3.2.2 UNDERSTANDING MARKETS AND CUSTOMERS
What you need to know
• The value of primary and secondary marketing research
• The value of sampling
• The interpretation of marketing data
• The interpretation of price and income elasticity of demand data
• The value of the concepts of price and income elasticity of demand data to
marketing decision makers
• The use of data in marketing decision making and planning
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MARKETING RESEARCH CLOSELY LINKS WITH
Customer
service
Sales
forecasting
Market
segmentation
Research &
Development
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Budgeting
Marketing
Research
Cash flow
forecasting
TWO CATEGORIES OF MARKET RESEARCH
PRIMARY RESEARCH
• Data collected first-hand for a specific research
purpose
SECONDARY RESEARCH
• Data that already exists and which has been collected
for a different purpose
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SOURCES OF PRIMARY DATA
• Observation
• Postal surveys
• Telephone interviews
• Online surveys
• Focus groups
• Face-to-face surveys
• Test marketing
• Experiments
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FOCUS GROUPS
A focus group is a form of qualitative
research in which a group of people
are asked about their perceptions,
opinions, beliefs, and attitudes
towards a product, service, concept,
advertisement, idea, or packaging.
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BENEFITS AND DRAWBACKS OF PRIMARY MARKET
RESEARCH
BENEFITS
DRAWBACKS
Designed specifically to meet the business’
needs
Expensive to obtain​
Up-to-date and relevant
Time consuming as needs to be analysed
Kept private to the business- not available to
other organisations
Risk of survey bias​
Provides more detailed insights, particularly into Sampling may not be representative​ of the
customer views​
wider market
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SOURCES OF SECONDARY DATA
Google
A great way of getting quick market research; free​ and quickly
Government departments
Provide detailed insights on the economy and on many
industry sectors​
Trade associations
Most industries have an industry association – a great source of
market analysis​
Trade press & magazines
Industry and market insights from professional business
publishers​
Competitor websites & marketing Valuable information on marketing activities of competitors​
materials
Market research reports
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Commercial organisations, for example, Mintel and
Mori, produce a wide variety of reports (online & in print) that
analyse individual markets & industries​
BENEFITS AND DRAWBACKS OF SECONDARY MARKET
RESEARCH
BENEFITS
DRAWBACKS
Often free but usually cheaper than primary
research
Can quickly become out of date​
Good source of market insights​ as data has
already been analysed
Not tailored specifically to business’ needs​
Quick to access and use​
Specialist reports often quite expensive​
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QUANTITATIVE AND QUALITATIVE DATA
QUANTITATIVE DATA
• Based on numbers and figures
• Answers questions such as ‘how many?’, ‘how often’, ‘who?’, ’when?’ and
‘where?’
• Easier to analyse but doesn’t provide in-depth information
QUALITATIVE DATA
• Based on opinions, attitudes, beliefs and intentions
• Answers questions such as ’why?’, ‘would?’ or ‘how?’
• More in-depth information but more difficult to analyse​
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MARKET SIZE, MARKET GROWTH AND MARKET SHARE
When analysing marketing data and setting marketing
objectives, it is important to be able to calculate and
interpret these three key marketing measures
Market
Size
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Market
Growth
Market
Share
MARKET SIZE
• Indicates the potential sales for a firm (the “size of the prize”)
• Usually measured in terms of both volume (units) and value (sales)
• Size of individual segments within the overall market can also be
measured
• Not normally a marketing objective – since a firm cannot influence it
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MARKET SIZE – USING INDEX NUMBERS
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Year
Market Size (£)
Index Number
(2012 = 100)
2018
5,000,000
100
2019
?
105
2020
?
112
2021
?
