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Free Certificate
Data Science in E-commerce
Master data science for e-commerce success! Learn recommendation systems, price optimization, market mix modeling, and A/B testing to drive sales and enhance customer experience.
13 hours of learning
Data Analysis
Searching/Sorting
A/B Testing
KEY HIGHLIGHTS
What You Will Learn
Data Analytics in E-commerce
Explore the business models and revenue streams of e-commerce firms, their processes, challenges, and how data analytics aids each stage.
Topics Covered:
- Inventory Management – Optimize stock levels to prevent shortages and overstocking.
- Improving User Experience – Use data insights to enhance customer journey and satisfaction.
- Fraud Detection – Identify and prevent fraudulent activities using data analytics.
- Marketing in E-commerce – Leverage data to optimize marketing strategies and campaigns.
Recommendation Systems
Learn about recommendation systems, their types, and their challenges. Then, implement one in R to recommend movies from a database.
Topics Covered:
- Content-Based Filtering – Recommend products based on user preferences and past behavior.
- User-Based Collaborative Filtering – Suggest items by analyzing similar users’ behaviors.
- Item-Based Collaborative Filtering – Find recommendations by comparing item similarities.
- Recommender System in Python – Implement recommendation algorithms using Python.
Price Markup & Markdown
Explore business cases on price markup/markdown, learn their reasons, and use a markup/markdown framework.
Topics Covered:
- Understanding Price Markup & Markdown – Learn how pricing adjustments impact revenue.
- Why They Are Done – Discover the strategic reasons behind price changes.
- Effects of Price Markup & Markdown – Analyze how pricing shifts influence sales and profits.
The Four Force Model
Analyzing demand elasticity, competition, internal economics, and category dynamics can help determine optimal product pricing using the Four Forces Model.
Topics Covered:
- The Four Forces of Price Optimization – Understand key factors affecting pricing strategies.
- Demand Elasticity – Measure how price changes impact consumer demand.
- Competitive Benchmark – Compare pricing with competitors to stay competitive.
- Internal Economics – Evaluate business costs and profitability in pricing decisions.
- Category Dynamics – Assess market trends and product positioning for pricing.
Factors that Impact Sales
Learn how Marketing Mix Modeling (MMM) uses regression to optimize marketing for maximum sales and profit growth.
Topics Covered:
- What is MMM and Why It Is Important – Use regression to analyze marketing effectiveness.
- How Advertising Impacts Revenue – Evaluate the influence of ad spending on sales.
- How Pricing and Promotion Impact Revenue – Understand the effect of discounts and pricing strategies.
- How Product Assortment Impacts Revenue – Optimize product offerings to drive sales.
Modeling the Impact of KPIs
Use regression models to predict and optimize marketing impact on sales and profitability.
Topics Covered:
- Basic Linear Model – Establish relationships between marketing inputs and revenue.
- Multiplicative Model – Measure non-linear impacts of marketing efforts.
- Exponential Attraction and Multinomial Logit Model – Assess customer preferences using advanced models.
- Koyck and Distributed Lag Model – Analyze long-term effects of marketing decisions.
- Hierarchical Model – Understand complex interactions between marketing variables.
A/B Testing
Use A/B testing to make data-driven decisions and optimize web pages and marketing strategies.
Topics Covered:
- Analyzing the Data – Interpret test results to measure effectiveness.
- Forming a Hypothesis – Define key assumptions before conducting experiments.
- Constructing the Experiment – Design and implement A/B tests for optimization.
- Interpreting the Result – Draw actionable insights from A/B testing outcomes.
Data Science for E-Commerce Certification
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Receive a signed and verifiable e-certificate from upGrad upon successfully completing the course.
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