BUS 421 Marketing
Analytics | Study Guide,
Exam Review & Practice
Questions
|Guaranteed success|
,components of marketing analytics ●3 Components:
○Data
○Analytics
○Visualization
types of data primary and secondary
primary data •Are collected specially to address a specific research objective (e.g. survey
data, customer feedback, experiments)
secondary data Were collected for some purpose other than solving the present problem (e.g.,
census data, syndicated data, primary data collected previously)
advantages of secondary data ●Cost (internet has made search cost low)
●Time
●At times more accurate, at times the only alternative
disadvantages of secondary data ●Problems of fit
○Inappropriate level of aggregation (time, company, region)
○Wrong unit of analysis (e.g. you are interested in the price
○elasticity for the small box but the data are about the large one)
●Problems of accuracy
○Uncertainty about supplier/collection methods
○Availability of multiple sources
steps for using data 1.Data Collection
2.Data Storage/Management
3.Data Cleaning
4.Data Integration
5.Data Analysis
6.Data Visualization
Data Collection ●Obtain the right data to achieve the right information, detailed information
about user interactions within their own website, social media, and other digital
platforms
○Time spent on website
○Clicks
○Conversion rates
●Internal sources like revenue and cost information
data storage and management ●After data is collected, it must be stored and managed
●Data management platforms:
○Adobe Audience Manager
○Oracle BlueKai
,data cleaning the process of checking data for errors after the data have been entered in a
computer file
Tools like Google refine will help clean messy data
data integration Combines data from multiple sources:
1.Relational databases
2.Data management platforms
3.Statistical programming languages
4.Commercial data visualization
Why Data Cleaning? ●Data in the real world are dirty
○incomplete: lacking attribute values, lacking certain attributes of interest, or
containing only aggregate data
○noisy: containing errors or outliers
○inconsistent: containing discrepancies in codes or names
●No quality data, no quality results!
○Quality decisions must be based on quality data
○Data warehouse needs consistent integration of quality data
data cleaning ●Data cleaning tasks
○Fill in missing values
○Identify outliers and smooth out noisy data
○Correct inconsistent data
data analysis Excel, R, and Python are some of the many tools to transform complex data
into insights:
●Marketing mix models
●Cluster analysis
●Moderation
●AB testing
Experimental design
Data Visualization describes technologies that allow users to see or visualize data to transform
information into a business perspective
●When data is presented in interactive maps and charts, people will
understand the information quickly and easily
○Tableau and GIS
Why structure data? •Because it comes to us in ways that would be hard to analyze.
•Think about a database of newspaper articles from the last 50 years.
•How would you "analyze" those millions of "unstructured" articles?
features of unstructured data ●Do not reside in traditional databases and data warehouses
●May have an internal structure, but does not fit a relational data model
●Generated by both humans and machines
○Textual and multimedia content
○Machine-to-machine communication
Big Data •Data sets of such size, complexity and volatility that their business value cannot
be fully realised with existing data capture, storage, processing, analysis and
management capabilities
, Challenges of Big Data ●Validity of statistical inference
○Sample biases
○Model biases
●Privacy and public trust
○Disclosure threat due to mosaic effect
●Data integrity
○Missing, inconsistent and inaccurate data
○Volatile sources
●Data ownership and access
○Public good versus commercial advantage
○Value of private sector data
why integrate data Two or more database sources (e.g., two data sources or tables within a single
data source) may offer more insights together than separate
Analytics the use of mathematics and statistics to analyze data
•Tools helpful for analytics:
●Excel is a spreadsheet tool
●Excel, R, and Python are good marketing analytics tools
●SQL or structured query language, a database language
marketing attribution the science of assigning credit to unique events that lead to key marketing
conversions like the sale of a product to a customer.
predictive anlaytics - the practice of interpreting data to predict the likelihood of future marketing
outcomes.
marketing optimization - the process of refining the marketing efforts of a firm to maximize marketing
outcomes.
