Marketing Basics – Core Commerce (B.Sc. Computer Science)
Objective
Equip CS students with a rigorous, first‑principles understanding of marketing.
Emphasise analytical tools, formal models, and edge‑case reasoning that are relevant for data‑driven product design, platform economics, and AI‑enabled advertising.
Table of Contents
- Why Marketing Matters in CS
- Foundational Concepts
- 2.1 Market & Consumer Definitions
- 2.2 Utility, Preferences & Demand
- 2.3 Information Asymmetry & Signalling
- The Marketing Mix (4 Ps & 4 Cs)
- Segmentation, Targeting & Positioning (STP)
- Consumer Behaviour Models
- Marketing Research & Experimental Design
- Digital & Data‑Driven Marketing
- Metrics, ROI & Cost‑Benefit Analysis
- Advanced Topics & Edge Cases
- Challenging Problem
- References & Further Reading
1. Why Marketing Matters in CS
| Perspective | Why Marketing is essential for CS |
|---|---|
| Product Success | A technically sound product can still fail if it does not reach its intended users. |
| Platform Design | Platform dynamics (two‑sided markets) require careful incentive alignment. |
| Data Science | Marketing experiments are a natural laboratory for A/B testing, causal inference, and reinforcement learning. |
| Ethics & Trust | Persuasive technologies raise privacy & bias concerns that must be understood from a marketing standpoint. |
| Career | CS graduates in product, growth, or data roles need solid marketing fundamentals to communicate with stakeholders. |
2. Foundational Concepts
2.1 Market & Consumer Definitions
- Market: A set of potential buyers and sellers interacting over a good or service.
Formalization: ( \mathcal{M} = { (i, j) : i \text{ buyer}, j \text{ seller} } ). - Consumer: An agent ( i ) with preferences ( \succ_i ) over bundles ( x \in X ).
Utility function: ( U_i(x) ).
2.2 Utility, Preferences & Demand
- Utility Maximization:
[ \max_{x \in X} U_i(x) \quad \text{s.t. } p \cdot x \leq B_i ] where ( p ) is price vector, ( B_i ) budget. - Demand Function: ( D(p) = { x : U_i(x) \text{ maximized} } ).
- Elasticity:
[ E = \frac{\partial D}{\partial p} \cdot \frac{p}{D} ] Interpretation: Responsiveness to price changes.
2.3 Information Asymmetry & Signalling
- Adverse selection: Sellers know more about product quality than buyers.
- Moral hazard: Buyers may act differently after purchase.
- Signalling: Sellers convey quality via warranties, certifications, or price.
- Screening: Buyers design mechanisms (e.g., trial periods) to elicit information.
3. The Marketing Mix (4 Ps & 4 Cs)
| P | Traditional View | Modern Interpretation |
|---|---|---|
| Product | Tangible goods + features | Platform + Service + User Experience (UX) |
| Price | Cost‑plus, competitor‑based | Dynamic pricing, price elasticity models, price discrimination |
| Place | Physical distribution channels | Digital distribution, App Stores, Cloud APIs |
| Promotion | Advertising, sales, PR | Content marketing, SEO, Programmatic ads, Influencer |
4 Cs (Customer‑Centric View)
- Customer (needs, wants)
- Cost (total cost of ownership)
- Convenience (accessibility)
- Communication (interactive, two‑way)
4. Segmentation, Targeting & Positioning (STP)
4.1 Segmentation
- Statistical Clustering: k‑means, Gaussian Mixture Models (GMM), hierarchical clustering.
- Latent Variable Models: Latent Dirichlet Allocation (LDA) for content preferences.
- Feature Selection: Use domain‑specific features (e.g., app usage logs, demographic data).
Rigorous Reasoning: Test cluster stability via silhouette score, gap statistic, or Bayesian Information Criterion (BIC).
4.2 Targeting
- Profitability Estimation: Predict lifetime value (LTV) per segment.
- Cost‑Benefit Analysis: Compute expected incremental revenue vs. marketing spend.
4.3 Positioning
- Perceptual Mapping: Multidimensional scaling (MDS) to place products in consumer perception space.
- Competitive Advantage: Use Porter's Five Forces framework to identify niche gaps.
5. Consumer Behaviour Models
| Model | Core Idea | Mathematical Form | |-------|-----------|