UNIT 4: Marketing Research : DATA COLLECTION AND ANALYSIS

Characteristics of Secondary Data

1. Readymade Information: Already collected, processed, and synthesized by an external party.

2. Processed Form: Presented in tabulated, structured, or published formats.

3. Past Events: Refers exclusively to historic issues, events, or relationships.

4. Economical: Saves significant time and money compared to primary gathering.

Internal vs. External Sources of Secondary Data

A. Internal Sources: Accounting records (invoices), sales force reports, internal research reports, internal subject experts.

B. External Sources: Libraries, literature, periodicals, census data, trade associations (FNCCI, Chamber of Commerce), government departments (CBS), commercial vendors, international orgs (IMF, WTO, ADB), ad agencies, external experts.

Limitations / Problems of Secondary Data

1. Mismatch of collection purpose with current study needs.

2. Difficult or expensive access.

3. Unsuitable aggregation level or definitions.

4. Lack of control over original data quality.

5. Initial biases influencing how data is presented.

6. Incompatible measurement units or class boundaries.

7. Publication Currency: Long time lags between data collection and final publication.

What are the Types of Observation Methods?

1. Participative Observation: Observer assumes a active role inside the group.

   • Pros: Natural behavior recording, depth, environmental context.

   • Cons: Selective perception, narrow perspective, emotional involvement.

2. Non-Participative Observation: Observer discloses purpose but maintains distance without direct interaction.

3. Electronic Observation: Uses electronic devices (time-lapse cameras, CCTV, video recorders) to record behavior with high reliability.

Merits and Demerits of Questionnaire Surveys

• Merits:

  1. Versatile: Applicable to brand tracking, segmentation, profiles.

  2. Suitable for Large Samples: Rapidly captures opinions and demographics.

  3. Low cost & high speed.

  4. Free from interviewer bias & low time pressure on respondents.

  5. Wider geographical coverage (especially online).

• Demerits:

  1. High non-response rates (especially mail surveys).

  2. High costs in illiterate populations requiring interviewers.

  3. Potential unreliability and incomplete/ambiguous responses.

Types of Questionnaire Surveys

1. Mail Survey: Dispatched and returned via mail; includes envelope, cover letter, questionnaire, return envelope, and incentive.

2. Interviewer-Administered Survey: Interviewer reads questions, explains context, and records responses (expensive).

3. Self-Administered Survey: Respondent fills out form independently (reduces non-response cost effectively).

4. Electronic Survey:

   • Telephone Survey: Conducted via phone call.

   • Internet Survey: E-mail surveys or web-based survey forms.

10 Steps in the Questionnaire Development Process

1. Information Need: Specify precise data required (e.g., brand association, quality perception).

2. Type of Survey: Match layout to mail, phone, or web mode.

3. Content: Eliminate unnecessary, unanswerable, or embarrassing questions.

4. Question Wording: Ensure clear, simple, unbiased, non-double-barreled phrasing.

5. Response Formats: Choose open-ended, closed-ended, multiple choice, or scaled options.

6. Question Sequence: Order by basic info $\rightarrow$ classification info $\rightarrow$ identification info.

7. Pre-coding: Assign numerical codes to questions/answers for computer entry.

8. Layout: Choose vertical or space-saving horizontal formats.

9. Pre-testing: Conduct small pilot tests under real conditions to check language/flow.

10. Reproduction: Final corrections and printing/binding.

What is Data Analysis & Statistical Measures Used?

• Concept: Ordering, breaking down, and manipulating data to answer research questions or test hypotheses.

• Key Statistical Measures:

  1. Central Tendency: Mean, Median, Mode.

  2. Dispersion: Range, Mean Deviation, Standard Deviation, Variance, CV.

  3. Association: Correlation, Regression, Chi-Square, Factor Analysis.

  4. Variance: One-way/Two-way ANOVA, MANOVA, ANCOVA.

  5. Time Series Analysis: Trend, seasonal, cyclical, and erratic variations.

Uses of Statistical Analysis in Marketing Research

1. Understanding Customers: Uncovers patterns in needs, tastes, and habits.

2. Market Segmentation: Groups markets by demographic and lifestyle factors.

3. Demand & Sales Analysis: Evaluates trends to forecast future demand.

4. Product & Price Decisions: Tests feature preferences and price elasticity.

5. Measuring Promotion: Evaluates ad/campaign impact pre- and post-launch.

6. Customer Satisfaction: Identifies reasons for brand switching or loyalty.

7. Evaluating New Products: Uses test market data to drop or continue items.

8. Risk Reduction: Grounding managerial decisions in facts over guesswork.

4 Measurement Scales Used in Marketing Research

• Nominal Scale (Non Metric): Assigns identity numbers or categories (e.g., jersey numbers, agree/disagree).

• Ordinal Scale (Non Metric): Ranks items in order without equal intervals (e.g., student exam ranks).

• Interval Rating Scale (Metric): Ranks items at equal distances without a true absolute zero (e.g., Likert scales, semantic differential).

• Ratio Scale (Metric): Highest measurement level with equal intervals and an absolute zero point (e.g., weight, distance, length, revenue).