Component 1: Market research — Eduqas A-Level Business

The value is measured by the cost of the mistake avoided. Evidence on customer needs shapes the product, price point, promotional message and channel; it sizes a segment before capital is committed; it produces the sales forecast that break-even and payback rest on; and it makes a bank or investor far more willing to lend. It also supports strategic choice, since in the Ansoff matrix market development and diversification carry the highest risk and are exactly the moves worth testing before committing. Against all that sit the direct cost, the delay while fieldwork runs, samples that are too small or unrepresentative, and the fact that buyers do not always do what they say they will. Coca-Cola reformulated to New Coke in 1985 after favourable blind taste tests and withdrew it within three months, because the research never asked how customers would feel about losing the original.

Field research is first-hand evidence a business collects itself for the question in front of it, through surveys, focus groups, observation or test marketing. Desk research uses data that already exists and was gathered for some other purpose: internal sales records, government statistics from the Office for National Statistics, trade press, competitor accounts filed at Companies House, or a paid report from a firm such as Mintel. The trade-offs run in opposite directions. Desk work is cheap, quick and good for sizing a market, but it is second-hand, may be out of date and is equally available to rivals, so it confers no competitive advantage. Field work is current, confidential and tailored to the decision, but it is slow and costly, and a small or biased sample can be worse than no data at all. Most firms start with the cheap source and use it to aim the expensive one.

The command word is the whole task here, so marks sit in the judgement, not the list. For the firm, research lowers the chance of an expensive launch failure, sharpens segmentation and targeting and gives lenders a forecast they can test; against that sit the direct cost, the delay, response bias, an unrepresentative sample and the risk that a fast market shifts before the findings are written up. Stakeholders read it differently: shareholders see risk reduced but cash spent, employees see job security or unwelcome change to how they work, suppliers see demand signals they can plan capacity around, customers see products closer to what they asked for, and campaigners may object to how personal data is collected. A sound judgement is conditional: research earns its keep when the decision is large, irreversible and unfamiliar, and is poor value in a market already well understood.

One kind of finding is numerical and countable, such as sales volumes, market share percentages or the proportion of a sample who say they would buy at a given price, and it can be graphed, compared over time and extrapolated into a forecast. The other captures opinions, motives and feelings, gathered from focus groups, depth interviews or open questions, and it explains why buyers behave as they do. The first tells a business what is happening, the second tells it why, and most decisions need both: a survey may show that a fifth of shoppers have switched away, but only a discussion group will reveal that they left over packaging waste. The limits matter in evaluation. Numbers can be precise and still wrong if the sample is too small or badly chosen, and opinion based findings come from few people, are open to interviewer bias and cannot safely be generalised to a whole market.

Field methods include questionnaires run online, by post, by telephone or face to face; focus groups; depth interviews; observation of shopper behaviour in store; and test marketing a product in one region before a national launch. Each needs a sample, and the sampling method chosen, random, quota, stratified or cluster, decides how far the results can be trusted beyond the people asked. Desk sources include internal sales records and loyalty card data, electronic point of sale figures, government statistics from the Office for National Statistics, trade journals, published competitor accounts and commercial reports from firms such as Mintel or Statista. Choosing between them is a judgement about cost, speed and accuracy: an online survey is cheap and fast but attracts respondents who select themselves, while a face to face interview yields depth at a far higher cost per response.

Choosing how to research is a budget decision before it is a technical one. The constraints a marker wants named are cost per response, the time before a board needs an answer, the size and accessibility of the target population, whether the question asks how many or asks why, and how quickly the market dates the findings. A start up launching one product cannot fund a national survey, so it buys desk data and runs a focus group of twenty customers, accepting that neither is representative. Tesco can analyse millions of Clubcard transactions and still learn nothing about why shoppers find a store unwelcoming. The trade off is always depth against representativeness, and speed against reliability, so say which constraint actually binds on the named firm.

Primary data is gathered first hand for this decision through surveys, interviews, focus groups, observation and test marketing; existing data already sits in government statistics, trade press, market reports and the firm's own sales records. The examiner's currency is not the list but the comparison. First hand data is current, confidential and tailored, yet slow and expensive per respondent. Existing data is quick and cheap, yet equally available to rivals, collected for another purpose and often out of date. Good answers also separate the method from the sample, because a badly worded questionnaire put to thousands is worse evidence than a carefully run focus group of eight. Judge each option against the decision it feeds and the cost to the firm of getting that decision wrong.

Numerical findings arrive as figures a candidate must read rather than repeat: percentages of respondents, means, ranges and response rates. Interpreting means turning the figure into a decision. If sixty of two hundred shoppers say they would pay a higher price, that is thirty per cent, and thirty per cent of a market of ten thousand is three thousand potential buyers, which is the number that meets or misses the break even volume. Word based findings arrive as quotations and themes from focus groups and depth interviews, and they explain the why behind the percentage. Evaluate by asking who was questioned, how many, how recently, and whether the wording pushed the answer. A large figure drawn from a biased frame is confident and wrong.

A sample is the subset of the target population a firm actually questions, standing in for everyone it cannot afford to ask. The vocabulary a mark scheme rewards is population, sampling frame, sample size and sample method. The frame is the list the respondents are drawn from, and it is where most errors begin, because drawing only from loyalty card holders excludes every shopper who never joined. Size drives cost and confidence together, since accuracy improves with the square root of the number questioned, so quadrupling the respondents only halves the margin of error while quadrupling the bill. Almost no business can question every customer, so the real question is never whether to do this but how much error the firm can afford to carry.

Under the first method every member of the sampling frame has an equal and known chance of selection, usually by numbering the frame and letting software draw the names, which removes the researcher's judgement and allows a margin of error to be calculated. Under the second the researcher sets targets for each segment, say forty responses from women aged sixteen to twenty four, and interviewers fill those cells with whoever is willing, so the structure mirrors the market but selection inside each cell is not random. One is more defensible, more expensive and needs a complete up to date list; the other is fast, cheap and workable on a high street, but supports no statistical confidence and lets interviewers approach the people who look approachable.

The danger is that a systematic tilt in the findings does not announce itself, so the firm acts on evidence it believes is neutral. Name the sources: leading or loaded questions, an incomplete sampling frame, self selection when only enthusiasts answer an online survey, interviewer effects when people give the socially acceptable answer face to face, and timing, such as surveying a supermarket only on a weekday morning. The business cost is real, because tilted data produces confident forecasts that overstate demand, and the firm then buys stock, staff and capacity it cannot sell. The controls worth naming are neutral wording, piloting the questionnaire, drawing respondents at random from a complete frame, anonymity and chasing a decent response rate.

Questioning a subset earns its keep by buying a usable estimate of demand for a fraction of the cost of asking everyone, which lets a firm cut the risk of launching the wrong product at the wrong price. The value differs by group: shareholders want evidence behind a capital investment, managers want a forecast to plan capacity and cash flow, employees want the job security that follows a launch that sells, suppliers want reliable order volumes, and customers end up with products closer to what they said they wanted. The limits are where evaluation marks live. The result is an estimate carrying a margin of error, it records stated intention rather than actual purchase, and it dates quickly in a fast moving market.