2- The nature of secondary research data

Question 1

Primary data

Answer 1

New data collected to solve a particular problem

Question 2

Secondary data

Answer 2

Data that have previously been gathered and that
might be relevant to the problem at hand

Question 3

Internal secondary data

Answer 3

Information originating within the company

Question 4

External secondary data

Answer 4

Outside sources of secondary information

Question 5

Advantages of secondary data:

Answer 5

1. Reveal opportunities, challenges
2. Provide ideas for innovation
3. Usually faster & easier to collect, lower cost
4. Alert you to specific primary data needs
5. Background information → expertise
6. Build credibility for the research report (VCs)
7. May provide the sample frame

Question 6

Limitations of secondary data:

Answer 6

You have no control over data collection
– Is it readily accessible?
– Is it free of cost…
– …and disinformation?!
– Think before you use

Question 7

Big data

Answer 7

collection, storage + analysis of massive quantities of info

Question 8

Limitation of big data

Answer 8

We see what people do but not why

Question 9

Focus group- benefits

Answer 9

• Interaction among respondents
• Reflection based on others’ opinions / ideas
• Opportunity to observe customers
• Can be executed fairly quickly

Question 10

Focus groups- drawbacks

Answer 10

• Group-think, influence from co-participants or moderator
• Insights compelling: leads to false sense of reality
• Introverts or dominant participants

Question 11

Criteria when choosing data

Answer 11

Currency
Relevance
Authority
Accuracy
Purpose

1. When was the information collected?
2. How relevant is it for your business? Who’s the intended audience?
3. Who gathered the data? Credentials? Qualifications, depth of knowledge?
4. Evidence? Collected how? Consistent with other sources? Peer reviewed?
5. Why was the study done?

Question 12

Traditional data vs big data

Answer 12

Traditional research often relies on structured methodologies such as surveys, focus groups, interviews, and observational studies. These methods involve a smaller sample size and are typically designed to gather specific insights through controlled interactions with participants.

Big data, on the other hand, involves the analysis of large volumes of data from various sources such as social media, online transactions, website traffic, and other digital interactions. It employs techniques like data mining, machine learning, and predictive analytics to extract insights from vast and diverse datasets.

Question 13

Qualitative projective techniques

Answer 13

Uncovering deeper (even unconscious) thoughts, feelings,
motivations

Question 14

Qualitative research:

Answer 14

Qualitative data: finding not subject to qualification. To examine attitudes, feelings, and motivations. The results are exploratory. Open questions, subjective, dynamic convo.

• What drives customers
• Learn the latest trends straight from buyers
• Thorough reactions to concepts and ideas
• Brainstorm possible uses and applications
• Explore with probes: follow up on responses
• Qualitative can improve on quantitative surveys.
• Focus groups
• Individual in-depth interviews
• Ethnography

Question 15

Quantitative research:

Answer 15

Quantitative: To find statistically significant differences for example surveys. The results are conclusive and confirming. Close questions for larger groups

Question 16

In debt interviews:

Answer 16

• Sensitive topics
• Eliminate group pressure
• Full focus of attention, probe deeper
• NOT group dynamics