MKTG3504
APPLIED MARKET RESEARCH
Lecture 13
Data Dashboards and
Course Review
WHAT ARE WE ACHIEVING TODAY?
▪ Data dashboards
▪ A3 Power BI
▪ AI and market research
▪ Course review
DATA DASHBOARDS
Lecture 13
Data Dashboards
and Course Review
WHAT IS A DATA DASHBOARD
Both dashboards and reports are commonly utilized to collect and analyze data. So
what makes them different?
Reports tend to be broader and feature historic data. Because they must be delivered
periodically, they represent a snapshot of a specific area or organization rather than
its real-time situation. Dashboards, on the other hand, are built to visualize and
organize data in real-time.
Broadly speaking, reports usually have a more narrow focus. They serve the purpose of
providing a deep-dive view into a data set and tend to concentrate on a single item
or event.
On the other hand, dashboards tend to have a high-level view of broad amounts of
data and are created to answer a single question. That question can be broad, such
as, “how was our site performance last month?” Or more specific, such as, “how many
units did we sell?” Or perhaps something that’s a little harder to track without
specialized expertise, such as, “is our overall efficiency improving?”
HOW DO DASHBOARDS WORK?
Dashboards take data from different sources and aggregate it so non-technical
people can more easily read and interpret it.
With interactive elements, it helps anyone using the dashboard better understand
certain points, explore areas of increased interest, and support more questioning to
arrive at key insights or make key decisions.
The ultimate purpose of a dashboard is to present data in a clear and
approachable way that facilitates the decision-making process for its users. An
effective dashboard is one that is accessible, read, and used by its users.
DATA DASHBOARD VERSUS DATA VISUALISATION
Two common terms when it comes to analytics and reporting are “data dashboard”
and “data visualization.” What’s the difference?
Data visualization is a way of presenting data in a visual form to make it easier to
understand and analyze.
Data dashboards are a summary of different, but related data sets, presented in a
way that makes the related information easier to understand. Dashboards are a type
of data visualization, and often use common visualization tools such as graphs,
charts, and tables.
KEY CHARACTERISTICS OF A DASHBOARD
• Interactive – can use filters to change data being shown
• Real-time analytics – updates automatically
• Point-in-time – spotlights the measures for a specific point in time
• Multiple metrics/sources - shows multiple metrics on one page/screen
• Intuitive visualisations – uses icons to reflect the measures (gauges, dials, clocks
etc. )
• Infographic elements – choose the best visual for each metric
• Focus on KPIs – only choose the most relevant and important metrics to show
• Easy - very easy to understand and interpret (often used at Exec level)
• Good design - good use of space; Visually pleasing, innovative and interesting
• Labels – be concise but clearly label every element
HOW TO CREATE A DASHBOARD
1.Define your audience and goals: Ask who you are building this dashboard for and what do they need to
understand? Once you know that, you can answer their questions more easily with selected visualizations
and data.
2.Choose your data: Most businesses have an abundance of data from different sources. Choose only
what’s relevant to your audience and goal to avoid overwhelming your audience with information.
3.Double-check your data: Always make sure your data is clean and correct before building a dashboard.
The last thing you want is to realize in several months that your data was wrong the entire time.
4.Choose your visualisations: There are many different types of visualizations to use, such as charts, graphs,
maps, etc. Choose the best one to represent your data. For example, bar and pie charts can quickly
become overwhelming when they include too much information.
5.Use a template: When building a dashboard for the first time, use a template or intuitive software to save
time and headaches. Carefully choose the best one for your project and don’t try to shoehorn data into a
template that doesn’t work.
6.Keep it simple: Use similar colors and styles so your dashboard doesn’t become cluttered and
overwhelming.
7.Iterate and improve: Once your dashboard is in a good place, ask for feedback from a specific person
in your core audience. Find out if it makes sense to them and answers their questions. Take that feedback
to heart and make improvements for better adoption and understanding.
TYPES OF DASHBOARDS
• Business dashboards
• Executive dashboards
• KPI dashboards
• Project dashboards
• Performance dashboards
• Website dashboards
• Operations dashboards
• Industry dashboards
• Healthcare dashboards
• Marketing dashboards
• Sales dashboards
Popular dashboard software
CRICOS code 00025B
A3 Power BI Dashboards and Insights
Final assessment:
Available to complete: Monday 27th May 2024
Due 12th June 2024
CRICOS code 00025B
• Data visualization is the practice of translating information into a visual context, such as a map or graph,
to make data easier for the human brain to understand and pull insights from.
• The main goal of data visualization is to make it easier to identify patterns, trends and outliers in large
data sets.
• Data visualization is important for almost every career.
• Power BI is one of the main players in this space.
Discussion can be found at https://www.techtarget.com/searchbusinessanalytics/definition/data-visualization
Background
Applied Market Research | Week 2 17
UQ Business School
CRICOS code 00025B
Power BI
18
CRICOS code 00025B
• The final assignment has two components.
1. Complete Power BI course experience; and
2. Complete a reflection on the course, the use of data in decision-making and the development of their
professional practice.