125
MARKET SIZE – USING INDEX NUMBERS
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Year
Market Size (£)
Index Number
(2012 = 100)
2018
5,000,000
100
2019
5,250,000
105
2020
5,600,000
112
2021
6,250,000
125
MARKET GROWTH
•A key indicator for existing and potential
market entrants
•Growth rate can be calculated using either
value (e.g., market sales) or volume (units
sold)
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MARKET GROWTH BY VOLUME CALCULATION
Year
Market Size
by Volume
(Units)
[A]
Change
(Units)
[B]
Growth Rate (%)
[B]/[A from previous
year] x 100
2018
1,000,000
-
-
2019
1,100,000
2020
1,350,000
2021
1,475,000
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MARKET GROWTH BY VOLUME CALCULATION
Market Size
by Volume
(Units)
[A]
Change
(Units)
[B]
Growth Rate (%)
[B]/[A from previous
year] x 100
2018
1,000,000
-
-
2019
1,100,000
100,000
10.0%
2020
1,350,000
250,000
22.7%
2021
1,475,000
125,000
9.3%
Year
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MARKET SHARE
• Explains how the overall market is split between the existing
competitors
• Tends to be calculated based on market value, but volume can
also be used
• Good indicator of competitive advantage
• Key is to look for significant +/- changes
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MARKET SHARE CALCULATION BY MARKET VALUE
Business
Sales in
2022 (£)
A
250,000
B
400,000
C
900,000
D
175,000
E
275,000
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Cumulative
Market Sales (£)
Market Share
(%) in 2015
MARKET SHARE CALCULATION BY MARKET VALUE
Business
Sales in
2022 (£)
Cumulative
Market Sales (£)
Market Share
(%) in 2015
A
250,000
250,000
12.5%
B
400,000
650,000
20.0%
C
900,000
1,550,000
45.0%
D
175,000
1,725,000
8.75%
E
275,000
2,000,000
13.75%
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SAMPLING IN MARKETING RESEARCH
Sampling involves the gathering
of data from a sample
of respondents, the results of
which should be representative of
the population (e.g. target market)
as a whole
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TYPES OF SAMPLING
RANDOM SAMPLING
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QUOTA SAMPLING
STRATIFIED SAMPLING
TYPES OF SAMPLING: RANDOM
• Every member of a population has an equal chance of being
selected
• Respondents are selected ‘at random’, for example, every tenth
person might be stopped and asked to complete a
questionnaire
• Random sampling does not target any specific segments of the
market
• It is quick and easy to select respondents and there is less
chance of bias (as respondents are selected randomly)
• But the sample may not represent the target market
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TYPES OF SAMPLING: QUOTA
• Respondents are selected based on specific characteristics,
such as age, income or location
• The required number of respondents (the ‘quota’) is then
drawn from each segment of the population
• Quota sampling is more likely to be representative of the
whole market and might require fewer respondents than
random sampling
• But it is not random and so there may be greater risk of bias in
the selection process
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TYPES OF SAMPLING: STRATIFIED
• The researcher divides or 'stratifies' the target group into sections,
each representing a key group (or characteristic) that should be
present in the final sample
• Then each of those sections is sampled individually. The sample
thus created should contain members from each key characteristic in
a proportion representative of the target population
• For example, if a class has 20 students, 18 male and 2 female, and
a researcher wanted a sample of 10, the sample would consist of 9
randomly chosen males and 1 randomly chosen female, to represent
this population.
• Stratified sampling ensures that the respondents are representative of the
whole market
• But it takes more time and resources to plan so is likely to be more
expensive than other sampling methods
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CORRELATION
Correlation looks at
the strength of a
relationship between
two variables
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CORRELATION AND SCATTER GRAPHS
• Correlation is usually measured by using a scatter diagram, on which data points
are plotted.
• The independent variable is usually plotted on the x-axis. This is the factor that
causes the other factor (the dependent variable) to change
• The dependent variable is usually plotted on the y-axis. This is the variable that is
influenced by the independent variable
• For example, if a business may be keen to understand the impact on customer
enquiries if advertising expenditure is varied
• In this case, advertising expenditure would be the independent variable and
customer enquiries would be the dependent variable
• A ‘line of best fit’ is used to plot the mathematical relationship between the
variables based on the data points.
