Marketing Information & Research

Session 4

- Sampling and data analysis.

- Databases.

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Six-stage sampling process

1. Define the population of interest.

2. Determine whether to sample or census.

3. Select the sampling frame.

4. Choose a sampling method.

5. Determine the sample size.

6. Implement the sampling procedure.

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1. Probability sampling

• Each member has a positive, calculable probability of being chosen.

• Response rate is important.

• Objective.

2. Non-probability sampling (sometimes called ‘purposive’ sampling)

• Uses human judgement.

• Subject to errors.

• Subjective.

Sampling methods

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• Random sampling:

- Simple (using random numbers).

- Systematic (using skip interval).

- Stratified (using sub-sets of population).

• Cluster sampling.

• Multi-stage sampling.

Probability sampling methods

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• Quota sampling.

• Convenience sampling.

• Judgement or purposive sampling.

• Snowball sampling.

Non-probability sampling methods

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How large does your sample have to be?

It depends on statistical significance…

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Statistical significance

The bad news

• There’s no such thing as absolute statistical significance. You simply can’t say that a survey finding is definitely statistically significant… or that it isn’t.

• There is always a risk of error in any survey, no matter how large it is, due to the fact that you’re using a sample of the population rather than interviewing the entire population. This is known as sample error.

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Statistical significanceThe good news

• There is a known level of accuracy attaching to any given statistic on any given random-sample survey

• The level of accuracy depends on:– The size of the sample (the bigger the survey, the better).– The statistic itself which you’ve measured.

• This information allows you:– To design surveys which are big enough (‘fit for purpose’).– To know how far you should trust the results to your survey.– It also tells you when you’re in danger of over-interpreting small

differences in the data.

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The principle of sample error (in simple terms)

A very large jar filled with a very large and equal

number of black and white marbles

You draw a sample of 100 marbles from the jar

without looking

Would you bet me:

• That you’ve drawn out exactly 50 black marbles?

• That the number of black marbles you’ve drawn is

somewhere between 40 and 60?

You’d LOSE this bet most of the time

You’d WIN this bet 95% of the time (19 times of 20)9

If you’ve a random sample survey with sample size n

and your survey has measured a given percentage p…

the error on this statistic (at the 95% confidence level) is:

1.96 (p)(100-p)

n

The principle of sample error (the maths)

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http://www.dssresearch.com/toolkit/secalc/error.aspAn easier way to do it: sampling error calculator

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An easier way to do it: sampling error calculator

http://www.dssresearch.com/toolkit/secalc/error.asp

Population 500 500 500

Sample size 50 100 200

Sample proportion

50% 50% 50%

Confidence interval

95% 95% 95%

Sampling error 13.2% 8.8% 5.4%

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In practice this means that if you’ve measured a statistic as 50% (our survey says that 50% of the British public believe a fried breakfast is healthier than cornflakes) then:

The error on this percentage is +/-10% if you had a sample size of 100

The error is +/-5% if you had a sample size of 400

The error is +/-3% if you had a sample size of 1,000

A researcher might report that (for the survey of 100 people): ‘The correct statistic is 50%, +/-10% at the 95% confidence level.’ (In other words, 95% of the time the correct answer is somewhere between 40 and 60)

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Other factors in deciding sample size:• Budget.• Timings.• Risk attached to the decision.• Variability of the population.• Likely response rates.

To double the level of accuracy……quadruple the sample size!

Sample size

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Other errors that affect accuracy of survey results

• Sampling frame error.

• Non-response errors:• Refusals.• Non-availability of respondents.

• Data errors• Respondent errors.

• Interviewer errors.• Data analysis errors.

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Data analysis

Before analysis we need to do a few things...

Data editing: Looking for obvious errors or inconsistencies.

Data coding: Allocating numbers to each response.

Data entry: Using CAPI/CATI, OCR scanners or manually.

Data cleaning: Check/clean OCR mistakes or mis-keying of manual data.

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Analysis of qualitative data

Use of transcripts• Advantages?

• Disadvantages?

Importance of ongoing analysis during qualitative research

• Compare with analysis of quantitative data: if the qualitative research is coming up with very different results you may need to adjust accordingly.

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Analysis of quantitative data

Univariate techniques, eg averages, frequency counts.

Bivariate techniques, eg cross tabulation, correlation.

Multivariate techniques, eg regression analysis, conjoint analysis.

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Averages

Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

No. of complaints 2 5 0 8 1 6 0 11 0 3 2 4

What is the mean?

