How do businesses gather and use data to make marketing decisions?
Primary and secondary market research, qualitative and quantitative data, sampling methods, the use of ICT and big data in marketing, and the interpretation of marketing data including correlation and confidence.
A focused answer to AQA A-Level Business 3.3, covering primary and secondary market research, qualitative and quantitative data, sampling methods, the use of ICT and big data, and the interpretation of marketing data including correlation and confidence.
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What this dot point is asking
AQA wants you to distinguish primary and secondary research, qualitative and quantitative data, describe sampling methods, explain the use of ICT and big data, and interpret marketing data including correlation versus causation and confidence. Questions often give a data extract and ask you to judge how much weight to put on it.
Primary and secondary research
Primary research is tailored and up to date but slow and expensive; secondary research is quick and cheap but may be out of date or not quite fit the firm's question. Firms often start with secondary research to size the market, then use primary research to answer specific questions.
Qualitative and quantitative data
Quantitative data is numerical: it can be measured, compared and analysed statistically, for example sales figures, prices or survey scores. Qualitative data captures opinions, motivations and reasons, often from interviews or focus groups, and adds the depth and the why that numbers alone miss. Strong marketing decisions usually combine the two: the numbers show what is happening, the qualitative data explains why.
Sampling
ICT and big data
Modern firms use ICT and big data, the very large datasets generated by loyalty cards, websites, apps and social media, to spot patterns, personalise marketing, predict demand and target customers precisely. This sharpens decision-making and cuts wasted spend, but raises costs, requires analytical skill and brings data-protection and customer-trust concerns.
Interpreting data
Key cautions when reading data: watch for correlation without causation (two things moving together does not mean one causes the other), the reliability of extrapolation (projecting past trends forward assumes conditions hold), the confidence level attached to a result, and whether the sample was large and representative enough to trust.
Exam-style practice questions
Practice questions written in the style of AQA exam questions on this dot point, with worked answer explainers. The year tag is the paper they imitate, not the source.
AQA 20219 marksAnalyse the benefits to a supermarket of using big data from its loyalty card scheme to inform marketing decisions. (9 marks)Show worked answer →
Big data is the very large datasets generated by loyalty cards, websites and transactions, analysed to find patterns.
Benefits: the supermarket can personalise offers to each shopper based on their actual buying history, raising redemption rates and basket size; it can predict demand more accurately, improving stock decisions and cutting waste of perishables; and it can spot trends (a rising category, a lapsing customer) and act before rivals. Better targeting raises sales and loyalty while reducing wasted promotional spend.
Balance: big data is costly to gather and analyse, raises data-protection and trust concerns, and patterns can mislead (correlation is not causation). Judgement: for a high-volume, low-margin supermarket the gains in targeting and stock efficiency are likely to justify the cost. Markers reward developed, applied benefits (personalisation, demand prediction, targeting) plus a limitation and a judgement.
AQA 20184 marksExplain why a larger sample size generally makes market research findings more reliable. (4 marks)Show worked answer →
A sample is a subset of the target market studied to draw conclusions about the whole.
A larger sample is more likely to include the full range of customer types in the right proportions, so the results are less affected by a few unusual respondents and random variation, reducing sampling error. This raises confidence that the findings reflect the real population, leading to better marketing decisions. The trade-off, worth noting, is that larger samples cost more time and money, so firms balance reliability against cost. Markers reward the link from sample size to reduced sampling error to greater reliability, ideally with the cost trade-off acknowledged.
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Sources & how we know this
- AQA A-level Business (7132) specification — AQA (2015)