Case Analysis & Marketing Research Applications: Real-World Problem-Solving

Core Concepts & Definitions

1. What is Case Analysis in Marketing Research?

Case analysis is the structured examination of a real business problem using marketing research tools to diagnose issues, evaluate alternatives, and recommend solutions. Unlike theoretical research, it focuses on applied problem-solving by:

• Extracting key data from a scenario (e.g., sales decline, brand perception).
• Identifying root causes (e.g., poor targeting, supply chain inefficiencies).
• Testing hypotheses (e.g., "Will a discount increase Pathao’s rider retention?").
• Proposing data-backed solutions (e.g., rebranding, loyalty programs).

Why it matters: Marketing research is useless without actionable insights. Case analysis bridges the gap between theory and practice, teaching students to:

• Spot hidden patterns in messy data (e.g., NTC’s declining SMS revenue).
• Avoid confirmation bias (picking data that fits preconceptions).
• Justify decisions to stakeholders (e.g., investors, managers).

2. The Case Analysis Framework

Use this 5-step process to dissect any marketing case (e.g., Khalti’s failed UPI push, Daraz’s warehouse delays):

graph TD
    A["1. Problem Identification"] --> B["2. Data Collection & Analysis"]
    B --> C["3. Root Cause Analysis"]
    C --> D["4. Alternative Solutions"]
    D --> E["5. Recommendation & Implementation Plan"]
    E -->|"Feedback Loop"| A

Step 1: Problem Identification

Ask:

• What’s the core issue? (e.g., "Nepal’s dairy farmers struggle to sell excess milk to hotels.")
• Who is affected? (farmers, hotels, consumers).
• Is it a symptom or root cause? (e.g., poor cold chain → spoilage → wasted milk).

Example:

Problem: 30% of milk spoils before reaching Kathmandu hotels due to lack of refrigerated transport.

Step 2: Data Collection & Analysis

Gather quantitative (numbers) and qualitative (opinions) data:

Key Metric: Spoilage rate = (Wasted milk volume / Total milk produced) × 100.

3. Root Cause Analysis (RCA)

Use the 5 Whys Technique to dig deeper:

1. Why is milk spoiling? → No refrigeration.
2. Why no refrigeration? → High cost of solar-powered coolers.
3. Why high cost? → Subsidies only cover 20% of rural areas.
4. Why limited subsidies? → Government prioritizes urban infrastructure.
5. Why urban priority? → Political lobbying by city-based dairy firms.

Visual Tool:

4. Alternative Solutions

Propose 3–5 options and evaluate them:

Worked Example: eSewa’s Customer Drop-Off Problem: eSewa sees a 40% drop-off after users add items to cart but don’t checkout. RCA:

• Primary Data: User surveys reveal fear of hidden charges (e.g., delivery fees).
• Secondary Data: Competitor analysis shows Khalti adds fees upfront. Solution: Transparency redesign → Show all costs at cart stage. Result: Drop-off rate fell to 12% after A/B testing.

5. Recommendation & Implementation

Write a 1-page memo with:

1. Executive Summary: 1-line problem + solution (e.g., "To reduce milk spoilage, implement a cooperative cold storage model with NABARD subsidies.").
2. Data Support: Key stats (e.g., "Current spoilage: 30%; proposed model reduces it to 5%.").
3. Action Plan:
   • Phase 1: Pilot in 5 districts (6 months).
   • Phase 2: Scale with NABARD partnership.
4. Budget: $200K for initial coolers + training.
5. Risks: Farmer resistance → Solution: Offer microloans for participation.

In the Real World

1. Daraz’s Supply Chain Gaps (Nepal)

Problem: Daraz’s "Same-Day Delivery" promise fails in Pokhara due to last-mile inefficiencies. Case Analysis:

• Data: 60% of orders take >24 hours; 80% of delays occur in rural areas.
• Root Cause: Understaffed delivery hubs + no real-time tracking.
• Solution: Partnered with Pathao’s rider network for dynamic routing. Outcome: On-time delivery improved by 45%.

2. Ncell’s Prepaid Tariff Wars

Problem: Ncell’s "Happy Hours" promotion saw low redemption (only 30% of subscribers used it). Case Analysis:

• Data: Surveys showed users didn’t know about the offer.
• Root Cause: Poor SMS marketing + confusing timing (e.g., "Happy Hours" at 3 AM).
• Solution: WhatsApp reminders + daytime slots. Outcome: Redemption rate jumped to 78%.

