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Market Research Data (Cambridge (CIE) A Level Business): Revision Note

Exam code: 9609

Lisa Eades

Written by: Lisa Eades

Reviewed by: Steve Vorster

Updated on

Reliability of market research data

  • Market research is a valuable tool that helps businesses make informed choices about, for example, product development, pricing and promotion

  • However, if the data collected is unreliable, it can lead to poor decisions that waste time, money and resources

Why market research data can be unreliable

Reason

Explanation

Example

Respondents may not give truthful or accurate answers

  • People might give answers they think sound good, rather than what they truly believe. Others may guess or not take the questions seriously

  • A customer claims they would buy an eco-friendly product to appear responsible, but they don’t actually plan to

Questions may be poorly designed or unclear

  • If the wording is confusing, too vague, or biased, responses can be inconsistent or misleading

  • A survey asking “Do you like affordable luxury?” may be interpreted differently by each person

Secondary data may be outdated or collected for a different purpose

  • Existing data might not reflect current trends, or it may have been gathered with different goals in mind

  • A business uses a 2019 report on travel habits, which doesn’t reflect post-pandemic behaviour

Data collection methods may be inconsistent or poorly managed

  • Mistakes in how the research is conducted, recorded or analysed can reduce reliability

  • An interviewer forgets to record some answers or enters the wrong data into a spreadsheet

Analysis of quantitative and qualitative market research data

Quantitative data

  • Quantitative data is based on numbers

    • It could include financial reports (e.g. sales, costs), market data (e.g. market share) or summaries of data gained from primary research (e.g. on a scale of 1–10, rate our customer service)

  • It is beneficial for a range of reasons

    • Percentages, scores, or yes/no answers are straightforward to organise, compare and present using graphs or charts

    • Large amounts of numerical data can reveal clear patterns in customer behaviour over time

      • E.g. A clothing retailer may notice that online sales increase by 20% every November, helping them plan future promotions

Analysis of quantitative data using mean, median, mode and range

Term

Definition

How to calculate

Example

Mean

  • The average of a set of numbers

  • Add all the numbers together, then divide by how many numbers there are

Data colon space 4 comma space 6 comma space 8 space space Mean space equals space left parenthesis 4 space plus space 6 space plus space 8 right parenthesis space divided by space 3 space space equals space 6

Median

  • The middle value when the numbers are in order

  • Arrange the data from smallest to largest and find the middle number

Data colon space 3 comma space 5 comma space 7 space space space Median space equals space 5 space space space Data colon space 3 comma space 5 comma space 7 comma space 9 space space Median space equals space left parenthesis 5 space plus space 7 right parenthesis space divided by space 2 space equals space 6

Mode

  • The number that appears most often

  • Find the value that repeats the most in the data set

Data colon space 2 comma space 4 comma space 4 comma space 5 space space Mode space equals space 4

Range

  • The difference between the highest and lowest values

  • Subtract the smallest number from the largest

Data colon space 2 comma space 7 comma space 9 space space Range space equals space 9 space minus space 2 space space equals space 7

Qualitative data

  • Qualitative data gathers descriptions or explanations

    • These can be based on conversations, discussions, impressions and emotional feelings and are usually gathered through primary research

    • It helps businesses understand not just what customers do, but why they do it

    • By listening to detailed customer feedback, businesses can make meaningful improvements to products or services based on real user experiences

      • E.g. A technology company might learn from a focus group that users find a mobile app too complicated, leading to changes in layout and features

Analysis of qualitative data

  • Businesses often analyse qualitative data through a process called coding

    • This is where similar responses or themes are grouped together, so the business can identify patterns and common issues

    • Each theme is given a code to make it easier to sort and compare

Case Study

Coding in action

Two hands creating a mobile app design with sketches, colourful pencils, and phone mockups on a wooden desk, surrounded by design materials.

