To identify the main features of a market and reduce risks of market entry
To predict future demand changes and market trends
To explain consumer buying patterns for existing products and market trends
To assess the most favoured designs, flavours, styles, promotions and packaging
PRIMARY RESEARCH is the process of COLLECTING ORIGINAL DATA FROM SOURCES to address specific research objectives. This data is GATHERED FIRSTHAND through methods such as surveys, interviews, observations, and experiments, providing current, relevant, and specific insights tailored to the researcher's needs.
UP-TO-DATE
VERY RELEVANT
CONFIDENTIAL
GETTING CHEAPER DUE TO I.T.
CAN BE COSTLY
TIME-CONSUMING
POTENTIAL FOR ERROR
SECONDARY RESEARCH is the process of COLLECTING AND ANALYSING EXISTING DATA THAT HAS ALREADY BEEN GATHERED BY OTHERS.
This data can come from sources like reports, academic studies, government statistics, industry publications, and online databases. It helps businesses gain insights without the time and cost of conducting original (primary) research.
Apply these advantages and disadvantages in context of your business
--SURVEYS--
Cost-Effective – Surveys can be conducted online, reducing costs compared to interviews or field research.
Time-Efficient – Large amounts of data can be collected quickly.
Scalability – Can reach a wide audience, even across different geographic locations.
Standardized Data – Ensures consistency, making analysis easier.
Anonymity – Respondents may feel more comfortable providing honest answers.
Limited Depth – Responses are often brief, lacking detailed explanations.
Response Bias – Participants may provide socially desirable or inaccurate answers.
Low Response Rates – Many people ignore surveys, leading to potential sampling bias.
Misinterpretation – Poorly worded questions can lead to confusion and unreliable data.
Lack of Flexibility – Cannot probe deeper into answers as in interviews.
--INTERVIEWS--
In-Depth Information – Allows for detailed responses and deeper insights.
Clarification Possible – Researchers can ask follow-up questions for better understanding.
Personalized Approach – Can adapt questions based on the respondent’s answers.
Higher Response Rate – More engagement compared to surveys.
Non-Verbal Cues – Body language and tone provide additional context.
Time-Consuming – Conducting and analyzing interviews takes longer than surveys.
Expensive – Requires travel, scheduling, and transcription, increasing costs.
Interviewer Bias – The way questions are asked may influence responses.
Limited Sample Size – Fewer participants compared to surveys due to time constraints.
Difficult to Analyze – Qualitative data requires extensive interpretation and categorization.
-FOCUS GROUPS--
Rich Insights – Participants build on each other’s responses, generating deeper discussions.
Immediate Feedback – Quick way to test ideas, products, or services.
Observing Group Dynamics – Body language and interactions provide additional insights.
Flexible Format – Can explore multiple topics in a single session.
Cost-Effective for Small Groups – Cheaper than large-scale surveys or multiple individual interviews.
Dominant Participants – Stronger personalities may overshadow quieter members.
Groupthink – Participants may conform to majority opinions, reducing originality.
Limited Sample Size – Findings may not be generalizable to a larger population.
Facilitator Bias – The moderator’s style may influence responses.
Logistical Challenges – Requires careful planning, recruitment, and a suitable environment.
-OBSERVATIONS--
Real-World Data – Captures actual behavior rather than self-reported responses.
Unbiased Insights – Eliminates response bias common in surveys and interviews.
Non-Verbal Cues – Records gestures, expressions, and interactions for deeper understanding.
Useful for Natural Settings – Effective in studying consumer behavior, work environments, or social interactions.
Can Be Longitudinal – Allows for long-term tracking of trends and patterns.
Time-Consuming – Requires extended periods for accurate data collection.
Observer Bias – The researcher’s presence or interpretation may influence results.
Limited Context – Cannot capture thoughts, motivations, or reasons behind actions.
Ethical Concerns – Covert observations may raise privacy issues.
Difficult to Generalize – Findings may not apply to different settings or larger populations.
--MARKET INTELLIGENCE ANALYSIS REPORTS--
--ACADEMIC JOURNALS--
--GOV'T PUBLICATIONS--
--TRADE ORGANISATIONS--
--MEDIA ARTICLES/SPECIALIST PUBLICATIONS--
"Quantitative data shows that Mo Salah scored 25+ goals this season, while qualitative data describes his playing style as dynamic, skillful, and composed under pressure."
QUALITATIVE DATA refers to information that representing information and concepts that are NOT REPRESENTED BY NUMBERS and STATISTICS.
"How would you describe Mo Salah’s playing style?"
"What makes Mo Salah a difficult player for defenders to handle?"
