E-Business Strategy and Platform Analytics Fundamentals

Posted on Jan 31, 2026 in Physical Activity and Sports Sciences

E-Business Fundamentals (Weeks 1 & 2)

E-Business vs. E-Commerce: Scope and Strategy

1. Understand the concept of e-business and explain how it differs from e-commerce in terms of scope, activities, and strategic focus.

• E-Commerce (Electronic Commerce): Refers to digitally enabled commercial transactions across organizations or individuals, encompassing the online buying and selling of goods and services.
• E-Business: Refers to the digital enablement of business processes and transactions not only across firms but also within a firm through Information Systems.

Key Differences

• Scope: E-Commerce is narrower, representing a subset of e-Business. E-Business is broader, extending beyond e-commerce to include internal digital processes, supply chain integration, and knowledge management.
• Strategic Focus: E-Commerce focuses on external transactions (sales), while e-Business focuses on internal efficiency and the strategic integration of IT into all business functions.
• Performance Model: E-business performance is modeled as a function of the integration of business activities and IT: Performance = f(Business X IT).

Transitioning to a Hybrid E-Business Model

2. Understand how a traditional brick-and-mortar company can transition into a hybrid e-business model. Address required changes in technology adoption, supply chain integration, customer experience, and organizational transformation.

1. Technology Adoption (Technical Convergence): Requires the convergence of multiple technologies into an integrated electronic infrastructure, utilizing IT devices as access points.
2. Supply Chain Integration (Business Convergence): Requires the integration of business processes, workflows, IT infrastructures, knowledge, and data assets within and among firms. Example: Walmart’s RetailLink connects suppliers directly for real-time inventory and collaborative planning.
3. Customer Experience: The organization must present a single point of contact to customers via electronic integration. This involves gathering finely segmented customer data and delivering tailored services. Example: Hilton tailoring services based on HHonors profiles and past visits.
4. Organizational Transformation: Requires a strong vision and negotiation across business units. The firm must evolve to become nodes in a network of firms, integrating processes with the common goal of obtaining data and adapting the organization’s structure.

Platform Economics and Network Effects (Weeks 3 & 4)

Understanding Two-Sided Markets

3. Understand a two-sided market and give a relevant example (e.g., Uber, Airbnb, YouTube). Explain why two-sided interactions are central to platform success.

• Two-Sided Market/Network: A network with two distinct user groups (sides) whose members consistently play the same role in transactions. The platform acts as a digital infrastructure that connects and facilitates these two groups’ interactions.
• Example: Uber connects drivers (suppliers) and riders (users).
• Centrality to Success: Two-sided interactions are central due to cross-side network effects. Platform value increases with the size of each side, creating a positive loop mechanism where more users attract more suppliers, which in turn leads to diversified functionality and attracts even more users.

Direct vs. Indirect Network Effects

4. Understand network effects and explain the difference between direct and indirect network effects. Consider an example of each.

• Network Effects: Evident when a network’s value to any given user depends on the number of other users with whom they can interact.
• Direct Network Effects (Same-Side Effects): A preference by users regarding the number of users on their own side of the network.
  • Example: A gaming community where users prefer more peers on the platform to play or swap video games.
• Indirect Network Effects (Cross-Side Effects): A preference by users for the number of users on the other side of a multi-sided network.
  • Example: On a smartphone platform, an increase in users attracts more developers (suppliers) to create more apps, which in turn increases the network’s value for the users.

Platform vs. Pipeline Firms: Strategic Comparison

5. Compare and contrast the strategic priorities of a platform firm (e.g., Airbnb, Amazon Marketplace, Android) with those of a traditional pipeline firm. Discuss differences in value creation and value capture, the role of data, and cost structure and scalability.

Platform Fee Structures and Subsidies

6. Understand how a platform’s fee structure differs from the linear markup structure in a traditional value chain. Why do platforms often charge different fees to different user groups?

