Research Problem & Objectives: Definitions, Types, Formulation & Link to Goals

TAKEAWAYS:

• A research problem is a gap in knowledge or a business challenge that requires data-driven solutions (e.g., "Why do 30% of Pathao riders cancel orders?").
• Research objectives are specific, measurable goals derived from the problem (e.g., "Identify top 3 reasons for cancellations in Kathmandu Valley").
• Problems must be feasible, relevant, and actionable—unlike vague questions (e.g., "How can we improve sales?").
• Exploratory research (e.g., focus groups) vs. descriptive research (e.g., surveys) vs. causal research (e.g., A/B tests) serve different problem types.
• SMART objectives (Specific, Measurable, Achievable, Relevant, Time-bound) ensure research delivers value (e.g., "Reduce Daraz cart abandonment by 15% in 6 months").
• Ethical considerations (e.g., privacy in Ncell’s customer data) must guide problem formulation.

1. What Is a Research Problem?

A research problem is a clear, concise statement of the issue or opportunity that marketing research aims to address. It answers:

• What is the core issue? (e.g., declining brand loyalty for Himalayan Beverages)
• Why does it matter? (e.g., 20% drop in repeat purchases in 2023)
• How can data help solve it?

How to Identify a Research Problem

1. Spot the gap: Compare current performance vs. goals (e.g., NTC’s customer satisfaction score of 6.2 vs. target 8.0).
2. Ask "Why?" repeatedly:
   • Problem: "Fewer students use eSewa for tuition payments."
   • Why? → "Users find the app slow during peak hours."
   • Why? → "Server latency spikes at 8–10 AM."
3. Check feasibility: Can you collect data? Is the problem solvable? (e.g., "Why do Nepali tourists avoid NEPSE stocks?" → Too broad; better: "Why do millennials avoid NEPSE IPOs?").

Example: Daraz’s Problem Formulation

Raw Issue: "Sales of electronics drop in monsoon season." Refined Problem: "How does seasonal weather (high humidity, power cuts) affect customer purchase behavior for electronics on Daraz, and what product categories are most impacted?" Why this works:

• Specific (electronics, monsoon, Daraz).
• Measurable (sales data, humidity logs).
• Actionable (adjust inventory, promotions).

flowchart TD
    A["Raw Business Issue"] --> B["Refine: Is it researchable?"]
    B -->|"Yes"| C["Define Problem Statement: Specific (electronics, monsoon, Daraz)"]
    B -->|"No"| D["Reject or Break Down"]
    C --> E["Set Research Objectives: Measurable (sales data, humidity logs)"]
    E --> F["Design Methodology: Actionable (adjust inventory, promotions)"]
    F --> G["Collect & Analyze Data"]
    G --> H["Recommend Solutions"]

2. Characteristics of a Good Research Problem

3. Types of Research Problems

Problems fall into three broad categories, each requiring different research designs:

Real-World Example: NTC’s Exploratory Problem

Problem: NTC noticed a 15% drop in landline subscriptions but didn’t know why. Approach: Conducted exploratory research via:

• Focus groups with households in Chitwan and Dharan.
• Literature review of global telecom trends. Finding: Users cited unreliable service during monsoons and lack of bundled internet plans. Outcome: NTC launched "Fiber + Landline" packages, reversing the trend.

4. Research Objectives: Turning Problems into Goals

Objectives are specific, measurable outcomes derived from the problem. They guide data collection and analysis.

How to Write SMART Objectives

Example: eSewa’s Objectives for a Problem

Problem: "High cart abandonment on eSewa’s mobile app." Objectives:

1. Descriptive: "Identify the top 3 stages where users drop off (checkout, payment, confirmation)."
2. Causal: "Test if reducing form fields from 8 to 5 increases conversions by 15%."
3. Exploratory: "Understand why users prefer Khalti over eSewa for small transactions (<Rs. 500)."

5. Linking Problems and Objectives: A Worked Example

Scenario: Khalti observes that 40% of users abandon transactions at the OTP verification step.

Step 1: Define the Problem

"Why do Khalti users abandon transactions during OTP verification, and how does this vary by device (mobile vs. desktop)?"

Step 2: Set Objectives

Step 3: Design the Study

• Method: A/B test (Group A: original 3 retries; Group B: 5 retries).
• Metrics: Conversion rate, time spent on OTP screen, user feedback.
• Tools: Google Analytics, Khalti app logs, post-transaction surveys.

