Interpreting Marketing Data (AQA AS Business): Revision Note

Interpreting 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

(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

(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

Infographics 

• Infographics are easy-to-understand visual representations of data

Correlation

• A correlation exists where there is a relationship or connection between two variables 

  • 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

Diagrams of correlation types

• Correlation does not always indicate a relationship or causation between two sets of variables

  • Businesses must conduct research to determine whether a relationship exists and the strength of that relationship

Extrapolation

• Extrapolation is the assumption that what has happened in the past will be the same as what will happen in the future

• 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

Confidence intervals

• The confidence level is the amount of certainty a business can have that its marketing research data results are accurate

  • E.g. a 95% confidence level means that if the same survey were repeated 20 times, the results would be the same on 19 occasions

• The confidence interval is the range of values possible for a given confidence level

  • E.g. a 98% confidence level that the level of sales will be somewhere between £1.2m and £1.3m

Examples of confidence intervals

• The Office for Budget Responsibility (OBR) publishes regular predictions of economic performance, such as GDP growth, inflation and interest rates

• Here is the actual GDP growth from 2015 to 2024, with a prediction of UK GDP growth up to 2029

  • The lighter the shading, the more confident the OBR is that the actual level of GDP growth will fall between these values

    • In this case, the OBR is 100% certain that GDP growth will be between -2.9% and 5.8% in 2029

    • The confidence interval is

A high confidence level

A lower confidence level

• The darker the shading, the less confident the OBR is that the actual level of GDP growth will fall between these values

  • In this case, the OBR is 80% certain that GDP growth will be between -1.0% and 4.6% in 2029

  • The confidence interval is

Why confidence levels and intervals help businesses

• These show how much to trust the marketing research data

  • Managers see the possible error, not just one headline figure

    • Office for National Statistics (ONS) household surveys publish 95% confidence intervals

    • Marketing planners know where results may vary and can take this into account when making decisions

• Guide the sample size

  • If the interval is too wide, the firm can survey more people to narrow it

  • Bigger samples shrink the interval, giving clearer answers upon which to make marketing decisions

• Support decisions

  • A narrow interval gives managers the confidence to make a key marketing decision, such as launching or dropping a product

Using data to plan and make marketing decisions

• Using data from marketing research turns marketing guesses into facts a business can act upon

Examples of data informing marketing decisions