Task
Applied Market Research | 19
UQ Business School
CRICOS code 00025B
Rather than only one option, there are a range of options depending on what you would like to achieve.
Power BI Experience Options
Applied Market Research | 20
UQ Business School
CRICOS code 00025B
The reflection will ask about three main themes around reflection on the course, the use of data in
decision-making and the development of their professional practice.
The minimum word count across 4 questions in Part 1 and Part 2 is 1200 words (200 words minimum for
each individual question).
What do you need to know in order to write the reflection?
- You will need to complete your Power BI experience first, and then reflect on the experience in Part 1,
and
- You will need to reflect about evidence-based decision making and the Ethical Career article in Part 2.
Reflection
Applied Market Research | 21
UQ Business School
CRICOS code 00025B
Reflection – What do we look for
Applied Market Research | 22
UQ Business School
CRICOS code 00025B
- You have to complete the Power BI experience in order to be marked on your reflection.
- AI tools are not allowed to be used for a reflection task. Inherently this reflection should
be yours and unique to your experience. See the AI notice from the ECP.
AI Notice: This assessment task evaluates students' abilities, skills and knowledge without the aid of generative Artificial
Intelligence (AI). Students are advised that the use of AI technologies to develop responses is strictly prohibited and may
constitute student misconduct under the Student Code of Conduct.
AI Course Context: This assessment involves a dashboard and/or analysis experience and is, as such, not appropriate for the
use of AI. The reflection needs to be a personal reflection of the individual student who completed the certification/experience. AI
involvement in reflections will not be counted as a personal reflection and the submission will not be counted as
meeting the assessment requirements. [This means you will get 0]
Key things to note
Applied Market Research | 23
UQ Business School
AI AND MARKET
RESEARCH
Lecture 13
Data Dashboards
and Course Review
AI AND MARKET RESEARCH
Artificial Intelligence is the
ability of computers to
perform tasks that normally
require human intelligence,
such as speech and image
recognition, iterative
learning and creative
thinking.
It is widely thought that AI
will revolutionise the market
research industry, but few
market researchers know
exactly what AI will do.
Source: How AI will reinvent the market research industry, Qualtrics
FRIEND OR
FOE?
Source: How AI will reinvent the market
research industry, Qualtrics
A I
A P P L I C A T I O N S
I N M A R K E T
R E S E A R C H
EXISTING
USE CASES
CURRENT USAGE BY SUPPLIERS IS SUPPLEMENTARY NOT
SUBSTITUTING
Source: GRIT Report 2023
SOME
PROBLEM
POINTS TO
BE
ADDRESSED
ANALYSING BIG DATA
Source: GRIT Report 2023
OTHER USE CASES
Source: GRIT Report 2023
FUTURE
OUTLOOK
Source: How AI will reinvent the market
research industry, Qualtrics
COURSE REVIEW
Lecture 13
Data Dashboards
and Course Review
COURSE
AIMS
This course aims to have you:
1. be able to explain the various research
and analytic methods used in market
research and demonstrate their
appropriate application in an applied
setting.
2. describe the conceptual and statistical
issues related to the analysis of their own
quantitative data and chosen research
methods.
3. articulate the managerial significance of
their findings and evaluate key ethical
issues for marketing and research.
WHAT IS MARKET RESEARCH?
▪ Market and social research means the systematic investigation of
the behaviour, needs, attitudes, opinions, motivations or other
characteristics of a whole population or a particular part of a
population, in order to provide objective, accurate and timely
information to clients (government, commercial and not-for-profit
organisations) about issues relevant to their activities, to support
their decision-making processes.
TYPES OF
RESEARCH
Consumer
research
Social
research
Market
research
Pricing
research
Product
research
Media
research
Polling
Customer
experience
research
Advertising
research
Policy
research
People
Plan
Scoping workshop
Research questions
Feasibility / ethics
Proposal / project plan
Explore
Literature review
Tertiary / secondary data analysis
Stakeholder consultations
Observational research
Synthesis
Conduct
Qualitative
Quantitative
Analyse
Data cleaning and processing
Analysis workshopping
Early hypotheses
Insight
Topline reporting
Storytelling arc
Interpretation
Workshopping recommendations
Reporting
Drafting report
Stakeholder storyboarding
Finalise report
Activation
Presentation
Workshop
Monitoring
“WITH GREAT
POWER, COMES
GREAT
RESPONSIBILITY”
NON-METRIC METRIC
EXPLORATORY
RESEARCH
Research which is undertaken using an
unstructured research approach with a
small number of carefully selected
individuals to produce non-quantifiable
insights into behaviour, motivations and
attitudes.
EXPERIMENTAL
RESEARCH
Experimental research is a study
conducted with a scientific approach
using two sets of variables. The first set
acts as a constant, which you use to
measure the differences of the second
set. Quantitative research methods, for
example, are experimental.
QUESTIONNAIRE
DESIGN
Questionnaire design is one of the most
critical stages in the survey research
process.