• Fee Structure Difference: A traditional value chain uses a linear markup structure where profit is added sequentially at each stage. A platform’s fee structure typically involves intermediary fees (commissions) charged directly to both the suppliers and the users.
• Why Different Fees (Permanent Subsidies): Platforms often charge different fees (or subsidize one side) to accelerate network growth and overcome the Penguin Problem (Excess Inertia). This strategy involves:
  1. Subsidizing the price-sensitive side (the subsidy side, e.g., consumers) to expand its user base.
  2. This growth increases the Willingness to Pay (WTP) of the other side (the money side, e.g., suppliers) due to positive cross-side network effects.
  3. The platform then boosts fees to the money side, extracting enough additional profit to recover the subsidy and make a greater profit.

Accelerating Platform Growth: Incentive Strategy

7. A new platform needs 500 buyers and 200 sellers to reach critical mass but currently has only 300 buyers and 80 sellers. 1) How should the platform adjust incentives on each side to accelerate growth? 2) Which side should be subsidized more heavily, and why?

Current State: Buyers are at 60% of the goal (300/500). Sellers are at 40% of the goal (80/200).

1. Adjusting Incentives: The platform must implement strategies to address the Penguin Problem by increasing the expected future network size.
   • Buyers (Users): Offer discounts or promotional subsidies (e.g., providing a 20% coupon) to increase the adoption rate on the larger side.
   • Sellers (Suppliers): Offer to decrease the service charge/commission (e.g., from 10% to 7%) to increase their incentive to join and list products.
2. Which Side to Subsidize: The platform should subsidize the seller side (the market side with the biggest gap/need) more heavily.
   • Reason: Sellers (suppliers) are the party whose presence adds differentiated value or content (variety), which drives the Indirect Network Effect that increases the WTP of buyers (the subsidy side). Filling the greatest gap (sellers at 40% of goal) is critical to reaching critical mass.

The Critical Role of Business Analytics (Week 5)

Analytics in Platform Value Creation

8. Understand the primary role of business analytics in a platform business. How does analytics contribute to value creation?

Primary Role: Business Analytics (BA) is Mission-Critical as it processes the high-volume, high-velocity data generated by platform and app interactions. It constructs a multi-layered customer graph by integrating data from many touchpoints (e.g., tied back to a “guest ID”).

Contribution to Value Creation: This structured data foundation enables:

• Personalization and Targeting: Predictive modeling for personalized recommendations, dynamic pricing, and targeted promotions.
• Performance Insight: Allows the platform to map and predict customer journeys across touchpoints and measure network effects.

Why Analytics is Critical for Platforms

9. Why is business analytics more critical for platform companies than for traditional pipeline/value-chain businesses? Provide at least two reasons.

1. Handling Data Volume and Velocity: Platform and app interactions generate a continuous Data Explosion (high-volume, high-velocity data) that requires sophisticated analytics to organize and maintain, a challenge far exceeding that of episodic data collected by many traditional businesses.
2. Managing Network Effects: Analytics are necessary to measure and manage network effects and cross-side platform dynamics, which are the non-linear forces driving platform growth and competition. Traditional pipeline businesses do not deal with this complexity.
3. Creating Competitive Insight: BA integrates data from multiple, often incompatible sources (internal systems, social media, third-party data). This process of data construction is the essential foundation on which competitive analytics and customer insight are built for the platform business.

Analytics for Matching, Trust, and Safety

10. Understand how analytics supports supply-demand matching in a two-sided platform. Consider two examples of how business analytics enhances trust and safety on a platform.

Supply-Demand Matching: Analytics supports matching by enabling predictive modeling to estimate the relationship between X and Y, such as “How likely is client X to buy product Y?” This insight allows the platform to tailor and personalize offers and content in real time and decide on which products to recommend, optimizing the match between available supply (ads, products) and customer demand.

Enhancing Trust and Safety (Examples):

• Risk Management: Classification models predict which clients are “at risk” of going to competitors or assess the Loan Risk of applicants, allowing the platform to deploy countermeasures or screen participants.
• Quality and Credibility: Sentiment Analysis and classification models classify product reviews to ensure the quality and credibility of the information users receive, building trust in the platform.

Managing Network Effects with Analytics

11. Understand how analytics helps platforms strengthen and manage network effects, especially during early-stage growth.

Analytics helps manage network effects by focusing on two critical areas: increasing customer intimacy and maximizing supplier engagement.

