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Quantitative methods to optimize EBI Summary + practice questions
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Lecture notes QMLecture 1
Introduction to QM methods in the content of entrepreneurship
Most decisions are made through experience and heuristics, this can lead to mistakes. Heuristics are helpful
in strategic management as they provide time-pressured professionals with a simple way of dealing with a
complex world. They sometimes lead to severe and systematic errors in decision-making.
Move from opinion/intuitive decision making to data base decision making. Find a balance in this. Moving to
quantitative applications in eship. Heuristics can help, but they can also be mistaking.
In the course, we will focus on analytical decisions. Managers should be aware of pitfalls and do reflective
thinking (system 2 thinking). To think critically about data.
Data based decision making involves developing and using computer-decision models for analyzing, planning
and implementing marketing strategies and tactics. > Most companies use Excel, but this is limited. We also
use SPSS.
- Managers in startups have a more data-friendly mindset, they know that success of their companies
depend on smart technologies.
- Many entrepreneurs are aware of the importance of having high-powered personal computers
connected to networks everywhere. Data = key to success
- volume of marketing data is exploding
- firms are reengineering their IT infrastructure for the information age.
External and internal analyses
How to differentiate between these two?
Layers of analysis >
,Look at the macro-environment, the industry and the competitive area in relation to the company.
Marketing is also about data analytics and performance measures.
Macro environment: is focused on environmental factors that may have an impact on a firms future
performance. Trends & events. Megatrends: globalization, scarcity of resources, climate change, pandemic
etc.
Example of tech trends that could influence decision making: AI. E.g. smart fridges, chatbots.
Entrepreneurs can fail because they do not track the macro-environment: Kodak camera business. They
invented the roll film. FujiFilm came with the digital camera and asked for loads of feedback from the
customer > continuous improvement.
Define the relevant market is an essential first step for analyzing competitive forces. Entire market scope vs
the relevant market. Think of different layers. Competitor of VW when it comes to communication
technologies = google. Would not think of this as a direct competitor for VW.
Practical example; street musician. They market their music performance to consumers. Could be seen as
entrepreneurs. We do not know a lot about critical factors in their ‘market’. So how do you optimize this? An
empirical setting is needed to understand the success factors of street music.
1. Conduct interviews and applied customer behavior theories to develop a conceptual framework of
the success factors
2. An empirical field study to investigate which factors have an impact
3. Apply econometric models and apply a simulation analysis to identify which factors influence the
salary.
Determine dependent and independent variables!
Transformation of a managerial problem into a
research problem;
, 1. Problem definition (stakeholder analysis)
To conduct solid analyses and research, an understanding of the company as a system of stakeholders is
essential. Stakeholders: Individuals, groups or institutions that have an interest, claim or stake in the
company and can affect or are affected by its decisions and activities.
o Organizational stakeholders: internal to the firm and include employees, managers, board
members, and stockholders.
o Economic stakeholders: external stakeholders such as customers, competitors, creditors,
suppliers and unions
o Societal stakeholders: external stakeholders such as communities, governments and other
regulators at a local, state, federal or supranational level (e.g. the European Union), media
and non-profit or non-governmental organizations.
2. Project design
Selection and design of relevant data collection and analysis methods for solving the identified problems.
Primary vs secondary data
3. Data collection / coding
Sampling: performing an analysis on a selection of the elements in a population, and with this selection we
can draw conclusions about the entire population. See sampling in the summary for more info.
What are the four steps of the sample process? 1. Define the population 2. Determine the sampling frame 3.
Select the sampling procedure 4. Determine the sample size
4. Analysis and interpretation
5. Decision and actions
Lecture 2; essentials of data collection and measurements
Secondary data; data already exists within the company or is collected by others (third parties) for purposes
other than solving the problem at hand. E.g. government publications, books, newspapers, annual reports &
social networks/media. Why is social media secondary data? It is in a public domain and not gathered for the
purpose of the research. If you use Facebook to manipulate something and investigate reactions? It would
not be secondary because this would be gathered for the purpose. Secondary data is gathered to get
insights. We would want to rely on primary data.
, Before collecting data yourself, it is always clever to check the available secondary data first.
Secondary data; low cost, less effort, less time, sometimes information can only be gathered from secondary
data. It is not targeted at the needs of your research.
Primary data: data that you collected yourself.
Interviews, questionnaires, etc. you can do eye tracking, manipulate the environment and see reactions.
Focus groups. Many options.
Advantages of primary research/data: adopt data to research goals/purpose. You can trust the data. The
data is exclusive because your competitors do not have it. Targeted to our individual needs. It is time-
consuming and expensive.
Types of questioning: Qualitative and quantitative.
Qualitative (kwaliteit): - expert interviews and focus groups. exploratory & flexible. Unstructured. Active role
of respondents. To explore the topics. Deeper understanding of motivations. Collection of sensitive data.
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