Data Driven Marketing
Unit 1 – Introduction to Data-Driven Decisions
Unit 1
Data and Technology trends
Data Challenges for businesses
Understanding how companies create
value through data
Building a data-driven strategy
Danone Activity
2
In-class exercise
Mixed-up Sentences
(1) Which statements are not true? (2) Anything missing?
Discussion in class
1. Nowadays, the big data challenges that companies are facing relate to the intrinsic characteristics of
the data itself.
2. Diagnostic (or inquisitive) analytics answer or explore the “what” of a specific managerial issue.
3. Predictive analytics are forward-looking analytics to analyze what will be happening in the future
considering the available historical data.
4. With the arrival of big data and big data analytics, managers are neglecting the use of traditional
analytics to make business decisions.
5. In defining big data, most common framework is the Vs framework that consider the following as the
key characteristists of Big data: volume, veracity, velocity and valence.
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Let’s talk about technology and data trends
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In-class discussion
Kicking-off the discussion: strategy and data
After Watching the video “Top Strategic Technology trends for 2021”
Discussion in class
• Looking at the trends described in the video, identify examples of those trends in
specific industries:
– How are they using data (What types of data?)
– How do they create value (To what extent?)
– What are the key challenges they may face?
• Which industries have been more disrupted by data in your opinion?
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Examples
Talking about data is talking about…
BUT also talking about…
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Examples
Data and the entertainment industry
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Examples
Data and the entertainment industry
To know more: is big data killing creativity?
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Examples
Data and the sport industry
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Examples
Data and the sport industry: WYSCOUT
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Examples
Data and the sport industry: WYSCOUT
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Examples
Data in music and arts
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Unit 1
Data and technology trends
Data Challenges for businesses:
understanding the different types of
analytics
Understanding how companies create
value through data
Building a data-driven strategy
Danone Activity
13
Types of Data
A framework to understand
data and marketing analytics
CRM
Marketing-mix
Privacy and security
Analytics and
analytical
methods
Personalization
Implementing data and
analytics in companies
Source: adapted from Wedel and Kannan (2016)
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Defining analytics
Data analytics is the process of
examining datasets in order to draw
conclusions about the information they
contain, increasingly with the aid of
specialized systems and software.
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Evolution of analytics
Source: Davenport, 2013
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Identifying data and analytics needs
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Types of analytics
DESCRIPTIVE
DIAGNOSTIC
PREDICTIVE
PRESCRIPTIVE
Analytics
Analytics
Analytics
Analytics
Source: adapted from Sivarajah et al. 2017
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Types of analytics
Information
DESCRIPTIVE
Analytics
What
happened in the
Business?
Source: adapted from Sivarajah et al. 2017
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1/Descriptive Analytics
Links the market to the firm
through information
Aid the manager to make
actionable decisions
Principles for systematically collect
and interpret data that can aid
decision-makers
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Types of analytics
Information
Insights
DESCRIPTIVE
DIAGNOSTIC
Analytics
Analytics
What
happened in
the Business?
Source: adapted from Sivarajah et al. 2017
Why
is something
happening in the
Business?
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2/ Diagnostic (inquisitive) analytics
Diagnostic analytics get into the root
cause
Determining the factors and events that
contributed to the outcome
Data discovery, data mining and
correlational analysis
E.g. Attribute importance, sensitivity
analysis, principal component analysis
and conjoint analysis
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Types of analytics
Information
Insights
DESCRIPTIVE
DIAGNOSTIC
PREDICTIVE
Analytics
Analytics
Analytics
What
happened in
the Business?
Why
is something
happening in
the Business?
Source: adapted from Sivarajah et al. 2017
What is likely
To happen in the
future?
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3/ Predictive analytics
Using
customer data
to predict what
they will do in
the future
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Types of analytics
Information
Insights
Decision/Action
DESCRIPTIVE
DIAGNOSTIC
PREDICTIVE
PRESCRIPTIVE
Analytics
Analytics
Analytics
Analytics
What
happened in
the Business?
Why
is something
happening in
the Business?
Source: adapted from Sivarajah et al. 2017
What is likely
So what? And
To happen in the Now what?
future?
What is required
to do more?
