2.3 Market Research

Before a company spends millions launching a new product, they want some proof that people will actually buy it. That's where market research comes in. It's how businesses figure out who their customers are, what those customers want, and whether a product idea is even worth pursuing. The same research also helps companies with products already on shelves stay relevant as customer tastes shift.

Why Businesses Conduct Market Research

Market research is the process of collecting detailed information about markets, products, and customer behavior to guide marketing decisions. Think of it as a business doing its homework before making big moves. Without it, companies are basically guessing, and guessing with millions of dollars on the line is a bad strategy.

Quantitative vs. Qualitative Data

Researchers collect two main types of data, and the difference between them matters.

Quantitative data is numerical. It's anything you can count or measure, and it answers questions like how many, how much, and how often. Examples:

• 73% of Gen Z shoppers buy clothes online at least once a month
• Average customer spends $42 per visit at Chipotle
• 1.2 million users signed up in Q3

Qualitative data is descriptive. It's collected in words and images, and it answers why and how. Examples:

• "I switched to Liquid Death because the cans look cool and I felt weird drinking from plastic bottles."
• A video of shoppers hesitating in the cereal aisle, picking up three boxes before choosing one.

Quantitative tells you what's happening. Qualitative tells you why it's happening. Most strong research uses both.

Researching a New Product: Desirability, Feasibility, Viability

When a business is developing something new, market research helps them check three boxes before they commit serious money:

• Desirability: Do customers actually want this? A product is desirable when it creates value by achieving problem-solution fit, meaning it solves a real problem people have. Juicero (the $400 juice press) failed the desirability test because people could just squeeze the juice packets with their hands.
• Feasibility: Can we actually build it? A product is feasible when the business has the resources, technology, expertise, and time to produce it. A small startup might love the idea of building electric jets, but it's not feasible.
• Viability: Can we make money from it? A product is viable when it has the potential to be profitable. If it costs $50 to make and customers will only pay $30, it's not viable, no matter how much they love it.

All three need to be true. A product can be desirable and feasible but not viable, and it'll still fail.

Research for Existing Products

Even companies with successful products keep researching. Customer needs change, competitors launch new stuff, and trends shift fast. Netflix runs constant research to see what shows keep people subscribed. Doritos uses research to decide which new flavors to test. The goals are usually:

• Understand changing customer needs and wants
• Spot chances for product innovation
• Develop strategies to retain current customers or grow market share

Secondary-Source Research

There are two big buckets of research: secondary and primary. Start with secondary because it's cheaper and faster.

Secondary-source research means gathering information that already exists somewhere else. You're not collecting new data yourself, you're pulling from external sources like:

• Government publications (Census Bureau, Bureau of Labor Statistics)
• Commercial databases (Statista, Nielsen, IBISWorld)
• Academic journals and university research
• Industry reports and trade publications
• Competitor websites and annual reports

Secondary research can tell you a lot:

• Market size in dollars (the U.S. pet food market is about $50 billion) and in total customers (around 90 million U.S. households own a pet)
• Market trends (plant-based foods growing 8% per year)
• Market segments (who's buying what, broken down by age, income, region, etc.)
• Factors that influence customer decisions

It's also where businesses scope out rival businesses. You can learn a competitor's product lineup, pricing, and recent moves without spending a dime on interviews.

Secondary research often connects to PESTEL factors: Political, Economic, Social, Technological, Environmental, and Legal forces shaping a market. For example, if you're launching an electric scooter rental service, you'd want secondary research on local laws (Legal/Political), gas prices (Economic), and attitudes toward sustainable transport (Social/Environmental).

Why start here? It's way more cost-effective than running your own studies. You use secondary research to get the lay of the land, then use primary research to answer the specific questions secondary sources can't.

Primary-Source Research

Primary-source research is data you collect yourself, directly from customers or potential customers. You use it to test a business hypothesis: an assumption about a customer, product, or market that you want to check before betting on it.

A hypothesis might look like:

• "Gen Z customers will pay $5 more for shoes made from recycled materials."
• "Our app users prefer a dark-mode interface over a light one."
• "Parents will buy our snack pouches more often if we add a resealable cap."

You pick a research method based on what kind of data you need.