3. NEPSE’s Investor Confidence Crisis

Problem: Trading volume on NEPSE dropped 20% after a major bank’s fraud scandal. Case Analysis:

• Data: Investor sentiment surveys revealed lack of transparency in disclosures.
• Root Cause: Delayed audit reports + no real-time fraud alerts.
• Solution: Blockchain-based audit trails (piloted in 2023). Outcome: Volume recovered to pre-scandal levels within 6 months.

Specialized Research Areas in Cases

A. Product Research

Goal: Assess whether a product meets customer needs. Example: Khalti’s UPI Failure

• Problem: UPI adoption stalled at 5% despite government push.
• Research: Focus groups found users preferred cash-on-delivery for trust.
• Solution: Launched "Khalti Cash" (digital + physical hybrid).

Key Metrics:

B. Advertising Research

Goal: Measure ad effectiveness. Example: NTC’s "Internet for All" Campaign

• Problem: Low awareness of NTC’s fiber deals in rural areas.
• Research: A/B tested ads:
  • Ad A: "Fast Internet for Rs. 999!" (urban-focused).
  • Ad B: "Study from Home – No More Tuition Fees!" (rural appeal).
• Result: Ad B drove 60% more sign-ups in hill districts.

Ad Evaluation Framework:

C. Distribution Research

Goal: Optimize supply chains. Example: Pathao’s Traffic Congestion in Kathmandu

• Problem: Riders spent 40% of time stuck in traffic.
• Research: GPS data showed hotspots (e.g., Thapathali intersection).
• Solution: Dynamic rerouting via AI + incentives for off-peak deliveries. Result: Delivery time cut by 25%.

Ethical Considerations in Case Analysis

Cases often involve sensitive data or conflicts of interest. Key ethical guidelines:

1. Confidentiality: Never disclose proprietary data (e.g., Daraz’s rider wages).
2. Bias Avoidance: Use random sampling (e.g., surveying 1,000 Khalti users, not just Kathmandu-based ones).
3. Transparency: Disclose funding sources (e.g., "This case was funded by NABARD").
4. Cultural Sensitivity: Avoid assumptions (e.g., don’t assume all Nepali farmers are illiterate).

Example Dilemma: A bank asks you to analyze why their loan default rate is high. You find the root cause is predatory lending to marginal farmers—but the bank wants you to blame "poor repayment culture." Solution: Present both findings but highlight ethical concerns in your report.

Exam Tip: How to Score Full Marks

1. Structure Your Answer Like a Consultant

Examiners love clear, actionable answers. Use this template:

"The case reveals [Problem] due to [Root Cause], as evidenced by [Data]. Three solutions are viable: [A], [B], [C]. Recommend [Best Option] because [Reason], with a phased rollout as follows: [Steps]."

2. Use Real-World Examples

• Never write generic answers like "Marketing research helps businesses understand customers."
• Instead: "Like eSewa’s drop-off analysis, case studies reveal that hidden fees (not price) drive cart abandonment—solvable via upfront transparency."

3. Visuals = Easy Marks

• Draw a flowchart for processes (e.g., Daraz’s delivery steps).
• Use tables to compare solutions (e.g., solar coolers vs. mobile vans).
• Label diagrams (e.g., NTC’s network coverage map).

Example Question: "Analyze the factors affecting marketing research decisions for a Nepalese FMCG brand." Top-Mark Answer: Explanation:

• Market Factors: For FMCG brands like Himalayan Drugs, research must track health trends (e.g., demand for herbal products post-COVID).
• Company Factors: A Rs. 500K budget limits primary research; use secondary data (e.g., Nepal Rastra Bank reports).
• Environmental Factors: Monsoon delays affect rural distribution—research must account for seasonal gaps.

4. Avoid Common Pitfalls

• ❌ "Marketing research is important." → Vague.
• ✅ "For Pathao, research identified that rider fatigue (not traffic) caused 60% of delays—solvable via shift-based incentives." → Specific + actionable.

5. Memorize These Key Terms

Final Practice Question

Question: "A bank in Nepal wants to launch a digital loan product but sees low adoption. Using case analysis, evaluate two possible reasons and propose solutions." Model Answer:

1. Reason A: Lack of Digital Literacy

   • Data: 60% of rural applicants fail the online form.
   • Solution: Partner with Ncell’s "Digital Sakhi" program for training.

2. Reason B: Distrust of Digital Transactions

   • Data: Focus groups cite fear of data leaks (e.g., 2021 bank hack).
   • Solution: Offer biometric verification + insurance against fraud.

Recommendation: Pilot in Bara District (low literacy + high mobile penetration) with both solutions. Track adoption via A/B testing.

Visual Summary: