The marketing department for a major restaurant chain is interested in testing a Kids Eat Free
campaign to determine if it will help to increase sales. They are interested in piloting the campaign
to determine which day of the week will improve sales the most.
The campaign is launched across 7 cities with each city promoting a different day of the week. The
sales data is collected and provided to a team for analysis. What concern might the analytics team
have regarding data quality across cities?
D
Explanation:
Variation is the degree to which the data values differ from each other or from a central tendency
measure, such as the mean or median. Variation can affect the data quality across cities, as it can
indicate the presence of outliers, errors, noise, or inconsistency in the data collection or processing
methods. Variation can also influence the statistical analysis and interpretation of the results, as it
can affect the significance, confidence, and validity of the findings12. Reference: 1: Guide to Business
Data Analytics, IIBA, 2020, p. 302: Statistics for Business and Economics, David R. Anderson et al.,
2014, p. 83.
A call center has requested to review their sales conversion data for the month. The analyst working
on this request is trying to identify the chart that will effectively present the data, which includes: the
number of leads, the number of calls made, the number of calls completed, the number of
customers interested and the number of sales. What chart should the analyst use to show the values
across each stage of the pipeline?
B
Explanation:
A funnel chart is a type of chart that shows the values of different stages of a process, such as a sales
pipeline, where each stage represents a subset of the previous one. A funnel chart is useful for
showing the conversion rate, the drop-off rate, and the potential revenue or profit at each stage12. A
funnel chart would be an effective way to present the data requested by the call center, as it would
show the number of leads, calls, customers, and sales, as well as the percentage of change between
each stage. Reference: 1: Guide to Business Data Analytics, IIBA, 2020, p. 662: Data Visualization: A
Practical Introduction, Kieran Healy, 2018, p. 233.
A government agency is conducting a study on the performance of 12th grade students' in
mathematics across the country. In particular, they want to understand if there is a relationship
between intelligence and scores, as well as the difference in performance between various locations.
Which combination of inferential statistics procedures should be used?
C
Explanation:
A correlation co-efficient is a measure of the strength and direction of the linear relationship
between two variables, such as intelligence and scores. A correlation co-efficient can range from -1
to 1, where -1 indicates a perfect negative relationship, 0 indicates no relationship, and 1 indicates a
perfect positive relationship12. An analysis of variance (ANOVA) is a procedure that tests whether
the means of two or more groups are significantly different from each other, such as the
performance of students across various locations. ANOVA can compare the variation within each
group and the variation between groups to determine if there is a statistically significant difference
among the group means34. Reference: 1: Guide to Business Data Analytics, IIBA, 2020, p. 582:
Statistics for Business and Economics, David R. Anderson et al., 2014, p. 7133: Guide to Business Data
Analytics, IIBA, 2020, p. 594: Statistics for Business and Economics, David R. Anderson et al., 2014, p.
849.
An organization's customers are categorized based on the amount of purchases completed over the
last 12 months. The analytics team would like to ensure the accuracy of their survey results and
decide to randomly select 500 customers to participate in a survey from this large pool of customers.
This is an example of:
A
Explanation:
Stratified sampling is a technique that divides the population into homogeneous subgroups (strata)
based on a relevant characteristic, such as the amount of purchases, and then randomly selects a
proportional number of elements from each subgroup to form the sample. Stratified sampling
ensures that the sample is representative of the population and reduces the sampling error and
bias12. Reference: 1: Guide to Business Data Analytics, IIBA, 2020, p. 312: Statistics for Business and
Economics, David R. Anderson et al., 2014, p. 262.
The results of the data analytics work led to some clear and strongly supported outcomes and the
analytics team is very confident in their recommendations; particularly given that the payback on the
required changes are a short 3 months. However, there is concern because the organization operates
in a highly regulated environment and some new regulatory changes are being considered with
announcements and implementation in the next 6 months. Under these conditions the team decides
to:
C
Explanation:
The best option for the team under these conditions is to identify and carefully document the
assumptions for their recommendation, such as the expected impact of the regulatory changes, the
risks and benefits of implementing the changes before or after the announcements, and the
sensitivity of the results to different scenarios. This way, the team can communicate their findings
and recommendations clearly and transparently, while also acknowledging the uncertainty and
limitations of their analysis. This can help the decision makers to evaluate the trade-offs and make
informed choices12. Reference: 1: Guide to Business Data Analytics, IIBA, 2020, p. 242: Data-Driven
Decision Making: A Primer for Beginners, Anand Rao, 2018, 1.
