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Marketing Research: Purpose, Process, and Strategic Value
Business Essentials · Chapter 12: Marketing Foundations and Consumer BehaviorSection 12.2 · lesson 56 of 10512 min · free with a free account
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In this lesson
Research that changes decisions, and how to read it critically.
- Describe the marketing research process
- Distinguish primary, secondary, qualitative, quantitative
- Spot sampling, question, and response problems
- Judge what a research design can establish
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Everything said in the video, section by section (1,824 words). Read along, or search it.
Marketing Foundations and Consumer Behavior
Marketing Research: Purpose, Process, and Strategic Value
Every strategic choice in the last section depends on information about customers. This section is about getting that information, and about not being fooled by it. First, what research is actually for, and the process it follows. Second, the main kinds of research. Secondary and primary, qualitative and quantitative. Third, the ways a study goes wrong. The sample, the questions, and who bothers to answer. And fourth, how to read any research finding critically before acting on it. The test throughout is simple. Could this finding change what the business does? If not, the research has not earned its cost.
What Research Is For
Reducing Uncertainty in a Decision
Marketing research is the systematic gathering and analysis of information to reduce uncertainty in a marketing decision. The operative phrase is reduce uncertainty in a decision. Research is worth doing only when its findings could change what the business does. Some studies have not earned their cost, however interesting they are. A study that confirms what the manager already knew is one. So is a study that could not have come out any other way. And so is one whose result would not alter the decision under consideration. The discipline follows directly. Before designing anything, ask what decision the research is meant to inform. Then ask which different findings would lead to different actions. If every possible answer leads to the same action, skip the study.
The Research Process
6 Steps, and the One That Fails Most
Marketing research moves through a sequence of steps. It begins by defining the problem, and the decision it supports. Next comes determining the information needed to make that decision. Then designing the research, which means choosing the methods and instruments. Then collecting the data, analyzing it, and reporting in a form the decision-maker can use. Problem definition is the stage that determines everything after it. It is also the most common failure in practice. A study built on a poorly defined problem can be executed flawlessly, and still not help. It answers a question the business did not actually need answered.
Symptom or Question?
Marrow Home Goods and Its Falling Traffic
The difference between a symptom and a research question makes this concrete. A symptom is an observed effect, like falling sales, rising complaints, or declining store visits. A research question is a specific, answerable question about the cause, or about a decision that might address it. Take a fictional retailer, Marrow Home Goods, whose foot traffic is falling in one store. Why are our sales down? That is just the symptom restated as a question. It is too broad to design a study around. A workable question narrows it. Has traffic fallen because a competitor opened nearby, or because the store's assortment changed? Has the surrounding population changed? Or is it a decline across the whole chain? Each of these can be investigated with a specific method. And which to investigate first should follow from one test. Which explanation, if true, would call for the most different response?
Secondary and Primary
Use What Exists, Then Collect What Fits
Secondary research uses information that already exists, collected for some other purpose. Sources include internal records, like past sales and customer service logs. They include government statistics from the Census Bureau and the Bureau of Labor Statistics. And industry publications, trade association reports, and data bought from commercial research firms. Its advantages are speed and low cost, because the information is already collected, and often organized. Its problem is that someone else collected it, for their own purpose. It may not measure exactly what you need, may define the customer group differently, or may be out of date. Even so, secondary research is nearly always the sensible first step. It may answer the question at a fraction of the cost, or at least sharpen it before new research is commissioned. Primary research is collected specifically for the question at hand. It costs more time and money, but it fits exactly, because the researcher controls what is measured, of whom, and how. Turn to it when secondary sources do not exist, are too old, or miss the decision on the table.
Qualitative Methods
Answering Why, Not How Many
Qualitative methods gather rich, non-numerical information. They answer why, rather than how many. A depth interview is an extended one-on-one conversation about a person's experiences, motivations, and reasoning. A focus group is a guided discussion among a small group, typically six to ten people. The interaction among participants can surface ideas that would never emerge one-on-one. Observation is the systematic watching and recording of behavior as it naturally occurs. It reveals what people actually do, which can differ from what they would report. These methods are good for generating hypotheses, and for finding the language customers use to describe a problem. They also help in understanding a decision's emotional or social side. But every qualitative finding carries a caution. It does not generalize to a population. A focus group of eight can suggest what a concept means to those eight people, and generate ideas to test. It cannot establish what share of a larger market would respond the same way. Treating a vivid quote as a population estimate is one of the most common misuses of research.
