Marketing Research & Info: Methods, Data, Decisions

Core Concepts: What is Marketing Research?

Marketing research is the systematic process of collecting, analyzing, and interpreting data to help marketers make informed decisions. It bridges the gap between consumers and businesses by answering key questions:

• Who are our customers?
• What do they need?
• How can we satisfy them profitably?

Why is it critical?

mindmap
  root((Marketing Research))
    Purpose["Solves business problems"]
    Types["Exploratory, Descriptive, Causal"]
    Methods["Primary vs. Secondary Data"]
    Tools["Surveys, Experiments, AI Analytics"]
    Ethics["Confidentiality, Transparency"]
    Applications["Product Development, Pricing, Promotion"]

1. Types of Marketing Research

Marketing research is classified based on objectives and methods:

Worked Example: NTC’s Internet Speed Study NTC wanted to understand why internet speeds fluctuated in Pokhara. They conducted:

1. Exploratory: Interviews with tech experts to identify potential causes (server load, weather, infrastructure).
2. Descriptive: Surveyed 500 users on speed issues (time of day, location, device type).
3. Causal: Ran experiments by throttling bandwidth at different times to measure impact on user satisfaction. Result: Identified peak-hour congestion as the main issue → led to infrastructure upgrades.

2. Data Collection Methods

Data can be primary (collected firsthand) or secondary (existing sources).

Primary Data Collection Techniques

flowchart TD
  A["Primary Data"] --> B["Surveys"]
  A --> C["Observations"]
  A --> D["Experiments"]
  A --> E["Focus Groups"]
  B --> B1["Online (Google Forms)"]
  B --> B2["Phone/In-person"]
  C --> C1["Mystery Shopping"]
  C --> C2["Traffic Analysis"]
  D --> D1["A/B Testing"]
  E --> E1["Group Discussions"]

Key Tools in Nepal:

• eSewa: Uses online surveys to gather citizen feedback on digital service usability.
• Khalti: Employs clickstream data (user navigation patterns) to improve app UX.
• NEPSE: Relies on secondary data (historical stock trends) for investment reports.

Advantages/Disadvantages:

3. Sampling Techniques

Not all customers can be surveyed—sampling selects a representative subset.

Probability vs. Non-Probability Sampling

mindmap
  root((Sampling Methods))
    Probability["Every unit has a chance"]
      Simple["Random selection"]
      Stratified["Divide into groups (e.g., age, income)"]
      Cluster["Geographic regions"]
      Systematic["Every nth item"]
    Non-Probability["No randomness"]
      Convenience["Easy access (e.g., mall intercepts)"]
      Snowball["Referrals from initial respondents"]
      Quota["Fixed numbers per subgroup"]

Worked Example: Daraz’s Customer Feedback Daraz wanted to test a new delivery feature. They used:

• Stratified sampling: Divided customers by location (Kathmandu, Pokhara, rural areas) and income levels.
• Quota sampling: Ensured 30% responses from each stratum. Result: Identified that rural users faced longer delivery times → led to partnering with local couriers.

4. Data Analysis Tools

Raw data becomes insights through statistical and qualitative analysis:

5. Ethical Considerations

Marketing research must adhere to ethical guidelines to avoid:

• Misleading data (e.g., cherry-picking results).
• Privacy violations (e.g., selling customer data).
• Bias (e.g., leading questions in surveys).

Nepali Case: Ncell’s Data Privacy Scandal (2021)

• Issue: Ncell shared customer call logs with third parties without consent.
• Outcome: Fined by the Telecommunication Regulatory Commission (TRC) and forced to implement GDPR-like policies.
• Lesson: Always obtain informed consent and anonymize data.

In the Real World

1. eSewa’s User Experience Research

   • Idea Used: A/B Testing (causal research)
   • How: Tested two payment button designs (green vs. red) to see which reduced checkout abandonment.
   • Result: Red buttons increased conversions by 12% → adopted globally.

2. Khalti’s Fraud Detection

   • Idea Used: Predictive Analytics (Machine Learning)
   • How: Analyzes transaction patterns to flag suspicious activities (e.g., sudden large transfers).
   • Impact: Reduced fraud cases by 40% in 2023.

3. Daraz’s Supplier Negotiations

   • Idea Used: Secondary Data + SWOT Analysis
   • How: Studied competitor pricing (Amazon India) and supplier reliability before renegotiating bulk orders.
   • Outcome: Secured 15% cost savings on electronics.

Exam Tip

This unit is heavily tested in TU exams through:

1. Scenario-Based Questions (e.g., "How would you design a survey for Pathao to improve driver earnings?").
   • Key: Always structure answers using the marketing research process (problem → method → analysis → action).
2. Data Interpretation (e.g., "Given this table of survey responses, what sampling bias exists?").
   • Key: Look for non-randomness (e.g., only urban respondents) or leading questions.
3. Ethical Dilemmas (e.g., "Is it ethical for NTC to sell user data to advertisers?").
   • Key: Use frameworks like GDPR or Nepal’s Consumer Protection Act (2019).

Common Mistakes to Avoid:

• Ignoring sampling errors in answers (always justify your sample size).
• Confusing primary vs. secondary data (e.g., calling NEPSE reports "primary").
• Overlooking ethical red flags (e.g., not mentioning consent in case studies).

Visual Summary:

flowchart LR
  A["Marketing Problem"] --> B["Choose Research Type"]
  B -->|"Exploratory"| C["Qualitative: Interviews, Focus Groups"]
  B -->|"Descriptive"| D["Quantitative: Surveys, Observations"]
  B -->|"Causal"| E["Experiments: A/B Tests"]
  C --> F["Analyze Themes"]
  D --> G["Run Stats: Mean, Regression"]
  E --> H["Test Hypothesis"]
  F & G & H --> I["Make Data-Driven Decision"]
  I --> J["Ethical Review"]