Key takeaways

• A consumer insight statement explains why people behave the way they do.
• It has three parts: observation, motivation and tension.
• The tension is the most valuable part, because it shows where your brand can help.
• Always write it in the consumer’s own words.

What is a consumer insight statement?

A consumer insight statement is one or two sentences, written in the consumer’s voice, that explain why people behave the way they do. A good one also reveals a tension that your brand can solve.

The formula I use

Observation + motivation + tension.

Writing in the first person helps a lot. It makes you think like the consumer and use the words they would use.

Four examples

I use these examples whenever I explain this to someone new. The brands and categories are hypothetical.

1. Instant coffee

• Weak: “Consumers want coffee that is quick to make.”
• Strong: “I make instant coffee every morning because I have ten minutes before I leave, but drinking it reminds me that I’ve stopped making time for things I enjoy.”

The strong version tells you about a feeling. The opportunity for the brand is to make those ten minutes feel like a small ritual.

2. Laundry detergent

• Weak: “Mothers want clean clothes for their children.”
• Strong: “I let my kids play in the mud because I want them to have a real childhood, but every stain feels like proof to other parents that I’m not on top of things.”

The real tension here is the fear of being judged by other parents.

3. Savings app

• Weak: “Young people find it hard to save.”
• Strong: “I know I should save because I see friends buying homes, but every time I try, the amount feels too small to matter, so I stop.”

Now the brief becomes clear. The brand has to make small amounts feel meaningful.

4. Food delivery

• Weak: “People order in because they are busy.”
• Strong: “I order dinner on weeknights because I’m exhausted, but I feel guilty that I’m not cooking for my family the way my mother did.”

The brand’s real job is to take away the guilt.

Three tests for your statement

• Is it true? It should be supported by several consumers and more than one source.
• Is it fresh? It should tell your team something they had missed.
• Can you act on it? It should point to what you will say or make differently.

Common mistakes

• Stating a fact. “Most shoppers compare prices” tells you what happens. You still need the why.
• Writing the solution in. “People want a faster app” is your product plan. Go back to the person and their problem.
• Skipping the tension. The “but” is where the brand finds its role, so always include it.
• Using marketing language. Use the words a real person would say at home.

FAQ

What is a consumer insight statement?

A short statement in the consumer’s voice that explains why people behave as they do and reveals a tension a brand can solve.

What is the difference between an observation and an insight?

An observation describes what people do. An insight explains why they do it.

How long should an insight statement be?

One or two sentences is enough.

Related reading

• 5 reasons consumer insights matter in decision-making
• Qualitative vs quantitative research: when to use which
• How to interpret data: 2 habits

If you found this post useful, do share it with your friends and colleagues. If you have any questions, leave a comment below and I will reply.

The post How to Write a Consumer Insight Statement (With Examples) appeared first on PRASHANT AGGARWAL.

The classification comes from psychologist S. S. Stevens, who set it out in his 1946 paper “On the Theory of Scales of Measurement” in the journal Science.

Key takeaways

• Nominal scales name categories. You can count them but not rank them.
• Ordinal scales rank things, but the gaps between ranks are unknown.
• Interval scales have equal gaps but no true zero, so ratios are meaningless.
• Ratio scales have a true zero, so every calculation is allowed.

The four scales at a glance

1. Nominal scale

A nominal scale labels things. The numbers, if any, are codes with no mathematical meaning. If 1 = Delhi and 2 = Mumbai, Mumbai is not twice Delhi. You can report how many respondents fall in each category and which category is the most common. You cannot calculate an average.

2. Ordinal scale

An ordinal scale puts things in order. If a respondent ranks three brands first, second and third, you know the order of preference but not the distance between them. First may be far ahead of second, or barely ahead. The median is a safe summary.

Rating scales such as a five-point agreement scale are strictly ordinal. In practice many researchers treat them as interval and report means. That is a common convention, but it is worth knowing it is an assumption.

3. Interval scale

An interval scale has equal distances between points, but its zero is arbitrary. Temperature in Celsius is the classic case: the gap between 10 and 20 degrees equals the gap between 20 and 30, but 20 degrees is not twice as hot as 10. You can add, subtract and average. You cannot form ratios.

4. Ratio scale

A ratio scale has equal intervals and a true zero that means “none”. Purchase value, number of units, age and income are all ratio measures. A shopper who spends 400 does spend twice as much as one who spends 200. Every arithmetic operation is valid.

