Summary Marketing Communication PART D
Week 12 Consumer empowerment
Guest lecture: Joanna Strycharz
Personalized communication: societal and individual consequences
Computational and personalized communication: what is it and how does it work?
- Computational advertising: “à broad umbrella term
Today
- Computational and personalized communication: what is it and how does it work?
- Impact of personalized communication on consumers and their reactions to it
o Privacy calculus
o Consumer empowerment
- ‘Side-effects’ of computational communication – looking ahead
Computational advertising: “broad, data-driven advertising approach relying on or
facilitated by enhances computing capabilities, mathematical models/algorithms, and the
technology infrastructure to create and deliver messages and monitor/surveil an individual’s
behaviors (Huh & Malthouse, 2020).
- Different disciplines (advertising, marketing, and computer science) come together
to deliver advertising messages.
- Also, aspects from computer science come in à new aspect.
1
,Characteristics of computer science:
- Personalized
- Data-driven
- Interactive
- Continuous
- Measurable
Interactive à no one way communication (what Priska told with the figure in the beginning)
New advertising ecosystem
Traditional advertising ecosystem Computational advertising
ecosystem
Advertisers Advertisers
Advertising industry Redefined advertising industry
Consumers Data-driven Redefined consumers
(Government) Redefined government
Consumer advocates
Environmental factors
- We have a government that gives consumers back control over their data.
Personalized communication: “the strategic creation, modification, and adaption of content
and distribution to optimize the fit with personal characteristics, interest, preferences,
communication styles, and behavior (Bol et al., 2018).
- Data around target group, personalized content, and timing.
Interactive process:
- Personalization happens in the process, probably machine learning is used to create
the message.
2
,Personalization process and landscape:
Interaction
Message
(data
delivery
generation)
Message Data
creation collection
Data
processing
- Strong interplay between factors
- The interplay defines what we will see in the upcoming years
- The consumer is central
Consumer perspective on personalization
- Benefits and concerns:
o Benefits: personal relevance
o Concerns: privacy risk
In research this is called the personalization paradox (benefits and concerns come together)
(Awad and Krishnan, 2006). Consumers report to care, but actually don’t do anything about
it. One reason can be the lack of protection.
Definition: “the personalization paradox means that consumers both see benefits of a
personalized message (in terms of convenience, relevance, added value, etc.), but also have
concerns about personalization (in terms of privacy, resignation to loss of control (people
have the feeling they lose control), manipulation, etc.).”
3
, Calculus model of effectiveness
- Personalization paradox (Awad and Krishnan, 2006).
- Privacy calculus (Culnan and Armstrong, 1999).
Privacy calculus: consumers weigh benefits again costs (calculation) which will
define how you react towards to an ad. For example, you see an ad that is
based on your past behavior. Then you realize you know this is a personalized
ad. Unconsciously, you calculate the benefits (personally for u) and concerns
(privacy risk) against each other before you take any action.
Consumer empowerment in context of data-driven communication
- GDPR à regulator roles have changed, a more active role.
o Aim = transparency and control
§ Ensure appropriate projection for individuals in all circumstances
§ Increase transparency for data subjects (consumers)
§ Enhance control over one’s own data
§ Raise awareness
§ Ensure informed and free consent
§ Protect sensitive data
§ Make remedies and sanctions morse effective
Transparency and choice = empowerment?
- The combination of the two had to aim to increase digital literacy of individuals.
- After this law, websites needed to ask consent for personalization (use of personal
data).
4
Week 12 Consumer empowerment
Guest lecture: Joanna Strycharz
Personalized communication: societal and individual consequences
Computational and personalized communication: what is it and how does it work?
- Computational advertising: “à broad umbrella term
Today
- Computational and personalized communication: what is it and how does it work?
- Impact of personalized communication on consumers and their reactions to it
o Privacy calculus
o Consumer empowerment
- ‘Side-effects’ of computational communication – looking ahead
Computational advertising: “broad, data-driven advertising approach relying on or
facilitated by enhances computing capabilities, mathematical models/algorithms, and the
technology infrastructure to create and deliver messages and monitor/surveil an individual’s
behaviors (Huh & Malthouse, 2020).
- Different disciplines (advertising, marketing, and computer science) come together
to deliver advertising messages.
- Also, aspects from computer science come in à new aspect.
1
,Characteristics of computer science:
- Personalized
- Data-driven
- Interactive
- Continuous
- Measurable
Interactive à no one way communication (what Priska told with the figure in the beginning)
New advertising ecosystem
Traditional advertising ecosystem Computational advertising
ecosystem
Advertisers Advertisers
Advertising industry Redefined advertising industry
Consumers Data-driven Redefined consumers
(Government) Redefined government
Consumer advocates
Environmental factors
- We have a government that gives consumers back control over their data.
Personalized communication: “the strategic creation, modification, and adaption of content
and distribution to optimize the fit with personal characteristics, interest, preferences,
communication styles, and behavior (Bol et al., 2018).
- Data around target group, personalized content, and timing.
Interactive process:
- Personalization happens in the process, probably machine learning is used to create
the message.
2
,Personalization process and landscape:
Interaction
Message
(data
delivery
generation)
Message Data
creation collection
Data
processing
- Strong interplay between factors
- The interplay defines what we will see in the upcoming years
- The consumer is central
Consumer perspective on personalization
- Benefits and concerns:
o Benefits: personal relevance
o Concerns: privacy risk
In research this is called the personalization paradox (benefits and concerns come together)
(Awad and Krishnan, 2006). Consumers report to care, but actually don’t do anything about
it. One reason can be the lack of protection.
Definition: “the personalization paradox means that consumers both see benefits of a
personalized message (in terms of convenience, relevance, added value, etc.), but also have
concerns about personalization (in terms of privacy, resignation to loss of control (people
have the feeling they lose control), manipulation, etc.).”
3
, Calculus model of effectiveness
- Personalization paradox (Awad and Krishnan, 2006).
- Privacy calculus (Culnan and Armstrong, 1999).
Privacy calculus: consumers weigh benefits again costs (calculation) which will
define how you react towards to an ad. For example, you see an ad that is
based on your past behavior. Then you realize you know this is a personalized
ad. Unconsciously, you calculate the benefits (personally for u) and concerns
(privacy risk) against each other before you take any action.
Consumer empowerment in context of data-driven communication
- GDPR à regulator roles have changed, a more active role.
o Aim = transparency and control
§ Ensure appropriate projection for individuals in all circumstances
§ Increase transparency for data subjects (consumers)
§ Enhance control over one’s own data
§ Raise awareness
§ Ensure informed and free consent
§ Protect sensitive data
§ Make remedies and sanctions morse effective
Transparency and choice = empowerment?
- The combination of the two had to aim to increase digital literacy of individuals.
- After this law, websites needed to ask consent for personalization (use of personal
data).
4