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PLOTTING CORRELATION: EXAMPLE
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TYPES OF CORRELATION
TYPE OF CORRELATION
EXPLANATION
POSITIVE
A positive relationship exists where as the
independent variable increases in value, so
does the dependent variable
NEGATIVE
A negative relationship exists where as the
independent variable increases in value, the
dependent variable falls in value
NO CORRELATION
There is no discernible relationship between
the independent and dependent variable
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POSITIVE CORRELATION
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NEGATIVE CORRELATION
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NO CORRELATION
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STRONG AND WEAK CORRELATION
STRONG CORRELATION
• The line of best fit indicates the strength of the correlation
• Strong correlation means that there is little room between the data points
and the line
• If the data suggests strong correlation, then the relationship might be used
to make marketing predictions
WEAK CORRELATION
• Weak correlation means that the data points are spread quite wide and far
away from the line of best fit
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CONFIDENCE INTERVALS & LEVELS
A confidence interval gives the
percentage probability that an
estimated range of possible values
in fact includes the actual value
being estimated
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CONFIDENCE INTERVALS
• Businesses benefit from the use of statistics in estimating or
predicting future events
• A confidence interval helps a business evaluate the reliability
of a particular estimate
• Because no estimate can be 100% reliable, businesses need
to know how confident they should be in their estimates and
whether to act on them
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CONFIDENCE INTERVALS: EXAMPLE
• If a manufacturing firm takes samples of finished
products from its production line to check for quality,
how confident can the business be that the sample of
products inspected is representative of all the products
being made?
• A common confidence interval acceptable to
management is 95%. This means that 19 out of 20
samples taken (95%) will give results that are
representative of the overall population. Or to put it
another way - 1 out of 20 (5%) are unrepresentative!
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CONFIDENCE INTERVALS: EXAMPLES
BUSINESS ACTIVITY
HOW CONFIDENCE INTERVALS ARE USED
Quality management
Percentage reliability of machines
Chance that quality control samples will detect
issues
Market research
Statistical estimates for sales forecasting
Reliability of data from customer surveys
Risk management &
contingency planning
Risks of sales forecasts not be achieved
Scenario planning for competitor actions
Budgeting &
forecasting
Likely range of revenues and costs based on key
assumptions
Sales forecasts to support new product launches
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EXTRAPOLATION
Extrapolation uses trends
established from
historical data to forecast
the future
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EXTRAPOLATION: HOW IT WORKS
• Identify the trend, for
example, in sales
• Draw a line of best fit (in red)
• Continue (extrapolate) the
trend into the future (in blue)
• Use this to make predictions
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EXTRAPOLATION
ADVANTAGES
DISADVANTAGES
A simple method of forecasting
Unreliable if there are
significant fluctuations in historical data
Not much data required
Assumes past trend will continue into the
future. This is unlikely in
many competitive business environments
Quick and cheap
Ignores qualitative factors (for example,
changes in tastes & fashions)
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ELASTICITY
Elasticity measures
the responsiveness of
demand to a change in a
relevant variable, such as
price or income
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PRICE ELASTICITY OF DEMAND (PED)
Price elasticity of
demand measures the extent to
which the quantity of a
product demanded is affected
by a change in price
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PRICE ELASTICITY OF DEMAND: FORMULA
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INTERPRETING PED
Value of PED​
Interpreting the
Elasticity​
Price elastic​
More than 1​
Change in demand is
more than the
change in price​
Price inelastic​
Less than 1​
Change in demand is
less than the change
in price​
Unitary
price elasticity​
Exactly = 1​
Change in demand =
change in price​
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PED CALCULATION EXAMPLE: INELASTIC DEMAND
Product A​