Why the scale matters

The scale tells you which analysis is defensible:

• Do not average nominal codes. Report percentages.
• Be cautious with means of ranked data. Check the distribution as well.
• Do not claim “twice as much” unless the measure has a true zero.
• Choose your statistical test to match the scale of the variable you are testing.

Checking the scale before running the numbers takes seconds and prevents conclusions that do not hold.

FAQ

What are the four measurement scales?

Nominal, ordinal, interval and ratio. Each adds a property: naming, order, equal intervals, and a true zero.

Is a Likert scale ordinal or interval?

Strictly it is ordinal, because the gaps between points are not proven equal. Many researchers treat it as interval by convention.

What is the difference between interval and ratio scales?

A ratio scale has a true zero meaning none of the quantity, so ratios are meaningful. An interval scale does not.

Related reading

• Quantitative research types explained
• How to interpret data: 2 habits

The post 4 Measurement Scales Every Researcher and Marketer Should Know appeared first on PRASHANT AGGARWAL.

That explanation is what makes consumer insights valuable in decision-making. Here are five reasons they deserve a seat at the table.

Key takeaways

• An insight explains why people behave as they do, and points to an action.
• Insights reduce the risk of expensive decisions made on assumption.
• They sharpen targeting, pricing and innovation choices.
• A shared insight aligns teams that would otherwise argue from opinion.

1. They reduce the risk of expensive decisions

Launches, price changes and campaigns all cost money before they earn any. Decisions made on internal assumption carry the risk that the assumption is wrong. Testing that assumption with consumers first is almost always cheaper than discovering the error in the market.

2. They explain the why behind the numbers

Sales and share data show what happened. They rarely show why. Two brands can lose the same share for opposite reasons, one on price and one on relevance, and the right response to each is completely different. Without the why, teams tend to treat every decline with the same medicine.

3. They sharpen who you target

Consumers do not choose brands at random. They choose brands that fit their needs, values and circumstances. Understanding those differences lets a business decide which groups to prioritise, what to say to them, and which groups to leave to competitors.

4. They guide pricing and value

Consumers decide what is cheap and what is expensive, relative to what they believe they are getting. Understanding what people value, and what they would trade away, is the basis of sound pricing and pack decisions. It also shows where a price increase will be accepted and where it will not.

5. They point innovation at real needs

New products succeed more often when they solve a problem consumers recognise. Insight work identifies unmet needs, the workarounds people already use, and the compromises they resent. Those are far better starting points than a technology looking for a use.

What makes an insight usable

A useful insight passes three tests:

• It is true. It rests on evidence, not a single anecdote.
• It explains. It gives a reason for behaviour, not a description of it.
• It is actionable. Someone can do something different because of it.

If a statement fails any of the three, it is still an observation, and it needs more work before it should drive a decision.

FAQ

What is a consumer insight?

A consumer insight is an evidence-based explanation of why consumers behave or feel a certain way, which a business can act on.

What is the difference between data and insight?

Data records what happened. An insight explains why it happened and suggests what to do next.

Why are consumer insights important in decision-making?

They reduce the risk of costly mistakes, explain the reasons behind sales data, and guide targeting, pricing and innovation choices.

Related reading

• How to interpret data: 2 habits
• Qualitative vs quantitative research: when to use which
• Zero-click search: what consumer health brands must do now

The post 5 Reasons Consumer Insights Matter in Decision-Making appeared first on PRASHANT AGGARWAL.

Key takeaways

• Experimental designs test cause and effect by applying an intervention.
• True experiments assign participants at random. Quasi-experiments do not.
• Surveys and correlational studies measure without intervening, so they cannot prove causation.
• Cross-sectional research is a snapshot. Longitudinal research follows the same subjects over time.

Types of quantitative research at a glance

Experimental research

True experimental

True experimental research aims to establish causal relationships between variables and uses statistical analysis to support or reject a hypothesis. It is considered the most rigorous experimental design. It always has a control group and a test group, participants are assigned at random, variables are manipulated, and the setting is controlled. A pre-test is optional.

Example: does classical music affect students’ academic performance? The control group studies with no music. The test group studies with classical music, and the researcher can vary its frequency and volume. At the end, the grades of the two groups are compared. If the test group scores significantly higher, the music is the likely cause.

Quasi-experimental

Quasi-experimental research also looks for causal relationships and also applies an intervention. The difference is that participants are not randomly assigned. Groups are often pre-existing, such as a class or a store, and the design usually relies on a pre-test and a post-test.

