Welcome to Marketing Research!
Ever wondered why some products become massive hits while others disappear from the shelves in weeks? It’s rarely luck! Successful businesses spend a lot of time "getting inside the heads" of their customers. In this chapter, we will explore how businesses use market research and data to make smart decisions. Don't worry if the numbers look a bit scary at first—we will break them down step-by-step!
1. The Basics: Primary and Secondary Research
To understand a market, businesses need information. There are two main ways to get it:
Primary Research (Field Research)
This is new data collected for a specific purpose. It’s like being a detective and going out to find fresh clues.
Examples: Surveys, interviews, focus groups, or observations.
Pros: It’s up-to-date and specific to your business.
Cons: It can be very expensive and time-consuming.
Secondary Research (Desk Research)
This is existing data that someone else has already collected.
Examples: Government statistics (ONS), newspaper articles, or market reports (like Mintel).
Pros: It’s often free or cheap and quick to find.
Cons: It might be out of date or not exactly what you need.
Qualitative vs. Quantitative Data
When doing research, you'll get two types of "answers":
1. Quantitative Data: Numerical data (think "Quantity"). It tells you how many or how often. Example: "70% of people like chocolate ice cream."
2. Qualitative Data: Descriptive data about feelings and opinions. It tells you why. Example: "People like chocolate ice cream because it reminds them of summer holidays."
Key Takeaway
Primary is fresh but pricey; Secondary is second-hand but fast. Quantitative is about numbers; Qualitative is about "the why."
2. The "Must-Know" Calculations
You need to be able to measure how well a business is doing in its market. Here are the three most important formulas:
Market Size: The total value or volume of sales in the whole market.
Market Share: The percentage of the total market that one business owns.
\( \text{Market Share \%} = \frac{\text{Sales of one business}}{\text{Total Market Sales}} \times 100 \)
Market Growth: How much the whole market has grown over time.
\( \text{Market Growth \%} = \frac{\text{New Market Size} - \text{Old Market Size}}{\text{Old Market Size}} \times 100 \)
Sales Growth: How much an individual business's sales have grown.
\( \text{Sales Growth \%} = \frac{\text{Sales this year} - \text{Sales last year}}{\text{Sales last year}} \times 100 \)
3. Sampling: Choosing Who to Ask
Businesses can’t ask every single person in the country what they think. Instead, they use a sample (a small group that represents the whole population).
Common Sampling Methods:
1. Random Sampling: Everyone has an equal chance of being picked. It’s like pulling names out of a hat.
2. Stratified Sampling: The population is divided into groups (e.g., by age or gender), and then people are picked randomly from those groups to make sure the sample is balanced.
3. Quota Sampling: The researcher is told to find a specific number of people from certain groups (e.g., "Find 20 men and 20 women in the shopping center").
Memory Aid: "RSQ"
Random (anyone), Stratified (balanced groups), Quota (target numbers).
4. Interpreting the Data
Once you have the numbers, you need to know what they mean. Look out for these terms:
Correlation
This shows the relationship between two things (like advertising spend and sales).
• Positive Correlation: Both things go up together. (Example: As the temperature rises, ice cream sales go up).
• Negative Correlation: As one goes up, the other goes down. (Example: As the price of a car rises, the number of cars sold goes down).
Confidence Intervals
No research is 100% perfect. A confidence interval is a range of values that the business is fairly sure the real answer falls within. Example: "We are 95% sure that between 40% and 45% of people will buy this product."
Extrapolation
This is using past data to predict future trends. Warning! This can be risky because the future doesn't always look like the past (e.g., a new competitor might suddenly enter the market).
Key Takeaway
Correlation shows a link, but it doesn't always mean one thing caused the other. Always be cautious when predicting the future!
5. Price and Income Elasticity of Demand
This sounds complicated, but it’s just a way of measuring how "sensitive" customers are to changes.
Price Elasticity of Demand (PED)
How much does demand change when you change the price?
• Inelastic: Customers aren't very sensitive. If you raise the price, they still buy it (like milk or petrol). Total revenue goes up if you raise the price.
• Elastic: Customers are very sensitive. If you raise the price, they switch to a cheaper brand. Total revenue goes down if you raise the price.
Income Elasticity of Demand (YED)
How much does demand change when the customer's income changes?
• Normal Goods: As people earn more, they buy more of these (e.g., nice meals out).
• Inferior Goods: As people earn more, they buy less of these because they can afford better (e.g., "value" range bread or bus travel).
Key Takeaway
If your product is inelastic, you have more power to raise prices. If it's an inferior good, you might actually do better during a recession!
Summary: Why use data in Marketing?
Using data helps a business:
• Reduce risk: They are less likely to launch a product that fails.
• Understand Trends: They can see where the market is going.
• Improve Competitiveness: They can spot gaps that rivals have missed.
• Set Objectives: They can set realistic targets based on facts, not guesses.
Common Mistake to Avoid: Don't assume market research is always right! It can be biased, the sample might be too small, or customers might say one thing in a survey but do something else in the shop.