• Strengthening Customer Side: Analytics powers personalized recommendation systems to enhance customer satisfaction and encourage repeat purchases, which increases customer and supplier intimacy and raises switching costs. This strengthens the pull for the demand side (users).
• Managing Early Growth (Subsidies/Incentives): Analytics informs the strategic use of subsidies (as discussed in Q6). By generating customer data, the platform can analyze which side needs more incentives (the subsidy side) to achieve critical mass, accelerating the positive feedback loop.

Machine Learning Concepts (Weeks 6–12)

Normality Assumptions in ML Algorithms

12. Why do many machine learning algorithms assume normality in their input data? What benefits does this assumption provide?

Assumption/Principle: Many models assume normality because the Central Limit Theorem (CLT) states that even if the population is not normal, sample means are approximately normal as long as the sample size is large enough.

Benefits: Assuming a Normal Distribution helps to more easily:

• Predict Behavior: It helps easily predict a user’s behavior or average consumer behavior.
• Quantify Error (Confidence Interval): It allows for the calculation of a confidence interval (e.g., 95% confidence interval), providing information about the variability of the estimate or predicted value, which helps the firm make a backup plan.

Classification vs. Econometric Models

13. The Titanic dataset is often used to predict whether a passenger survived (1) or did not survive (0). Explain why survival prediction is a classification problem rather than a traditional econometric model.

• Classification Problem: Survival prediction is a classification problem because the target variable (Survived) is a categorical outcome (Yes=1/No=0). The goal is to predict which of the two predefined categories the data point belongs to.
• Contrast with Econometric Model: Traditional econometric models are generally concerned with measuring the causal impact of a specific variable (e.g., Age) on a target variable, assuming the data follows a normal distribution and is often small data. Classification’s focus is simply on accurate prediction of the categorical outcome using a range of predictors.

Classification Insights for Business Decisions

14. What business insights can classification models provide for decision-making in areas such as risk management or customer segmentation?

• Risk Management: Classification models determine if a customer belongs to a predefined class, such as the Reject Class (customers with trouble paying back the loan) or Accept Class. This allows banks to provide business insights by determining the Loan Risk and deciding whether to approve the application.
• Customer Segmentation: Classification helps narrow down the consumer group by predicting if a customer will Respond or Not Respond to a marketing campaign based on attributes like Age, Gender, and Income. This insight guides targeted promotions and customer retention efforts.

The Importance of Testing Data

15. You split a dataset into training (70%) and testing (30%). Why is it important to evaluate a classification model on a test set rather than only on training data?

• Goal of Generalization: The primary goal of a model is to generalize effectively to new, unseen data, not just fit the training data well.
• Risk of Overfitting: Evaluating only on training data risks Overfitting. Overfitting occurs when the model learns noise or irrelevant details from the training data instead of the true patterns.
• Unbiased Estimate: The testing data provides an unbiased estimate of the model’s real-world performance and generalization ability, ensuring the deployed model is reliable and offers business value.

Determining K in K-Means Clustering

16. When using k-means clustering, how should a business analyst decide on the appropriate value of k? Discuss both quantitative and qualitative considerations.

• Quantitative Consideration (Elbow Method): The Elbow Method plots the Within-Cluster Sum of Squares (WSS) against the number of clusters (k). The analyst looks for the “Elbow Point,” where the rate of decrease in WSS slows down significantly. This point represents a balance between model simplicity and clustering accuracy.
• Qualitative Consideration: The decision on k should align with business objectives, such as using the clusters as a starting point for developing a scientific hypothesis and further study. For example, interpreting the clusters to create a meaningful consumer classification that supports targeted marketing requires a qualitative understanding.

Clustering vs. Classification: Uncovering Patterns

17. Explain the difference between clustering and classification when applied to the same dataset. Why might a business choose clustering even when labels (such as “survived”) are available?

• Difference: Classification is a supervised learning task where the goal is to assign data points to predefined categories (e.g., Survived/Not Survived). Clustering is an unsupervised learning task that groups data points into clusters based on their similarities without using predefined labels.
• Why Choose Clustering with Labels: A business might choose clustering even when labels are available because clustering can uncover hidden patterns or structures within the data. This allows the discovery of new groupings (subgroups) and relationships that might be missed by manual analysis or are not reflected in the existing, simplistic labels.