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How companies use analytics
Source: Davenport and Harris, 2007
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Descriptive
Summary statistics
and test, metrics,
dashboards,
visualization
Diagnostic
Statistical,
econometrics, and
psychometric models
Predictive
Prediction models,
machine learning,
cognitive systems
Structural models or
optimization methods
Data: information value
Analytics: decision value
Summarizing overview: Data and analytics for marketing decisions
Prescriptive
Source: Wedel and Kannan, 2016
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Insert: Research types (from descriptive to diagnostic)
Exploratory
Reseach
Descriptive
Reseach
Causal
Reseach
(Ambiguous problem)
(Aware of problem)
(Clearly defined problem)
Our sales are declining…why?
What kind of people are buying
our products? Who buys our
competitors products?
Will buyers purchase more of
our product with a change of our
website?
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Insert: Research types (from descriptive to diagnostic)
Exploratory
Reseach
Descriptive
Reseach
Causal
Reseach
(Ambiguous problem)
(Aware of problem)
(Clearly defined problem)
Our sales are declining…why?
What kind of people are buying
our products? Who buys our
competitors products?
Will buyers purchase more of
our product with a change
of our website?
30
Insert: Research types (from descriptive to diagnostic)
Exploratory
Reseach
Descriptive
Reseach
Causal
Reseach
(Ambiguous problem)
(Aware of problem)
(Clearly defined problem)
Our sales are declining…why?
What kind of people are buying
our products? Who buys our
competitors products?
Will buyers purchase more of
our product with a change
of our website?
31
Insert: Research types (from descriptive to diagnostic)
Exploratory
Reseach
Descriptive
Reseach
Causal
Reseach
(Ambiguous problem)
(Aware of problem)
(Clearly defined problem)
Our sales are declining…why?
What kind of people are buying
our products? Who buys our
competitors products?
Will buyers purchase more of
our product with a change
of our website?
32
Insert: Research types (from descriptive to diagnostic) - overview
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In-class
discussion
APPLE is considering the possibility to display music in its
stores.
▪ Managers wonder if the music should have a high (low)
tempo and should be known (original) in order to increase
consumers’ satisfaction and sales.
▪ What is the methodological approach? What are the
techniques they might use?
L’Oréal is considering the possibility to launch a new product
line for men.
▪ Managers wonder if men are interested in “natural beauty
products” and what should be the characteristics of those
products?
▪ What is the methodological approach? What is the technique
they should use?
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Defining data
Information, especially facts or
numbers, collected to be examined
and considered and used to help
decision-making, or
Information in an electronic form that
can be stored and used by a computer
Source: The Cambridge Dictionary
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What is “Big Data”?
Source:
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Big data is… DATA
Information, especially facts or num
bers, collected to
be examined and considered and
used to help decision-making, or
Information in
an electronic form that can
be stored and used by a computer
Source: The Cambridge Dictionary
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Big data is… DATA
“Big Data refers to the explosion in the
quantity (and sometimes, quality) of
available and potentially relevant data,
largely the result of recent and
unprecedented advancements in data
recording and storage technology.”
Source: Diebold, 2000.
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Big Data characteristics: THE 5 Vs
Velocity
Veracity
Variety
Value
Volume
Source: Marr, 2014
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Source:
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Types of data
Structured data
Unstructured data
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Unstructured data and sense-making
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Big Data Analytics vs. Traditional Analytics
Traditional Analytics Big Data Analytics
Focus on
Descriptive analytics
Diagnostic analytics
Predictive analytics
Prescriptive analytics
Data Sets
Limited Data sets
Cleansed data
Simple models
Large scale data sets
More types of data
Raw data
Complex data models
Main analysis
techniques
Causation: what
happened and why?
Correlation: new
insights, finding data
patterns
Source: own elaboration based on various sources
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Correlation is not causation!
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Examples
Beware of Correlation!
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http://www.tylervigen.com/spurious-correlations
Examples
Beware of Correlation!
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Big Data Challenges
Source: Sivarajah et al. 2017
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Unit 1
Data and technology trends
Data Challenges for businesses
Understanding how companies create
value through data
Building a data-driven strategy
Danone Activity
49
How companies create value through data
3 approaches to value creation
Data as a tool
Data as an industry
Data as a strategy
Source: Mazzei and Noble, 2017
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How companies create value through data
1/ Data as a tool
• Active collection of data
• Internal development of analytical
capabilities
• Improvements of specific areas like
product development, marketing,
sales, distribution, customer service…
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How companies create value through data
1/ Data as a tool
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Examples
1/ Data as a tool
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How companies create value through data
2/ Data as an industry
Providers of big data products or
services to a broad range of companies.