A colleague proposes measuring job satisfaction by asking the question "What is your salary?". What
is the concerning factor about this question?
A
Explanation:
Validity is the extent to which a measure or a question accurately captures the intended concept or
construct1. The question “What is your salary?” is not a valid measure of job satisfaction, as it does
not reflect the various aspects of job satisfaction, such as work environment, recognition, autonomy,
growth, etc. Salary is only one possible factor that may influence job satisfaction, but it is not a direct
or comprehensive indicator of it23. Therefore, the question is not valid for measuring job
satisfaction. Reference: 1: Guide to Business Data Analytics, IIBA, 2020, p. 302: Job Satisfaction:
Application, Assessment, Causes, and Consequences, Paul E. Spector, 1997, p. 23: Job Satisfaction
Survey, 1.
A marketing director has asked the question 'How many product purchases are expected this coming
year given the current marketing campaign?". What type of analytics would be performed to answer
this question?
B
Explanation:
Predictive analytics is a type of analytics that uses historical and current data, as well as statistical
and machine learning techniques, to forecast future events or outcomes, such as product purchases,
customer behavior, or market trends12. To answer the question ‘How many product purchases are
expected this coming year given the current marketing campaign?’, predictive analytics would be
performed to estimate the demand and sales based on the existing data and the marketing campaign
variables. Reference: 1: Guide to Business Data Analytics, IIBA, 2020, p. 182: Predictive Analytics: The
Power to Predict Who Will Click, Buy, Lie, or Die, Eric Siegel, 2016, p. 3.
An insurance company has seen an upward trend in winter-related accidents over the past three
years. The company has just completed an analytics study to better understand the primary reasons
for these accidents and assess how many of the drivers were using winter tires. This analysis will help
the company decide how to move forward with drivers not taking precautionary measures during
winter. What type of analysis will help in determining the primary reasons and percentage of those
drivers with winter tires?
D
Explanation:
Descriptive analytics is a type of analytics that summarizes and visualizes the data to provide an
overview of what has happened or is happening, such as the trend of winter-related accidents over
the past three years, or the percentage of drivers using winter tires12. Diagnostic analytics is a type
of analytics that explores and analyzes the data to understand why something has happened or is
happening, such as the primary reasons for these accidents, or the factors that influence the drivers’
decisions13. To answer the question, both descriptive and diagnostic analytics would be needed to
provide the relevant information and insights for the company. Reference: 1: Guide to Business Data
Analytics, IIBA, 2020, p. 182: Business Analytics: Data Analysis & Decision Making, S. Christian
Albright and Wayne L. Winston, 2015, p. 53: Data Science for Business, Foster Provost and Tom
Fawcett, 2013, p. 13.
A Human Resource manager recently learned that their competitor reduced employee attrition rates
by 20% after implementing personality tests as part of their screening process. Intrigued by the idea,
the manager suggests collecting data on personality tests and attrition rates over the next year. The
data from this year is then analyzed to explore possible relationships. What type of analytics has the
team been asked to perform?
B
Explanation:
Descriptive analytics is a type of analytics that summarizes and visualizes the data to provide an
overview of what has happened or is happening, such as the attrition rates and the personality test
scores of the employees12. The team has been asked to perform descriptive analytics to explore
possible relationships between the data variables, without making any predictions or prescriptions
for the future. Reference: 1: Guide to Business Data Analytics, IIBA, 2020, p. 182: Business Analytics:
Data Analysis & Decision Making, S. Christian Albright and Wayne L. Winston, 2015, p. 5.
A large telecommunications company wants to increase their Average Revenue Per User per month
by 5%, by end of year, to increase revenue in a highly competitive market. From a SMART target
perspective, what is missing?
D
Explanation:
A SMART target is one that is specific, measurable, achievable, relevant, and time-bound1. The target
of increasing the Average Revenue Per User (ARPU) per month by 5%, by end of year, to increase
revenue in a highly competitive market is missing the specificity criterion, as it does not mention
which product group or line the target applies to. The target should be more specific and clear about
the scope and context of the desired outcome, such as which segment, region, or service the target
relates to23. Reference: 1: Guide to Business Data Analytics, IIBA, 2020, p. 192: SMART Goals: How to
Make Your Goals Achievable, MindTools, 2021, 13: How to Set SMART Marketing Goals, CoSchedule,
2021, 2.