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.

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

• 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.