Visualization ●Graphs and other visuals help communicate findings from analytics and data
●Visualization facilitates good storytelling
●Visualization Tools: Tableau, Excel, Power BI and geographic information
systems
which is least likely to be an example of unstructured d. spreadsheet
data?
a. chat
b. tweets
c. email
d. spreadsheet
IBM top priorities for marketing increase digital acumen
Analytics | Study Guide,
Exam Review & Practice
Questions
|Guaranteed success|
,components of marketing analytics ●3 Components:
○Data
○Analytics
○Visualization
types of data primary and secondary
primary data •Are collected specially to address a specific research objective (e.g. survey
data, customer feedback, experiments)
secondary data Were collected for some purpose other than solving the present problem (e.g.,
census data, syndicated data, primary data collected previously)
advantages of secondary data ●Cost (internet has made search cost low)
●Time
●At times more accurate, at times the only alternative
disadvantages of secondary data ●Problems of fit
○Inappropriate level of aggregation (time, company, region)
○Wrong unit of analysis (e.g. you are interested in the price
○elasticity for the small box but the data are about the large one)
●Problems of accuracy
○Uncertainty about supplier/collection methods
○Availability of multiple sources
steps for using data 1.Data Collection
2.Data Storage/Management
3.Data Cleaning
4.Data Integration
5.Data Analysis
6.Data Visualization
Data Collection ●Obtain the right data to achieve the right information, detailed information
about user interactions within their own website, social media, and other digital
platforms
○Time spent on website
○Clicks
○Conversion rates
●Internal sources like revenue and cost information
data storage and management ●After data is collected, it must be stored and managed
●Data management platforms:
○Adobe Audience Manager
○Oracle BlueKai
,data cleaning the process of checking data for errors after the data have been entered in a
computer file
Tools like Google refine will help clean messy data
data integration Combines data from multiple sources:
1.Relational databases
2.Data management platforms
3.Statistical programming languages
4.Commercial data visualization
Why Data Cleaning? ●Data in the real world are dirty
○incomplete: lacking attribute values, lacking certain attributes of interest, or
containing only aggregate data
○noisy: containing errors or outliers
○inconsistent: containing discrepancies in codes or names
●No quality data, no quality results!
○Quality decisions must be based on quality data
○Data warehouse needs consistent integration of quality data
data cleaning ●Data cleaning tasks
○Fill in missing values
○Identify outliers and smooth out noisy data
○Correct inconsistent data
data analysis Excel, R, and Python are some of the many tools to transform complex data
into insights:
●Marketing mix models
●Cluster analysis
●Moderation
●AB testing
Experimental design
Data Visualization describes technologies that allow users to see or visualize data to transform
information into a business perspective
●When data is presented in interactive maps and charts, people will
understand the information quickly and easily
○Tableau and GIS
Why structure data? •Because it comes to us in ways that would be hard to analyze.
•Think about a database of newspaper articles from the last 50 years.
•How would you "analyze" those millions of "unstructured" articles?
features of unstructured data ●Do not reside in traditional databases and data warehouses
●May have an internal structure, but does not fit a relational data model
●Generated by both humans and machines
○Textual and multimedia content
○Machine-to-machine communication
Big Data •Data sets of such size, complexity and volatility that their business value cannot
be fully realised with existing data capture, storage, processing, analysis and
management capabilities
, Challenges of Big Data ●Validity of statistical inference
○Sample biases
○Model biases
●Privacy and public trust
○Disclosure threat due to mosaic effect
●Data integrity
○Missing, inconsistent and inaccurate data
○Volatile sources
●Data ownership and access
○Public good versus commercial advantage
○Value of private sector data
why integrate data Two or more database sources (e.g., two data sources or tables within a single
data source) may offer more insights together than separate
Analytics the use of mathematics and statistics to analyze data
•Tools helpful for analytics:
●Excel is a spreadsheet tool
●Excel, R, and Python are good marketing analytics tools
●SQL or structured query language, a database language
marketing attribution the science of assigning credit to unique events that lead to key marketing
conversions like the sale of a product to a customer.
predictive anlaytics - the practice of interpreting data to predict the likelihood of future marketing
outcomes.
marketing optimization - the process of refining the marketing efforts of a firm to maximize marketing
outcomes.
Visualization ●Graphs and other visuals help communicate findings from analytics and data
●Visualization facilitates good storytelling
●Visualization Tools: Tableau, Excel, Power BI and geographic information
systems
which is least likely to be an example of unstructured d. spreadsheet
data?
a. chat
b. tweets
c. email
d. spreadsheet
IBM top priorities for marketing increase digital acumen