Example: a teacher wants to test a reading programme for students with low comprehension. A pre-test identifies the students who need it. They go through the programme, which can be varied by duration, schedule, activities and teaching strategy. A post-test then shows whether their comprehension improved.

True vs quasi-experimental: similarities and differences

Both designs test causal relationships, apply an intervention and manipulate variables. They differ in three ways:

• Assignment: random in a true experiment, not random in a quasi-experiment.
• Pre-test: optional in a true experiment, usually central to a quasi-experiment.
• Control group: always present in a true experiment. A quasi-experiment may have a comparison group, but it is not randomly formed, and sometimes there is none.

Non-experimental research

Survey research

Survey research gathers evidence on people’s knowledge, opinions, attitudes and values using questionnaires and interviews. Nothing is manipulated and no intervention is applied. The researcher records, tallies, analyses and interprets the responses.

Example: a survey asks whether people favour a proposed policy. If 75% say yes and 25% say no, the researcher concludes that the majority is in favour.

Correlational research

Correlational research measures the degree of relationship between two or more variables using statistics. As with surveys, nothing is manipulated. It shows whether variables move together. It does not show that one causes the other.

Example: is there a relationship between online gaming and critical thinking? The researcher recruits gamers, measures their gaming habits and their critical-thinking level, and applies a statistical test to see whether the two are related.

Research by time frame

Cross-sectional research

Cross-sectional research gathers data at a single point in time and compares groups on the same variables.

Example: comparing the spending patterns of men and women in their thirties, then comparing the result against other age brackets.

Longitudinal research

Longitudinal research collects data from the same subjects at several points in time, so change can be measured.

Example: a study of a long-term medicine records each patient’s baseline measure before treatment, then monitors it on a fixed schedule to see how the treatment performs. Studies like this take a long time to complete, which is the trade-off for seeing real change.

Choosing the right design

Start from the research objective. If you need to prove that one thing causes another, you need an experiment. If you need to describe a population, a survey will do. If you need to see change, plan a longitudinal design from the start.

FAQ

What are the main types of quantitative research?

Experimental designs (true and quasi-experimental) and non-experimental designs (survey and correlational). Studies are also classed by time frame as cross-sectional or longitudinal.

What is the difference between true and quasi-experimental research?

A true experiment assigns participants to groups at random. A quasi-experiment uses groups that are not randomly assigned.

Does correlation prove causation?

No. Correlational research shows that two variables are related, not that one causes the other.

Related reading

• Qualitative vs quantitative research: when to use which
• 4 measurement scales every researcher and marketer should know
• How to interpret data: 2 habits

The post Quantitative Research: All Types Explained with Examples appeared first on PRASHANT AGGARWAL.

Key takeaways

• Use qualitative research to explore, understand and generate hypotheses.
• Use quantitative research to size, compare and test those hypotheses.
• Qualitative findings are context-specific and should not be reported as percentages of a market.
• The six main qualitative designs are case study, ethnography, phenomenology, historical analysis, content and discourse analysis, and grounded theory.

Qualitative vs quantitative research: the difference

Qualitative research is characterised by the researcher’s direct involvement with participants in a natural setting. Its findings are shaped by the perceptions and experiences of the people taking part, which is its strength and its limit.

When to use which

• You do not yet know the right questions: start qualitative. You cannot write a good survey about something you do not understand.
• You need to size an opportunity or choose between options: go quantitative.
• You have a number you cannot explain: go back to qualitative to find the reason.
• The decision is large: do both, qualitative first to build hypotheses and quantitative second to test them.

How to tell if a proposal uses the wrong method

A few warning signs are worth checking in any research proposal:

• Percentages quoted from a handful of focus groups. Small samples cannot support them.
• A survey proposed for a topic nobody has explored yet. The questionnaire will only measure what the team already assumed.
• A method chosen before the business question is written down. The question should pick the method, never the reverse.

Six types of qualitative research design

1. Case study

A case study is a long, in-depth study of one organisation, situation or phenomenon. It narrows a broad field into one or a few examples, and is useful for checking whether a theory holds in the real world.

Example: does environment influence students’ vocabulary development? A researcher could interview two groups of students from different environments at several points through senior high school, then compare the expressions and vocabulary each group uses.

2. Ethnography

Ethnography studies a particular cultural group to understand its beliefs, practices and way of life. It comes from anthropology. The researcher learns through interviews, observation and immersion, gaining first-hand experience of how the group lives.

Example: observing the practices of a community living far from urban centres. Because the work concerns one group, the findings apply to that group and not to the general public.