Surveys

Use surveys when you need a lot of quantitative data from a big group. Surveys can reach thousands of people quickly through email, social media, or in-app forms. They're great for questions like "On a scale of 1 to 10, how likely are you to recommend our product?"

Strength: large sample sizes that reflect a whole population. Weakness: limited depth. You won't get the deep "why" behind answers.

Focus Groups and Interviews

Focus groups (small group discussions, usually 6 to 10 people) and one-on-one interviews are used when you need rich, qualitative data from a small number of highly engaged customers or potential customers. A moderator can ask follow-up questions like "Why did that bother you?" or "Can you describe the last time that happened?"

Lego famously uses focus groups with kids and parents to understand how new sets actually get played with, not just whether people would buy them.

Strength: depth and nuance. Weakness: small sample, so the findings might not represent everyone.

Experiments and Observations

These methods focus on behavior, not opinions. Sometimes what people say they'll do and what they actually do are very different.

• Experiments happen in a controlled environment where researchers change one variable and measure the result.
• Observations happen in a natural environment, watching customers do their thing without interfering. A grocery chain might watch how shoppers move through the produce section to figure out where to place new items.

A/B Testing

A/B testing is a specific type of experiment where you show two viable alternatives to real customers and measure which one performs better. It's huge in tech and ecommerce.

Example: Spotify wants to know if a green "Subscribe" button gets more clicks than a black one. They show version A (green) to half their users and version B (black) to the other half, then compare click rates. Whichever wins, they roll out to everyone.

A/B testing gives you authentic, real-world data on specific preferences.

Avoiding Skewed Data

Bad research is worse than no research because it leads to confident wrong decisions. Two big things to watch:

1. Sample size and composition: Your sample needs to be large enough and representative of the population you're studying. Surveying 20 of your friends about a product idea isn't going to tell you what the actual market thinks.
2. Unbiased questions: Questions need neutral phrasing. Asking "How much do you love our amazing new feature?" pushes people toward a positive answer. A better version: "How would you rate this feature on a scale of 1 to 5?"

When Data Isn't Enough

Even great research doesn't guarantee success. Sometimes the data is limited, unclear, or contradictory. New Coke was launched in 1985 after extensive taste tests showed people preferred it. Customers hated the change anyway because the research missed the emotional attachment to the original. Businesses often have to make decisions with imperfect information, then adjust as they learn more.

Data Visualizations

Once you've got research findings, you need to communicate them to people who'll make decisions. Raw spreadsheets don't cut it. Data visualizations turn complex numbers into patterns people can see at a glance, which helps stakeholders make evidence-based decisions.

Picking the right chart matters. Each one is built for a specific kind of story.

Bar Charts

Bar charts compare individual data points side by side. Use them when you want to show differences between separate categories.

Good uses:

• Annual revenue for Nike, Adidas, Puma, and Under Armour in 2024
• Monthly sales at one store across six months

The bars make it easy to spot which category is biggest or smallest.

Stacked Bar Charts

Stacked bar charts compare totals and show the subcategories that make up each total. Each bar is divided into segments.

Example: A bar chart showing Apple's total revenue per year, with each bar split into iPhone, Mac, iPad, Wearables, and Services. You see both how total revenue changed and how the mix of product lines shifted.

Line Graphs

Line graphs show trends over time. The x-axis is usually time (months, quarters, years) and the y-axis is the value you're tracking.

Good uses:

• Netflix's number of subscribers each year from 2015 to 2024
• Daily active users on TikTok over a 12-month period

If you want to show "this is going up, down, or staying flat over time," use a line graph.

Pie Charts

Pie charts show part-to-whole relationships. They're great when you want to communicate how a total is divided up.

Good uses:

• Market share in the U.S. soda market (Coca-Cola 45%, Pepsi 26%, Dr Pepper 23%, others 6%)
• Breakdown of a company's expenses by category

Pie charts work best with a small number of slices. If you have 15 categories, a pie chart turns into a confusing color wheel and a bar chart works better.

The big idea: match the chart to the message. Comparing categories? Bar chart. Showing change over time? Line graph. Breaking down a whole? Pie chart. Need to do two of those at once? Stacked bar chart.