6. Common Pitfalls in Problem Formulation

1. Too broad: "How can we improve marketing?" → Fix: "Why do Nepali millennials prefer Facebook ads over YouTube for fashion brands?"
2. Unresearchable: "Why do people exist?" → Fix: "What psychological factors drive impulse purchases on Daraz?"
3. Lacking actionability: "Users dislike our brand." → Fix: "Identify top 3 pain points in Ncell’s customer service via NPS surveys."
4. Ignoring ethics: "Track user behavior without consent." → Fix: Use anonymized data and disclose purposes (e.g., eSewa’s privacy policy).

7. Ethical Considerations in Problem Selection

Marketing research must adhere to ethical guidelines, especially when dealing with:

• Consumer privacy: Never collect data without consent (e.g., Ncell’s call detail records).
• Bias: Avoid leading questions (e.g., "Don’t you agree Khalti’s fees are too high?").
• Transparency: Disclose sponsorship (e.g., "This survey is funded by Daraz to study delivery delays").

Example: NEPSE’s Ethical Dilemma Problem: "Why do retail investors avoid NEPSE IPOs?" Ethical Risk: If NEPSE’s research team only surveys active traders, it biases results. Solution: Use a random sample of both active and inactive investors.

8. In the Real World

1. eSewa: Reducing Fraud with Problem-Solving

• Problem: eSewa’s fraud detection system flagged 12% of legitimate transactions as suspicious, causing user frustration.
• Research Objective: "Develop an algorithm to reduce false positives in fraud detection by 30% while maintaining security."
• Method: Used descriptive analytics (past transaction data) + machine learning to identify patterns.
• Outcome: Reduced false flags by 28%, improving user trust.

2. Daraz: Optimizing Product Placement

• Problem: Low conversion rates for electronics in rural Nepal.
• Research Objective: "Test if placing high-demand products (e.g., solar chargers) above the fold increases clicks by 20%."
• Method: A/B test on Daraz’s homepage for 3 weeks.
• Result: Clicks increased by 18%, and sales rose by 12% in Chitwan.

3. Ncell: Understanding Prepaid Loyalty

• Problem: Prepaid users churn faster than postpaid users.
• Research Objective: "Compare usage patterns (calls, data, SMS) between prepaid and postpaid users to identify churn drivers."
• Method: Descriptive research via call detail records (CDRs) and surveys.
• Finding: Prepaid users with <5GB data/month churned 3x faster.
• Action: Launched "5GB Free" prepaid plans, reducing churn by 15%.

9. Exam Tip: How to Score Full Marks

Do’s:

• Define clearly: Start with precise definitions (e.g., "A research problem is a statement of the gap between current knowledge and desired outcomes...").
• Use examples: Tie answers to Nepali businesses (e.g., "Like NTC’s exploratory research on landline decline...").
• Structure logically:
  1. Define the concept.
  2. Give a real-world example.
  3. Explain how it’s applied (e.g., "Daraz used A/B testing to...").
• Link to objectives: Show how problems lead to SMART objectives (use tables or bullet points).
• Highlight ethics: Mention privacy, bias, or transparency where relevant.

Don’ts:

• Vague answers: Avoid "Research problems are important" without elaboration.
• Irrelevant examples: Don’t use global brands (e.g., Coca-Cola) unless the question allows.
• Ignoring methodology: Always connect problems/objectives to how you’d research them (surveys, experiments, etc.).
• Overcomplicating: Stick to 1–2 key points per question. Examiners reward clarity.

Sample Exam Answer Structure

Question: "Define research objectives and explain their importance in marketing research. Use an example from a Nepali company." Answer:

Research objectives are specific, measurable goals that guide marketing research, ensuring the study yields actionable insights. They must be SMART (Specific, Measurable, Achievable, Relevant, Time-bound).

Example: For Khalti’s problem of high OTP abandonment, objectives could be:

1. "Measure the drop-off rate at the OTP step for mobile users in Kathmandu" (Descriptive).
2. "Test if reducing OTP retries from 3 to 5 increases completion rates by 20%" (Causal).

Importance:

• Focus: Directs researchers to collect relevant data (e.g., Khalti’s app logs).
• Feasibility: Ensures objectives can be achieved with available resources.
• Actionability: Results lead to decisions (e.g., Khalti’s algorithm update).
• Ethics: Clear objectives prevent biased or unethical data collection (e.g., avoiding leading survey questions).

Final Note: Always visualize your answer. If asked about problem formulation, sketch a flowchart like the one above. For objectives, use a SMART table. Examiners reward structured, example-rich answers—practice linking theory to Nepali businesses like eSewa, Daraz, or Ncell.