3. Phenomenology

Phenomenology examines how people experience a particular phenomenon, treating each person’s experience as unique and meaningful. The aim is to present the lived experience of participants.

Example: understanding how international students adapt to a new language-learning environment, through in-depth interviews or group discussions about their struggles and coping strategies.

4. Historical analysis

Historical analysis examines primary documents to understand how past events connect to the present. It relies on primary sources: direct accounts, archival data, official documents, personal records and eyewitness records.

Example: tracing the development of a country’s education system through policy changes, teaching strategies and materials over time.

5. Content and discourse analysis

This design analyses the text or content of communication. It is typically used to:

• Identify the intentions, focus or communication trends of an individual, group or institution
• Describe attitudinal and behavioural responses to communications
• Reveal patterns and differences in communication content
• Assess the psychological or emotional state of a person or group

Example: studying the language of journalism by analysing articles, journals and audiovisual material, then comparing it with everyday language.

6. Grounded theory

In grounded theory, the researcher does not start with a theory to test. The theory is built from the data as it is collected and analysed, through interviews, observation, fieldwork and the study of texts. The result explains the how and why behind a phenomenon.

Summary

There are more advanced methods, such as semiotics, which I have left out to keep this guide simple.

FAQ

What is the main difference between qualitative and quantitative research?

Qualitative research explores why people think and behave as they do, using small samples. Quantitative research measures how many and how much, using large samples.

When should I use qualitative research?

Use it when you need to explore a topic, understand motivations or explain a number you cannot account for.

Can qualitative research be generalised?

Not statistically. Its findings are context-specific and are best treated as hypotheses to test with quantitative research.

Related reading

• Quantitative research types explained
• 4 measurement scales every researcher and marketer should know
• 5 reasons consumer insights matter

The post Qualitative vs Quantitative Research: When to Use Which (And When Your Agency Is Selling You the Wrong One) appeared first on PRASHANT AGGARWAL.

Key takeaways

• Form a view before you open the data, so the numbers can confirm or challenge it.
• A surprise against your hypothesis is the most useful result, because it tells you where to dig.
• Qualitative data generates hypotheses. Quantitative data sizes and tests them.
• A small gap between two numbers is not a finding until it is statistically significant.

Habit 1: Start with a hypothesis

When I started my career in market research in 2011, my biggest mistake was looking at data with no point of view. I would get so lost in the tables that I forgot what I was looking for. I still see the same thing in people new to the industry.

So before you open any dataset, ask one question: what do I expect this to tell me?

A simple example

Say you are about to review monthly car sales. Before looking, form a view. Perhaps you expect sales to be falling because the economy is weak. Now one of two things happens:

• The data agrees. Sales are falling. You have your answer quickly and can move on to the size of the decline.
• The data disagrees. Sales are rising. Now you are curious, and that curiosity sends you looking for the reason: a new launch, a price cut, a segment behaving differently.

Either way you are reading with purpose. The hypothesis does not need to be right. It needs to exist.

Habit 2: Know what type of data you are reading

The second common mistake is reading qualitative data as if it were quantitative, or the reverse. They answer different questions.

Qualitative research can tell you exhaustively which car segments exist in the market. Only quantitative research can tell you the size and growth of each one. If one segment seems bigger in a set of interviews, that is a hypothesis to test, not a conclusion.

Check significance before you call it a difference

With quantitative data, significance testing matters. Suppose the hatchback segment measures 25% in one sample and 26% in another. It looks like a one-point gap. Depending on the sample sizes, that gap may well sit inside the margin of error, in which case you cannot say the two are different.

Putting the two habits together

Before your next analysis, write one line stating what you expect to see, and one line stating whether the data in front of you can size things or only describe them. Those two lines will save you most of the wandering.

These were the two biggest mistakes I made early in my career, which is why I am sharing them.

FAQ

What is the first step in interpreting data?

Decide what you expect the data to show before you look at it. A hypothesis gives you something to confirm or challenge.

What is the difference between qualitative and quantitative data?

Qualitative data describes what exists and why, using words and observation. Quantitative data measures how much or how many, using numbers.

What does statistically significant mean?

It means a difference between two numbers is unlikely to be explained by sampling chance alone, given the sample sizes involved.

Related reading

• Qualitative vs quantitative research: when to use which
• Quantitative research types explained
• 4 measurement scales every researcher and marketer should know

The post How to Interpret Data: 2 Habits That Stop You Getting Lost in the Numbers appeared first on PRASHANT AGGARWAL.