitative methods rely on human judgment, intuition, and opinions to forecast demand. They are often used when there is little or no historical data available, or when the market is uncertain or volatile. Some examples of qualitative methods are surveys, focus groups, expert opinions, and Delphi technique. The advantages of qualitative methods are that they can capture the preferences, expectations, and motivations of customers, and that they can incorporate insights from industry experts or stakeholders. The disadvantages are that they are subjective, prone to bias, and difficult to validate or replicate.

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Bhakti Tanna

Entrepreneur | Engineer | ISB | SPJIMR
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Demand forecasting is important to ecommerece business there is no denying to that. But which method to apply can be confusing. If you are a small business then qualitive or quantitative method could be the best way forwards. Qualitative method as the business owner can give a good judgement and knows the market and customers well. Quantative method as excel spreadsheet can be easily available and requires basic understanding. Some software tools are available as a ready to use billing solutions that can help in qunatative analysis. Rest both the methods machine learning and causal method require huge amount of data and deep technical knowledge. They might not be needed for small to medium businesses.

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Ram Dev

Global eCommerce | Full-Stack Marketer, Tech Consulting
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As an e-commerce expert, I often encounter businesses struggling to accurately forecast demand, leading to inventory shortages or overstocking. While quantitative methods rely on historical data, qualitative methods provide insights based on expert opinions and market research. Usually we do it by Market Research, Delphi Method (Which is time consuming)

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2 Quantitative methods

Quantitative methods use mathematical models and statistical techniques to forecast demand based on historical data and patterns. They are often used when there is sufficient and reliable data available, and when the market is stable or predictable. Some examples of quantitative methods are trend analysis, moving average, exponential smoothing, and regression analysis. The advantages of quantitative methods are that they are objective, consistent, and easy to measure or evaluate. The disadvantages are that they assume that the past will repeat itself, and that they may not account for sudden changes or external factors.

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Ram Dev

Global eCommerce | Full-Stack Marketer, Tech Consulting
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The best demand forecasting method for an e-commerce business depends on the specific product, historical data availability, and forecasting horizon. It is often beneficial to use a combination of methods to get a more comprehensive understanding of future demand. These are some ways that we can get started 1. Trend Projection - Simple and easy but Ignores Macro Level Understanding of the Niche/Market 2. Seasonal Indexing - Excellent for products with consistent seasonal patterns 3. Regression Analysis - Useful for identifying relationships between demand and external factors. 4. Exponential moving average (EMA)

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3 Causal methods

Causal methods use econometric models and algorithms to forecast demand based on the relationship between demand and one or more independent variables or factors. They are often used when there is a clear cause-and-effect relationship between demand and other variables, such as price, income, weather, seasonality, or promotions. Some examples of causal methods are linear regression, multiple regression, and time series analysis. The advantages of causal methods are that they can explain the factors that influence demand, and that they can simulate different scenarios or outcomes. The disadvantages are that they require a lot of data and expertise, and that they may not capture the complexity or uncertainty of the market.

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4 Machine learning methods

Machine learning methods use artificial intelligence and data mining to forecast demand based on complex and nonlinear relationships between demand and multiple variables or features. They are often used when there is a large amount of data available, and when the market is dynamic or competitive. Some examples of machine learning methods are neural networks, decision trees, support vector machines, and deep learning. The advantages of machine learning methods are that they can learn from data and adapt to changes, and that they can handle high-dimensional and heterogeneous data. The disadvantages are that they require a lot of computational power and resources, and that they may be difficult to interpret or explain.

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Afsar Saman M A

Digital Marketer & AI Enthusiast | Helping Businesses Succeed Online
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I was handling the ecommerce for a small business. After a successful holiday season the previous year. We decided to launch a new product in the holiday season without historical data. The season came, but the expected rush didn't follow. Reasons? - Not having past sales info made it hard to guess who'd want our products. - We tried guessing demand based on people's opinions, and competitor analysis but it's not always right. - We couldn't use past patterns to guide us because there weren't any for this new item. - The data and machine learning methods were out of our league; we didn't have the tools or the know-how for that.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Nicolas Babin

Business strategist ■ Catapulting revenue & driving innovation ■ Serial entrepreneur & executive with global experience ■ Board member ■ Author
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This is a great article and an issue we always look at. Let's take concrete examples: Time Series Analysis: ? Advantages: Simple, effective for stable trends ? Disadvantages: Poor in fluctuating markets, limited long-term use Trend Projection: ? Advantages: Identifies long-term trends, minimal data needs ? Disadvantages: Assumes past trends continue, ignores external factors Market Research: ? Advantages: Direct customer insights, good for new products ? Disadvantages: Time-consuming, potentially costly Panel Consensus: ? Advantages: Expert opinions, diverse perspectives ? Disadvantages: Subject to bias, can be time-intensive Econometric Models: ? Advantages: Accurate, considers multiple variables ? Disadvantages: Complex, data-intensive

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Ram Dev

Global eCommerce | Full-Stack Marketer, Tech Consulting
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Demand forecasting accuracy in e-commerce can be greatly improved by understanding consumer psychology, particularly mob psychology. The bandwagon effect can be capitalized on by analyzing social media trends, customer reviews, and popular product categories. In addition, mob psychology helps identify new trends and product launches with high demand potential. Businesses can proactively prepare for increased demand by analyzing the emotional factors driving consumer behavior. Despite quantitative demand forecasting being strong, incorporating mob psychology insights can help businesses anticipate consumer behavior shifts, capitalize on emerging trends, and improve forecasting accuracy.

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What are the advantages and disadvantages of each demand forecasting method for E-Commerce?

1

2

3

4

5

1 Qualitative methods

2 Quantitative methods

3 Causal methods

4 Machine learning methods

5 Here’s what else to consider

E-commerce

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What are the advantages and disadvantages of each demand forecasting method for E-Commerce?

1

2

3

4

5

1 Qualitative methods

2 Quantitative methods

3 Causal methods

4 Machine learning methods

5 Here’s what else to consider

E-commerce

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