Quantitative Methods and Sampling
Measuring Across a Defined Group
Quantitative methods gather numbers, to measure the size, frequency, or strength of a pattern across a defined group. A structured survey asks a fixed set of questions, usually with set response options. An experiment changes one factor, like a price or a package, while holding others constant. Both can generalize to a larger population, but only if the people studied represent it. That is the job of sampling. The population is the whole group you want to understand. The sampling frame is the actual list you draw from. The frame problem arises when the people reachable through that list differ systematically from the population. A survey of existing customers cannot speak for non-customers, however large it is. They never had a chance to be included. A probability sample gives every member a known, nonzero chance of selection, so precision can be calculated. A non-probability sample, like whoever answers a posted link, is faster and cheaper, but offers no such calculation. Larger probability samples narrow the uncertainty, but slowly. Doubling a sample does not halve it. And no sample size can fix a frame problem. A huge sample from the wrong frame still describes only the frame.
Question Design
Good Samples, Wrong Answers
A well-sampled study can still produce wrong answers if the questions are flawed. A leading question is worded to suggest the desired answer. For instance, asking whether a respondent agrees that a clearly beneficial feature is worth paying for. A double-barreled question asks about two things at once, like whether a product is affordable and reliable. Someone who finds it affordable but unreliable has no way to answer accurately. A question with assumed knowledge presumes a term or experience that not everyone shares. Some respondents end up answering a different question from the one intended. And then there is a persistent gap between what people say they would do, and what they actually do. A respondent may sincerely intend to buy the healthier product, or pay more for an ethical option. Then a real purchase arrives, with its real cost and real alternatives, and they behave differently. So read stated intentions with that gap in mind, and check them against observed behavior where possible.
Non-Response Bias
Response Rate Beats Raw Numbers
Non-response bias occurs when the people who respond differ systematically from those who do not. Then the responses are unrepresentative, even if the original sample was sound. This is why a high response rate matters more than a large number of responses. Picture a survey sent to fifty thousand people that gets five hundred replies. Five hundred sounds like a lot. But the response rate is very low. The five hundred who bothered may be the most satisfied, or the most dissatisfied. Or they may simply be the most available. In any of those cases, they differ from the forty-nine thousand five hundred who did not reply. A smaller survey with a much higher response rate gives more confidence. Its respondents are more likely to resemble the full sample invited, and so the population behind it.
Experiments and Observed Behavior
Why a Control Group Matters
An experiment tests one factor by comparing a group exposed to it with a group that is not. The control group is the comparison group that does not receive the change. It exists so that any difference can be attributed to the change, and not to something else happening at the same time. That something else might be a season, an economic shift, or a competitor's promotion. Take Verano Coffee, a fictional national chain, testing whether a new loyalty app increases visit frequency. It rolls the app out in a sample of stores, the treatment group, while comparable stores carry on without it. If visits rise in treatment stores relative to control stores, over the same period, the app gets the credit. A simple before-and-after comparison could not rule out a seasonal rise that would have happened everywhere. There is another alternative to asking people questions. Observe what they already do. Transaction and observational data, like purchase histories, clicks, and loyalty activity, record actual behavior. That avoids the say-do gap. The governance questions it raises are covered in Chapter eighteen.
Figure 12.2.1
From Population to the People Counted
Here is a research finding traced back to where it came from. It starts with a population of two million four hundred thousand people. The figures are illustrative. The first loss is the sampling frame. Not everyone in the population is reachable through the list. The second loss is selection. The sample is drawn from the frame, by a probability or a convenience method. The third loss is non-response. Only some of those sampled choose to answer. By the end, the finding rests on one thousand one hundred respondents. So a headline that says customers say usually describes only that last, smallest group. At every stage of shrinkage, the finding could drift away from the truth about the population. That is why the questions on the next slide matter.
Reading a Finding
5 Questions, and a Survey of the Wrong People
Before acting on any finding, internal or from an industry report, ask five questions. Who was asked? Does the population and frame match the group the decision concerns? How many? That bears on precision, but not on frame problems. How were they selected? A probability method generalizes more confidently than a convenience one. What was asked? Check for leading, double-barreled, or assumption-laden questions. And who paid? A sponsor's interest does not invalidate a study, but it is a reason to check the other four harder. Now a composite case. Owen, a product manager at Halden Cycle Works, is weighing a pricier electric-assist bicycle line. He emails the existing customer list, and a large majority say they are interested at the proposed price. He recommends full development. But this is the frame problem. Existing customers already value the brand, and will likely favor its new line, whatever the broader market thinks. Non-customers decide whether the line grows the business, and the survey could say nothing about them. A sounder design would sample beyond current customers, or pilot real purchase behavior, and treat this result as encouraging, nothing more.
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