Intensive in technical knowledge and
the ability to deal with massive
amounts of structured and unstructured
data.
Offer technology and data-as-a-service
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Examples
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How companies create value through data
3/ Data as a strategy
Big data as a driver of
competitive strategy and new
business models.
Ecosystems development
based on the data that they
are able to accumulate.
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Examples
Data at the core of strategy
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Examples
Data as a strategy: Rolls Royce transformation
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Examples
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Examples
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Examples
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Examples
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How organizations use analytics to gain insights and guide
action?
Top performing organizations
are twice as likely to apply
analytics to different
activities.
Source: Lavalle et al., 2011
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How organizations use analytics to gain insights and guide
action?
The biggest challenges in
adopting analytics are
managerial and cultural.
Source: Lavalle et al., 2011
64
How organizations use analytics to gain insights and guide
action?
Visualizing data differently
and creatively will become
increasingly valuable.
Source: Lavalle et al., 2011
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Unit 1
Data and technology trends
Data Challenges for businesses
Understanding how companies create
value through data
Building a data-driven strategy
Danone Activity
66
To sum-up: 3 key aspects to consider…
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1. Choose the right data
Source Data creatively:
Getting IT support right
In analyzing the internal data that the
Company already has
Obsolete structures that hinder data
sourcing, storage and analysis
In addressing the potential of external and
new sources of data (e.g. unstructured
data from Social Media; external,
monitored processes, sensors…)
Integration of siloes of information
Managing unstructured data
Need to identify and connect the most
important data
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2. Building models to predict and optimize business outcomes
Simple data mining is not enough
Model complexity can be an issue
Huge data sets allows to run statistical test
to identify patterns, but sense-making of
those correlations is what drives value.
Very complex models might me not practical
More targeted strategies pay-off: hypothesisled modeling (e.g. Identifying relevant
factors first)
What’s the least complex model that would
improve performance?
Higher accuracy vs actionable insights
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3. Transforming the organizational capabilities
Develop business-relevant analytics to put
into use
Embed analytics into simple tools
That can be used for the front lines
If big data and analytics are not sync with
day-to-day activities, not useful
Enhance visualization capabilities
Analytics should aid decision-making
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To sum-up: Data to make decisions that matter
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Unit 1
Data and Business strategy
Data Challenges for businesses
Understanding how companies create
value through data
Building a data-driven strategy
Danone Activity
72
In-class
discussion
Brief DANONE is asking your team to help Brand managers to choose
the relevant research technique to respond to each of the
following research problem.
1) The Brand manager of ACTIMEL wants to know their % of sales
according to the type of stores (supermarket, little stores, etc.) in
Spain.
2) The Brand manager of VITALINEA wants to know its brand’s
positioning towards competing brands according to consumers’
perception of quality and price.
3) The brand manager of KIDS’ YOGHURTS wants to know how the
children from 5 to 7 years old consume those yoghurts in order to
adapt the product to this target.
4) The trade manager dealing with CARREFOUR is wondering if it will
be interesting to spray the smell of yoghurt in the stores. He is
wondering what should be the impact of different flavors (natural,
strawberry or vanilla) on sales.
In-class
discussion
Brief
You have to write down your proposals addressing the
following aspects:
•
Methodological approach (exploratory, descriptive,
causal)?
•
Technique to be used (focus group, in-depth interview,
observation, coolhunting, survey, panels, experiment,
neuromarketing)?
•
Question to be asked (open / closed questions, projectives,
etc..) or aspects to observe: Propose precisely 3 main
questions/observations to be asked (if relevant).
Additional Sources and Readings
Barton, D., & Court, D. (2012). Making advanced analytics work for
you. Harvard business review, 90(10), 78-83.
Kiron, D., Shockley, R., Kruschwitz, N., Finch, G., & Haydock, M.
(2012). Analytics: The widening divide. MIT Sloan Management
Review, 53(2), 1.
LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N.
(2011). Big data, analytics and the path from insights to value. MIT
sloan management review, 52(2), 21.
Mazzei, M. J., & Noble, D. (2017). Big data dreams: A framework for
corporate strategy. Business Horizons, 60(3), 405-414.
Data Driven Marketing
Master in Management