LECTURES CUSTOMER AND MARKETING ANALYTICS
Lecture 1: Introduction
Why do firms do research in Marketing?
Marketers use “the right” principle “to do” Marketing
get the right products to the right people at the right place at the right time at the right
price using the right promotion techniques.
To be “right” in marketing: need for decision making information that reduces uncertainty to
aid in smarter managerial decision making.
Marketing research
Planning, collection, and analysis of data relevant to marketing decision making and the
communication of the results of this analysis to management.
It can be micro-level (individual) or macro-level (market) in nature.
Value of marketing research:
Decreased uncertainty
Increased likelihood of a correct decision
Improved marketing performance and resulting higher profits
1. Identifying the problem and problem definition
Marketing decision problem Marketing research problem
Asks what the decision-maker needs to do Asks what information is needed and how it
can best be obtained
Action oriented Information oriented
Focuses on the symptoms Focuses on the underlying causes
2. From decision problem to research problem
Example:
Decision problem: What logo design should we use for the Olympics in London?
Research problem: How much do people like the different proposed logos?
3. Classifying marketing research
a. Type of data
Quantitative research Qualitative research
Focus on numbers Not concerned with numbers
Profiling detailed usage and behavior Mapping the customer’s overall range of
behavior and attitude
Highlighting variations between sub-groups Pinpointing motivations behind people’s
behavior
Precisely measuring consumer preferences Stimulating new and creative ideas
, b. Research design
Exploratory research Descriptive research Causal research
Emphasis on gaining ideas Often guided by an initial Determining a cause-and-
and insights hypothesis effect relationship
Clarify concepts Describe characteristics of Reveal associations between
certain group changes in variables
Develop specific hypotheses Examine associations
between two or more
variables
Make specific predictions
Qualitative research Quantitative research Quantitative research
c. Data source
Primary data Secondary data
Data collected specifically to answer the Data previously collected for purposes
question(s) posed by the research (e.g., other than the research at hand (e.g.,
demographics) customer transaction databases)
Syndicated research
Large-scale marketing research that is
undertaken by a research firm and sold to
clients/companies
Ncpmobile
Lecture 2: Basic Statistical Analysis
1. Screen dataset: Investigate quality of data
a. Error, missing values, inconsistencies
2. Explore and analyze the data
a. Describe and summarize data: A complete run-down analysis of all the
variables in your dataset one-at-a-time (univariate statistics)
b. Inferential analysis: learning about “the world” (univariate statistics)
c. Differential analysis (bivariate)
d. Associative analysis: (bivariate)
Descriptive analysis: used to describe the data set; frequency distributions and summary
statistics.
Inferential analysis: used to generate conclusions about the population’s characteristics
based on the sample data; confidence intervals and hypothesis testing.
Differences analysis: used to compare the mean of the responses of one group to that of
another group; testing differences between samples.
Associative analysis: determines the strength and direction of relationships between two or
more variables; cross-tabulations and correlations.
Lecture 1: Introduction
Why do firms do research in Marketing?
Marketers use “the right” principle “to do” Marketing
get the right products to the right people at the right place at the right time at the right
price using the right promotion techniques.
To be “right” in marketing: need for decision making information that reduces uncertainty to
aid in smarter managerial decision making.
Marketing research
Planning, collection, and analysis of data relevant to marketing decision making and the
communication of the results of this analysis to management.
It can be micro-level (individual) or macro-level (market) in nature.
Value of marketing research:
Decreased uncertainty
Increased likelihood of a correct decision
Improved marketing performance and resulting higher profits
1. Identifying the problem and problem definition
Marketing decision problem Marketing research problem
Asks what the decision-maker needs to do Asks what information is needed and how it
can best be obtained
Action oriented Information oriented
Focuses on the symptoms Focuses on the underlying causes
2. From decision problem to research problem
Example:
Decision problem: What logo design should we use for the Olympics in London?
Research problem: How much do people like the different proposed logos?
3. Classifying marketing research
a. Type of data
Quantitative research Qualitative research
Focus on numbers Not concerned with numbers
Profiling detailed usage and behavior Mapping the customer’s overall range of
behavior and attitude
Highlighting variations between sub-groups Pinpointing motivations behind people’s
behavior
Precisely measuring consumer preferences Stimulating new and creative ideas
, b. Research design
Exploratory research Descriptive research Causal research
Emphasis on gaining ideas Often guided by an initial Determining a cause-and-
and insights hypothesis effect relationship
Clarify concepts Describe characteristics of Reveal associations between
certain group changes in variables
Develop specific hypotheses Examine associations
between two or more
variables
Make specific predictions
Qualitative research Quantitative research Quantitative research
c. Data source
Primary data Secondary data
Data collected specifically to answer the Data previously collected for purposes
question(s) posed by the research (e.g., other than the research at hand (e.g.,
demographics) customer transaction databases)
Syndicated research
Large-scale marketing research that is
undertaken by a research firm and sold to
clients/companies
Ncpmobile
Lecture 2: Basic Statistical Analysis
1. Screen dataset: Investigate quality of data
a. Error, missing values, inconsistencies
2. Explore and analyze the data
a. Describe and summarize data: A complete run-down analysis of all the
variables in your dataset one-at-a-time (univariate statistics)
b. Inferential analysis: learning about “the world” (univariate statistics)
c. Differential analysis (bivariate)
d. Associative analysis: (bivariate)
Descriptive analysis: used to describe the data set; frequency distributions and summary
statistics.
Inferential analysis: used to generate conclusions about the population’s characteristics
based on the sample data; confidence intervals and hypothesis testing.
Differences analysis: used to compare the mean of the responses of one group to that of
another group; testing differences between samples.
Associative analysis: determines the strength and direction of relationships between two or
more variables; cross-tabulations and correlations.