What is the median?

What is the mode?

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Time series data

Trend: “Underlying movement in time series data over the long term”20

CorrelationMeasures the nature and strength of association between two variables.

Can be negative or positive:• As price increases, sales decline – negative correlation

between price and sales.• As advertising expenditure increases, sales increase –

positive correlation between ad spend and sales.

Does correlation between two variables prove cause and effect?

• ‘Going to bed fully clothed causes a hangover.’ Does it?

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Customer databases

• Computerised or manual.

• Source of accurate up-to-date information.

• Relevant to organisation’s goals.

• Data is collected systematically.

• Data is maintained and monitored.

• Is used to formulate strategy.

• Is used to set marketing objectives.

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Advantages of customer databases

• Can provide information on ALL customers

• Not just a sample in a marketing research survey.

• Enables data to be analysed, including:• Purchasing behaviour - products purchased and

the quantities, frequency and timing of purchases.• Customer loyalty - length of relationships and

customer value/profitability.• Customer responses - to promotions, price

changes, new products etc.23

What can you do with good customer databases?

• Increase sales to new and existing customers through better timing, identifying needs more effectively and cross-selling.

• Produce effective marketing communications through a more personal approach.

• Develop new and improved products.

• Enhance customer satisfaction, retention and the value from existing customers - and reduce the cost of servicing them.

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Limitations of customer databases

• They only record WHAT has happened - not WHY.

• They only record historical data - not your customers’ likely future actions.

• They do not depict the whole market.

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Data categories

• Volunteered data– Provided willingly by the customer.

• Behavioural data– Derived from customer behaviour.

• Profile data– From linking database with other information sources.

• Attributed data– Extrapolated from market research.

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Decisions on databases

As well as current customers, should we include prospects? If so does that mean all enquiries? What about also buying or renting lists?

How will we format and validate the data? How will we avoid duplication? How will we keep it up-to-date?

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Customer profiling

The main goal of customer profiling is to categorise the traits and characteristics of current customers, to identify the primary characteristics of good and bad customers.

Customer profiling will help you:

• Discover which customers are sales and profit contributors.

• Identify customers who have most potential.

• Find out who are your unwanted customers.

• Categorise customers by relevant and defined criteria.

• Bring more focus to your marketing and sales efforts and use your resources more effectively.28

Customer profiling

A tool that can be useful in B2C customer profiling:

• ACORN (A Classification Of Residential Neighbourhoods) http://www.caci.co.uk/acorn-classification.aspxThe banner space of this page is a slideshow of some ACORN classifications.

For a good guide to classifications and a detailed list of the categories go here:http://www.businessballs.com/demographicsclassifications.htm

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Customer profiling

Some tools that can be useful in B2B customer profiling:

• SIC (Standard Industrial Classification)http://www.companieshouse.gov.uk/about/sic2007.shtmlThis has a good introduction to the SIC system.

http://www.ons.gov.uk/ons/guide-method/classifications/current-standard-classifications/standard-industrial-classification/index.htmlTake a look at the ‘Main Volume’ pdf document – from page 27 it has the extensive list and explanations of business classifications that you might use when organising your B2B database into profile groups.

• Data HQ factsheethttp://www.datahq.co.uk/factsheets/FS5_B2B_Customer_Profiling_and_Market_Penetration.pdf This is a fact sheet from a company that carries out data profiling for companies, using SIC data as the basis for profiling. It gives you a good flavour of how data profiling is done.30

Boots Advantage card – segmenting by response to promotions

• Deal seekers - only ever buy promotional lines.• Stockpilers - buy in bulk when goods are on offer

and then don’t visit the store for weeks.• Loyalists - existing buyers who will buy a little

more of a line when it is on offer but soon revert to their usual buying patterns.

• The new market - customers who start buying items when on promotion and then continue to purchase the same product once it reverts to normal price.

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Setting up a customer database

• Business review.

• Data audit.

• Data strategy & specification.

• Data verification.

• Hardware/software.

• In-house or outsource.

• Applications.

• Review.32

Customer databases: is bigger always better?

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Eight principles of the Data Protection Act

• Fairly and lawfully processed.

• Processed for limited purposes.

• Adequate, relevant and not excessive.

• Accurate and up-to-date.

• Not kept for longer than is necessary.

• Processed in line with individuals' rights.

• Secure.

• Not transferred to other countries without adequate protection.

Information Commissioners Office (ICO) guide to the Data Protection Act: http://bit.ly/czuf43

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