A mobile phone company collects hundreds of open-ended survey responses asking customers what they like or dislike about a new handset

Marketing researchers read through the comments and assign codes:

  • “BAT” for battery life

  • “CAM” for camera quality

  • “EAS” for ease of use

  • “DES” for design and appearance

After coding the responses, they may notice that many customers mention “BAT” (battery life) negatively

The business can then use this insight to improve the battery in the next version of the product

They may also adjust their marketing to focus on features that customers value more positively

Limitations of quantitative and qualitative research data

Limitations of quantitative data

Limitations of qualitative data

  • Numerical data may be out-of-date, especially in dynamic markets

  • Data analysis and interpretation is a complex skill that is likely to be lacking in smaller businesses

  • Looking at a small amount of data and then extrapolating results can provide wrong assumptions on which to base decisions

  • Numerical data does not provide reasons for outcomes, e.g. data may reveal sales volumes are falling but not the reason for the decline

  • Bias may mean that analysts can interpret responses in a particular way

  • Respondents may lack awareness or language skills to explain preferences accurately

  • Respondents in focus groups may be influenced by the responses of others or not provide accurate information

  • Qualitative data is difficult to present in graphs and charts, so it may not be easily understood

Interpretation of tables, charts and graphs

1. Tables

  • Tables summarise data in an organised form

Table titled "Sales (£m)" showing quarterly sales of four products: KP4000, FlashMaster, Swift Vue X, and LumoLens from January to December.
A table summarises data in an organised form

  • The table shows that:

    • Flashmaster generates the highest sales in each period

    • Sales of LumoLens are lower at the end of the year than at the start of the year

2. Graphs and charts

  • Data contained in graphs and charts can be important sources of marketing research

  • Data may be presented in a range of forms

Bar charts

  • Bar charts show data that are independent of each other, such as sales per store

A bar chart showing US spending on home video entertainment in 2017, with streaming up 32% and DVD and Blu-ray sales down 14%, rent-by-post down 20% and shop rentals down 21%, for a total of a 10% increase since 2016.
A bar chart showing sales revenue for a selection of home video entertainment formats in the USA in 2017 (Source: British Council)

Pie charts 

  • Pie charts show how a whole is divided into different elements, such as total sales divided amongst different product types

An example of a pie chart showing Apple's quarterly revenue by category in April 2021: iPad 9%, Mac 10%, services 19%, wearables 9% and iPhone 54%.
A pie chart showing Apple's quarterly revenue by category in April 2021 (Source: Six Colours)

Scatter graphs

  • Scatter graphs allow businesses to compare two variables, such as sales volume and advertising, to establish if there is any correlation between them

A scatter graph depicting the relationship between the number of sales managers employed and the volume of sales, with data points marked as red crosses.
A scatter graph showing the number of sales managers employed by a business and the volume of items sold

  • A correlation exists where there is a relationship or connection between the two variables in a scatter graph

    • A positive correlation means that as one variable increases, so does the other variable

      • A line of best fit that slopes upwards can be identified 

    • A negative correlation means that as one variable increases, the other variable decreases

      • A line of best fit that slopes downwards can be identified 

    • No correlation means that there is no connection between the two variables

      • It is not possible to identify a line of best fit

Correlation types

Three graphs showing positive, negative and no correlation between variables A and B, with data points and trend lines labelled accordingly.
The main types of correlation between two variables: positive, negative and no correlation

  • Where a line of best fit can be identified and when causation is determined, a business can extrapolate data to make predictions around changes to either of the variables

    • E.g. extrapolation of the line of best fit in the example below means that the business could predict that employing seven sales managers would likely result in sales of 46 units

Extrapolation using a line of best fit

The graph shows a linear relationship between the number of sales managers and the volume of sales, with data points plotted and a trend line.
A scatter graph with a line of best fit showing the number of sales managers employed by a business and the volume of items sold

Examiner Tips and Tricks

When drawing a line of best fit, you should try to include as many data points above the line as below the line

Watch out for outlying data — if there is more than one outlier above the line, adjust your line of best fit upwards

Similarly, if there is more than one outlier below the line, adjust your line of best fit downwards. Just one outlier should not influence your line of best fit

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    Author
    Reviewer
    Lisa Eades

    Author: Lisa Eades

    Expertise: Business Content Creator

    Lisa has taught A Level, GCSE, BTEC and IBDP Business for over 20 years and is a senior Examiner for Edexcel. Lisa has been a successful Head of Department in Kent and has offered private Business tuition to students across the UK. Lisa loves to create imaginative and accessible resources which engage learners and build their passion for the subject.

    Steve Vorster

    Reviewer: Steve Vorster

    Expertise: Economics & Business Subject Lead

    Steve has taught A Level, GCSE, IGCSE Business and Economics - as well as IBDP Economics and Business Management. He is an IBDP Examiner and IGCSE textbook author. His students regularly achieve 90-100% in their final exams. Steve has been the Assistant Head of Sixth Form for a school in Devon, and Head of Economics at the world's largest International school in Singapore. He loves to create resources which speed up student learning and are easily accessible by all.