Ensemble Models: Definition and Accuracy

18. In your own words, define what an ensemble model is. Why are ensemble methods such as Random Forest and XGBoost often more accurate than a single decision tree?

• Definition: An ensemble model combines the predictions of multiple models to improve the overall accuracy and robustness. The fundamental idea is that by averaging or combining multiple imperfect models, the final prediction can be closer to the truth.
• Reason for Higher Accuracy:
  • Reduces Overfitting/Variance: Single models (like decision trees) are unstable predictors. Ensemble methods like Bagging (Random Forest) reduce variance and prevent overfitting by averaging these unstable predictions.
  • Reduces Bias/Improves Accuracy: Methods like Boosting (XGBoost) build models sequentially, with each new model learning from the errors of the previous ones. This process turns a collection of “weak learners” into a strong learner, thereby reducing bias and significantly improving predictive accuracy.

Deep Learning and Nonlinear Pattern Detection

19. Explain how deep learning algorithms can detect nonlinear patterns in consumer purchasing or browsing behavior that simpler models might miss. Provide an example.

• Nonlinear Pattern Detection (The Mechanism): Deep Learning (DL) algorithms use Neural Networks (NNs) with multiple Hidden Layers (Multi-Layer Perceptron).
• Feature Extraction and Filtering: During the Feedforward process, the multi-layered architecture performs implicit Feature Extraction, filtering out noise and unimportant data while processing the core information.
• Complex Modeling: By iteratively passing data through multiple weighted layers (and using Activation Functions like ReLU to avoid data loss), DL algorithms discover detailed behavioral patterns, allowing the model to detect the complex, nonlinear relationships in consumer behavior that simpler models would miss.
• Example (The Long Tail): DL enables the platform to effectively predict consumer behavior for the long-tail of niche products, not just the top 10% of popular products. By detecting fine-grained preferences, DL helps platforms profit from these diverse preferences, leading to growing sales and profits from product variety.

Deep Learning Mechanics and Accuracy

Understanding Layers and Nodes:

• Increasing Layers: The process of stacking layers (Hidden Layers) splits the data into smaller pieces. The core goal of the Hidden Layer is Feature Filtering, ensuring that unnecessary data is removed and only the core information is analyzed.

How to Increase Accuracy Rate (Optimization Process):

• Optimization of Weights: The model computes the error and changes the weights during Backpropagation. The main goal is to get the minimum value of the error term using the gradient descent method. When the weights are optimized, the accuracy rate improves.
• Addressing Underfitting: Applying an Activation Function (e.g., ReLU or Sigmoid) resolves the underfitting issue (data loss) that occurs during feedforward.
• Addressing Overfitting: Using the Dropout method randomly selects units with a probability (p) in each layer to reduce the model’s degree of freedom, preventing the model from being too specific.

Analyzing Changes in Model Performance

If performance trends change across models (improves, decreases, or stabilizes), explain why these changes may occur based on deep learning concepts such as overfitting, model capacity, or regularization.

• Decreases in Performance (Low Accuracy):
  • Overfitting: Occurs when the model performs very well on training data but poorly on test data, learning noise instead of true patterns.
  • Underfitting (Data Loss): Can occur during the feedforward process if too much information is lost, requiring an Activation Function (e.g., ReLU).
• Increases in Performance (High Accuracy):
  • Model Capacity: Increasing the number of nodes or layers increases model capacity. If the initial model suffered from underfitting, increased capacity allows the model to learn more complex and nonlinear patterns, thus improving performance.
  • Regularization (Dropout): If the initial model was overfitting, implementing regularization helps reduce the model’s complexity, improving its ability to generalize to unseen data.
• Stabilization: If performance stabilizes after an initial increase, the model may have reached its optimal capacity or the convergence point in the training process.

Comparing Tree-Based vs. Deep Learning Models

If the results of XGBoost or Random Forest outperform the deep learning models, provide possible explanations. Consider factors such as dataset size, feature structure, model interpretability, and suitability for the given problem.