Quantity Demanded​
Change in Price​
Original Price​
New Price​
£100​
£125​
500 units​
400 units​
+ £25​
Change in Demand​
- 100 units​
% Change in Price​
(25/100) x 100 = 25%​
% Change in Demand​
(100/500) x 100 = 20%​
PED
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20% / 25% = - 0.8
PED CALCULATION EXAMPLE: ELASTIC DEMAND
Original Price​
New Price​
£1,000​
£900​
Quantity Demanded​
200 units​
250 units​
Revenue (price x qty)​
£200,000​
£225,000​
Product B​
Change in Price​
- £100​
Change in Demand​
+ 50 units​
% Change in Price​
(100/1000) x 100 = 10%​
% Change in Demand​
PED​
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(50/200) x 100 = 25%​
25% / 10% = 2.5​
WHY PRICE ELASTICITY OF DEMAND IS IMPORTANT
• PED helps businesses to examine the likely impact of a
change in price
• If PED > 1 (price elastic) then a change in price will cause a
larger change in demand
• This means overall revenues would increase if the price was
reduced
• But overall revenues would decrease if the price was
increased
• If PED < 1 (price inelastic​) then the opposite would happen
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FACTORS INFLUENCING PED
Factor​
Effect on PED​
Brand strength​
Products with strong brand loyalty and reputation tend be price
inelastic​
Necessity​
The more necessary a product, the more demand tends to be
inelastic​
Habit​
Products that are demanded and consumed as a matter of habit
tend to be price inelastic​
Availability of substitutes​
Demand for products that have lots of alternatives (substitutes)
tends to be price elastic​
Time​
In the short-run, price changes tend to have less impact on
demand than over longer periods​
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INCOME ELASTICITY OF DEMAND (YED)
Income elasticity of
demand measures the extent to
which the quantity of a
product demanded is affected
by a change in income
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INCOME ELASTICITY OF DEMAND: FORMULA
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INTERPRETING YED
Value of YED​
Interpreting the Elasticity​
Normal products
Positive
A rise in incomes will see an increase in
demand and vice versa
The extent of the change will depend on
the type of product (luxury/necessity).
Inferior goods
Negative
A rise in incomes will see a decrease in
demand as consumers switch to better
alternatives
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NORMAL GOODS: LUXURIES AND NECESSITIES
Luxuries​
Necessities​
Income elasticity more than 1​
Income elasticity less than 1, but more
than 0​
As income grows, proportionally less is
spent on necessities​
As income grows,
proportionally more is spent on
luxuries​
Examples:
Consumer goods​
Expensive holidays​
Branded goods​
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Examples:
Staple groceries (for example, milk)​
Own-label goods​
PED & YED COMBINED
How might the following price and income elasticity
data for two products be interpreted?
PED​
YED​
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PRODUCT A​
- 1.5​
+ 0.5​
PRODUCT B​
- 0.2​
+ 1.9​
PED & YED COMBINED
PED​
YED​
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PRODUCT A​
- 1.5​
+ 0.5​
Price elastic​
Income inelastic​
Likely to be a
necessity​
PRODUCT B​
- 0.2​
+ 1.9​
Price inelastic​
Income elastic​
Likely to be a
luxury good
(perhaps branded)​
THE VALUE OF THE CONCEPTS OF PRICE AND INCOME
ELASTICITY OF DEMAND TO MARKETING DECISION MAKERS
• Elasticities provide useful insights for management in decision making
• Firms tend to like to have products with inelastic demand
• Building strong brands and product USPs is a good strategy for making demand
more price inelastic
• But, it can be difficult to get reliable data​, especially in markets where there is
rapid change (such as technology)
• Other factors, such as consumer tastes, also affect demand
• Many markets subject to rapid technological change – make previous data less
reliable
• Firms should be aware that competitors will react; pricing decisions can’t be
taken in isolation!
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THE USE OF DATA IN MARKETING DECISION MAKING
AND PLANNING
• Businesses use a variety of tools to analyse marketing data and make
forecasts
• The key aim of interpreting data is to make reasonable assumptions which
support marketing decision making
• This information can help with business planning, for example, ensuring
that the business has sufficient resources (raw materials, workers and
production capacity) to meet demand
• Data can also be used inform financial planning, such as sales forecasts,
costs and profits as well as cash flow forecasts and break even.
• But, no one technique is completely reliable
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