"How has Mo Salah’s leadership influenced his teammates?"
"What emotions does Mo Salah’s playing evoke in fans?"
"How has Mo Salah’s performance changed over the years?"
QUANTITATIVE DATA refers to information that CAN BE COUNTED, MEASURED, or GIVEN A NUMERICAL VALUE.
"How many goals has Mo Salah scored this season?"
"What is Mo Salah’s average sprint speed during a match?"
"How many assists has Mo Salah provided in the last five games?"
"What is Mo Salah’s shot accuracy percentage this season?"
"How many minutes does Mo Salah play per game on average?"
In most cases of primary data collection it is impossible or too expensive to ask the ‘entire population’. In data collection, the term ‘population’ does not mean ‘the population of the region/country/world’ but the total number of people under study, as defined by the objectives of the market research. For example: ‘All people aged 21–25 years old still in full-time education’. Owing to the cost of gathering data from the whole population that is of potential interest to the business, as well as the time it would take, sampling is essential when using primary research methods. Generally speaking, the larger the sample, the more confidence can be given to the final results. In surveying consumer reaction to a new advertising campaign for a major brand of chocolate, a sample of ten people is unlikely to be sufficient. The first ten people chosen might show a positive reaction to the new advertisement. Yet the next ten might show a negative reaction. A sample of ten is too small to be confident about the result, as variations from the views of the whole target population occur by chance because of the limited number of respondents. A sample of 100 or even 1000 will produce results that will reflect much more accurately the total preferences of the whole survey population. There will be much less risk of pure chance distorting the results and causing sampling error. What prevents all primary research being based on a sample size of 1000? Cost and time are the two major constraints here – the bigger the samples, the greater the cost and the longer the time needed to collect and interpret results.
QUOTA SAMPLING as the name suggests involves
The population is first segmented into mutually exclusive subgroups
– such as part-time and full-time workers. Then the interviewer or researcher uses their judgement to select people from each segment based on a specified proportion. For example,
an interviewer may be told to sample 200 part-time workers and 300
full-time workers between the ages of 45 and 60 years. In quota
sampling the selection of the sample is non-scientific and it may therefore be biased. Interviewers might be tempted to interview those who look most helpful or most attractive. The main weakness of quota sampling is that not everyone gets a chance of selection.
Random sampling
Each member of the target population has an equal chance of
being included in the sample. To select a random sample the
following are needed:
• a list of all the people in the target population
• sequential numbers (each member of this population is
assigned a number)
• a list of random numbers generated by computer.
If a sample of 100 is required, then the first 100 numbers on the
random number list are taken and the people who had these
numbers allocated to them will form the sample – but it may take
time to contact these specific people. Just asking the first 100
pedestrians who pass by during a survey on a main shopping
street is not random sampling. That is called convenience sampling
and will be biased because different groups of people tend to
frequent the main shopping streets at different times. This means
that a single convenience sample will not reflect the whole
population in which the business is interested.
convenience sampling
The advantages of convenience sampling are the availability and
the speed with which data can be gathered. The disadvantages are
the risk that the sample might not represent the population as a
whole, and it might be biased by volunteers. For example, if a study
to determine the average age and gender of customers at a supermarket is conducted for three hours on a weekday afternoon it might be over-represented by elderly people who have retired and under-represented by people of working age.
Random Sampling: You use a computer to randomly select seat numbers and interview whoever is sitting there, ensuring an unbiased selection.
Convenience Sampling: You only interview the fans sitting near the entrance because they’re the easiest to reach.
Quota Sampling: You decide in advance to interview 50% Liverpool fans and 50% opposing team fans, then selectively approach people until you meet that quota.
Random Sampling
✅ Pros:
Provides an unbiased and representative sample.
Results can be generalized to the larger population.
Eliminates researcher bias.
❌ Cons:
Can be time-consuming and costly.
May require access to a full list of the population, which isn’t always possible.
Some randomly selected individuals may be unwilling to participate.
Convenience Sampling
✅ Pros:
Quick, easy, and inexpensive to conduct.
Requires minimal planning and resources.
Useful for exploratory research or when time is limited.
❌ Cons:
Highly prone to bias and may not represent the entire population.
Results cannot be generalized.
Can overrepresent certain groups while excluding others.
Quota Sampling
✅ Pros:
Ensures specific subgroups are represented.
Faster and easier than random sampling while still providing some structure.
Useful when population characteristics are known in advance.
❌ Cons:
Still involves some researcher bias in selecting participants within each quota.
May not be fully representative if quota criteria don’t reflect the true diversity of the population.
Can be challenging to balance quotas accurately.