Identify the factors that influence the accuracy of market‑research data, explain why they matter and suggest ways to improve reliability. Understand how accurate research supports business decision‑making and the development of the 4 Ps (product, price, place, promotion).
What is Market Research?
Market research is the systematic collection, recording and analysis of data about customers, competitors and the wider market environment. The information obtained is used to:
Identify market opportunities and threats.
Inform decisions on product, price, place and promotion (the 4 Ps).
Reduce risk when launching new products or entering new markets.
Monitor the performance of existing marketing activities.
Primary vs. Secondary Research
Aspect
Primary Research
Secondary Research
Source of data
Collected directly from respondents (e.g., surveys, interviews, observation)
Existing data such as published reports, government statistics, company records, competitor’s annual report
Cost
Usually higher – design, data collection and analysis are required
Generally lower – data already compiled
Control over data
High – researcher decides what, how and when to collect
Limited – researcher must work with data that already exists
Relevance & timeliness
Can be tailored to the exact research question and current market conditions
May be outdated or not perfectly matched to the research need
Typical examples
Online questionnaire about a new smartphone feature
Face‑to‑face interview with a local retailer
Observation of shopper behaviour in a supermarket aisle
Industry report on UK fashion sales
Office for National Statistics (ONS) data on household income
Competitor’s published sustainability report
Primary Research Methods – Advantages, Disadvantages & When to Use Them
Method
Advantages
Disadvantages
Typical justification (when to choose)
Surveys (questionnaires, online polls)
Reach large numbers quickly; easy to analyse quantitative data; relatively low cost per respondent.
Low response rates can affect reliability; limited depth; risk of poorly worded questions.
Best when you need statistical information from a broad audience – e.g., measuring brand awareness across a country.
Interviews (face‑to‑face or telephone)
Provides detailed, qualitative insights; flexible – can probe further.
Time‑consuming and expensive; interviewer may unintentionally influence answers.
Useful when exploring complex motivations, such as why customers switch brands.
Observation (store visits, traffic counts)
Shows actual behaviour rather than reported behaviour; useful for studying non‑verbal actions.
Cannot reveal motives; observer may affect the behaviour being observed (Hawthorne effect).
Ideal for testing layout changes in a shop or measuring footfall at a new location.
Focus groups
Encourages interaction and idea generation; useful for testing concepts and advertising copy.
Dominant participants can skew discussion; not statistically representative of the whole market.
Effective for early‑stage product development where you need creative feedback.
Sampling – Size, Selection & Limitations
Sample size – Larger samples usually give results that are more representative of the whole population, reducing sampling error. Example: A sample of 400 respondents typically yields a margin of error of ±5 % at a 95 % confidence level.
Sample selection – The way respondents are chosen determines how well the sample mirrors the target market.
Sampling type
How it works
Main limitation
Random sampling
Every member of the population has an equal chance of being selected (e.g., computer‑generated list).
Can be costly and time‑consuming to obtain a truly random list.
Stratified sampling
Population divided into sub‑groups (strata) such as age or income; a random sample is taken from each stratum.
More complex to design; requires accurate information about the size of each stratum.
Convenience sampling
Respondents are chosen because they are easy to reach (e.g., shoppers in a mall).
High risk of bias – the sample may not represent the wider market.
Numerical illustration (optional)
Sample‑size estimate: To achieve a 95 % confidence level with a ±5 % margin of error for a population of 10 000, the required sample size is roughly 370 (using the formula n = N × Z² × p(1‑p) / [e²(N‑1) + Z² × p(1‑p)] where Z=1.96, p=0.5, e=0.05).
Factors Influencing the Accuracy of Market‑Research Data
Sample size – Small samples increase random error.
Sample selection – Biased or non‑random selection reduces representativeness.
Question design – Ambiguous, leading or double‑barrelled questions can mislead respondents.
Data‑collection method – Online, telephone or face‑to‑face each carries specific biases (e.g., self‑selection in online surveys).
Respondent honesty – Social desirability or fear of judgement may cause inaccurate answers.
Timing of research – Seasonal trends, economic cycles or recent events can affect responses.
Researcher bias – The way questions are asked or data are interpreted can unintentionally influence results.
External influences – Media coverage, competitor actions or sudden news events can skew opinions.
Ways to Improve Accuracy (and Why Each Helps)
Use a sufficiently large, randomly selected sample (or stratified where appropriate). Why: Reduces sampling error and improves reliability of conclusions.
Design clear, neutral, single‑concept questions; avoid jargon. Why: Prevents misinterpretation and eliminates leading bias.
Pre‑test or pilot the questionnaire with a small group. Why: Identifies ambiguous wording or technical problems before the main study.
Combine several research methods (triangulation). Why: Cross‑checking findings from different sources increases validity.
Choose an appropriate time for data collection and, where possible, repeat the research. Why: Minimises the impact of temporary events and checks consistency.
Train researchers to follow a standard script and remain neutral when recording or interpreting data. Why: Limits interviewer bias and ensures consistent data handling.
Monitor external events and note any possible influence on respondents’ answers. Why: Allows the researcher to explain anomalies and adjust interpretation.
Data‑Response Tip for Paper 1
When a question asks you to “interpret the chart/graph”, follow this quick checklist:
Identify the variables (e.g., product type, price, sales volume).
Note the scale and units of measurement.
Describe the main trend(s) – rising, falling, stable.
Point out any peaks, troughs or outliers and suggest a possible reason.
Link the information back to the research question or the marketing decision it could influence.
Linking Market Research to the Marketing Mix (4 Ps)
Accurate market‑research data helps businesses make informed choices about each element of the marketing mix:
Product – Identify features customers want, test prototypes, gauge satisfaction. Research factor link: Poor question design can hide true preferences, so clear questions improve product decisions.
Price – Determine willingness to pay, test price‑sensitivity, compare with competitors. Research factor link: Timing matters – price sensitivity may vary seasonally.
Place (Distribution) – Find the most convenient buying locations, assess channel performance. Research factor link: Sample selection must reflect the geographic market to avoid location bias.
Promotion – Evaluate the effectiveness of advertising messages, preferred media, and promotional offers. Research factor link: Data‑collection method (e.g., online vs. face‑to‑face) influences the type of feedback on promotional material.
Thus, reliable research underpins strategic decisions such as product development, market entry, pricing strategy and promotional planning (Sections 3.3 & 3.4 of the syllabus).
Unit‑Wide Road‑Map (Quick Checklist)
Role of marketing, competition and market segmentation (Section 3.1).
Methods of market research, primary vs. secondary, sampling, accuracy factors (Section 3.2).
Application of research to the 4 Ps and to emerging e‑commerce trends (Section 3.3).
Developing a marketing strategy, legal controls and entry into foreign markets (Section 3.4).
Suggested diagram: Flowchart of steps to improve accuracy of market research data – Define objective → Choose sample (size & selection) → Design questionnaire (pilot test) → Collect data (method) → Analyse (check for bias) → Review & refine.
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