A questionnaire is only as good as the
questions it asks—ask a bad question, get
bad results.
Composing a good questionnaire
appears easy, but it is usually the result of
long, painstaking work.
STRUCTURE OF THE QUESTIONNAIRE
INTRODUCTION
Always include
an
introduction
that frames
the questions
appropriately
– not too long!
SCREENERS
Always include
screener
questions to
remove any
ineligible
respondents
TOPIC MODULES
Think about
sections or
modules and
what order
they should go
in – think
about order
bias
PROFILING
Typically
conclude with
demographics
and profiling
questions
TYPES OF QUESTIONS
Question Type Definition
Dichotomous Requires the respondent to choose one of two alternatives
(e.g., yes or no).
Determinant-choice /
multiple choice
Requires the respondent to choose one response from
among multiple alternatives (e.g., A, B, or C).
Frequency Asks for an answer about general frequency of occurrence
(e.g., how many times have you…?).
Checklist Allows the respondent to provide multiple answers to a
single question by checking off items.
Scales Rating, ranking, max-diff or trade-offs
HOW SHOULD
QUESTIONNAIRE
BE ARRANGED?
Arrangement Definition
Logical order Sequence questions in sets/sections to
help guide the respondents through the
questionnaire. Think about which
questions make sense to be asked
together.
Funnel
approach
Start with general information first and
move to more specific questions through
the questionnaire.
Build trust Start with simple and interesting questions
or an open-ended question to allow the
respondent to warm up and build trust.
Sensitive questions should go towards the
back of the questionnaire.
Unbiased order Awareness before brand measures
Overall satisfaction before and after
detailed satisfication
HOW SHOULD THE QUESTIONNAIRE BE PRETESTED?
▪ Qualitative testing (cognitive testing)
▪ Link testing – testing the programming, routing, looping, filters
▪ Pilots – small-scale pre-testing used to detect weaknesses in
instruments and provide preparation and training of the research
team. Note:
o Mode of administration should be the same as the actual
questionnaire.
o Pre-testing seeks to determine whether respondents have any
difficulty understanding the questionnaire and whether there are
any ambiguous or biased questions.
o A tabulation of the results of a pretest to help determine whether
the questionnaire will meet the objectives of the research.
KEY ETHICAL PRINCIPLES
Do no harm
Integrity
Validity
Power
Transparency
SELF-REGULATION
General rules of professional behaviour
Distinguishing research from other activities
Disclosure of identifiable research information
Proposals, commissioning and design
Data collection and handling
Children, young people and other vulnerable groups
Observation and recording
Re-contacting participants
Data provision and reporting
Data storage and security
Cross border disclosure of identifiable research
information Responsibility to carry out professional
activities
in accordance with the Code
Implementation of the Code
KEY
ANALYTICS
Factor analysis and cluster
analysis: identify new latent
variables; providing additional
insight unobtainable through
descriptive analysis of observable
variables
Regression analysis: delivers
predictive power; and the ability
to measure relationships between
independent and dependent
variables; a great way to derive
importance
Anova and t-test: are tests of
significance; so we can distinguish
between meaningful differences
and sample fluctuations
Dependent variable
Categorical Continuous
Independent variable
Categorical
Chi-squared test
ANOVA
T-test
Continuous
Regression Correlation Test
CHARTING
• Always title the chart and use
labels so that the data can be
correctly interpreted
• Put a base/filter on the chart to
clearly label what data is being
shown and what it represents
• Always show the sample size
• Keep scales as consistent as
possible across charts in one
report
• Label statistically significant
differences if they exist to
highlight key findings
4.3
2.5
3.5
4.5
2.4
4.4
1.8
2.8
2 2
3
5
0
1
2
3
4
5
6
7
8
9
10
Category 1 Category 2 Category 3 Category 4
Average Product Purchase
Brand 1 Brand 2 Brand 3
Q. How many times did you buy [product] in the last three months?
Base: Quarter 1, 2024
Filter: Males aged 18-24, n=425
A DATA DUMP ≠ REPORT
What is the purpose of/type of research?
Purpose: Foundational
Method: Exploratory
Purpose: Activation
Method: Experimental
Market Market understanding Market growth activation
Brand Brand architecture/strategy
Brand positioning
Brand campaign testing
Advertising Creative strategy/development Campaign testing
Product / service Product strategy
Pricing strategy
Product development
Price point testing / price sensitivity
Customer Customer strategy Customer experience
Channel Channel strategy / mix Channel optimisation
Analysis: descriptive
Recommendations: Key
insights/learnings
Analysis: test results
Recommendations:
Activations
RESEARCH FINDING VERSUS INSIGHT VERSUS
RECOMMENDATION
Finding Insight Recommendation
What the research found -
the data result
What the result could
mean, what the possible
implications could be
What should be done
about it
THINK LIKE A BUSINESS CONSULTANT
© Ipsos58 ‒
The experiment is over, so
what did you think?
61
STAY IN TOUCH
https://www.linkedin.com/in/
kathy-benson-rastro/
THE END