Marketing Strategy
in Service Business
- amegaro@unisa.it -
Programme
• Marketing and Service Marketing: an overview
• Marketing plan
• Complexity management
• New marketing strategies approaches: Relationship Marketing
and Many-to-Many network; Experiential marketing;
Unconventional marketing
• New marketing vision: Service Research - from S-D logic &
Service Science to service ecosystems & service systems
• Technologies as Decision Support Systems for marketing
strategies
Case studies
Examples
Project work
Agenda: Lesson 5
• Technologies as Decision Support Systems for marketing
strategies
Examples
Data-driven decision making
5.1
Need for new technologies in
marketing
Today innovation is increasingly associated with the function
of encouraging companies in pursuing and maintaining their
survival, through the achievement of a competitive
advantage.
Service innovation occurs within networks when existing
value propositions are modified through a process of
integrating existing resources or by inventing new resources
and involves the creation, renewal and transformation of
pre-existing knowledge. Innovation cannot be exclusively
linked to the use of new technologies; they have the “task”
of providing companies with data.
Need for new technologies in
marketing
The use of data and technologies is closely linked to
human behavior.
Innovation implies that through technologies,
responsible entities may improve themselves, and,
as service systems are centered on people, it occurs
when people, thanks to technologies, are able to
optimize service systems operation.
Need for new technologies in
marketing
To be competitive and survive in their complex
context, companies must be able to solve
complex situations and adapt to the needs of
their customers.
It is necessary to use a service approach and
always look for interactions oriented to value co-
creation.
Data-driven decision making
Decision Support Systems (DSS) are automatic data
processing systems to process information, aimed at
improving decision-making processes.
It is necessary to understand how decision makers can
plan strategies and pursue conditions of viability,
despite the uncertainty and turbulence of the market.
Data-driven decision making
Data-driven decision making
The DSS have the purpose of supporting difficult
decisions, relating to all levels of the organization, also
favoring the integration of these levels if necessary.
They are studied in the literature because:
• their aim is to simplify and optimize the operational
performance that underlies the decisions taken
during all operations;
• their aim is to support communication between all
levels of the organization.
Data-driven decision making
Data-driven decision making (DDDM) involves the use of
metrics and data to comply with companies’ goals and
initiatives, in favor of more effective and timely
decisions.
Data-driven is a mind-set, a new culture that is wrecking
companies’ business models, according to which data
are conceived as strategic resources useful for decisions.
Data-driven decision making
However, it is necessary to create a data-driven corporate
culture, which pervades the entire organization, to
optimize the information flows and the interactions
between actors.
To allow the spread of data-driven decision-making
governance it is necessary to train specialized skills in data
analysis for an in-depth knowledge of the context and its
growth potentialities.
Data-driven decision making
A data-driven decision-making process can improve the
competitiveness of companies and enable them to
pursue their marketing aims, such as customer
relationship management, improvement of the quality
of the product or service proposed.
[E.g. Data-driven decision making can also support
healthcare actors to improve their performance. The
healthcare system is today increasingly patient-centered
and, for example, patient data, if correctly interpreted,
could allow a doctor/patient co-learning process.]
Data-driven decision making
Today, decision-making processes could be more
“informed”, thanks to a more precise exchange of
information. This would support decision makers to
define and implement a strategy in an ever timelier
and precise manner, with an improvement in the
overall performance.
Data-driven decision making can improve
organizations' performance and enable them to
pursue their survival goals.
Augmented decision making
5.2
However, new technologies could also determine
ineffective outcomes and value co-destruction.
As well, sub-optimal performances may occur, as
assessed at different levels than the one of the
Human-Machine Interactions (HMIs), also due to
emergent consequences.
HMIs and value co-creation
The risk of data-deluge
and the difficulty of having
useful information is very
high, while the possibility
of making quick, accurate,
thoughtful decisions
becomes more and more
necessary.
HMIs and value co-creation
Augmented decision-making is a
decision-making process in which
algorithmic insights used may change
the information variety of a decision-
maker.
Augmented decision making
According to the Viable Systems Approach
(vSa), complexity can be scaled through
knowledge. There is in fact a close
connection between decision and
knowledge and the knowledge, in a given
instant, of a given decision maker or viable
system, is characterized by the variety of
information.
Information Variety
The information variety of a viable system is
composed of:
• Information units;
• interpretative schemes;
• value categories.
Information Variety
The information units are the structural
composition of the knowledge of the viable
system. It is all that can be perceived.
Perception changes according to the
characteristics of the viable system (previous
information variety) and the relationship it
develops with the external context.
Information Variety
The interpretative schemes define the forms of
knowledge and have the task of transforming
non-specific data into information relating to a
specific context.
The interpretative schemes are divided into
"general" and "synthetic".
The general interpretative schemes define a
general rationalization of the information.
Synthetic interpretative schemes provide a way
to filter specific information.
Information Variety
The value categories determine the resistance
to change and represent the system of values,
strong beliefs and are closely connected with
the emotional level of the decision maker.
They represent the subjective filter with which
the decision maker uses the interpretative
schemes.
Information Variety
According to vSa, a system, in its search for viability in complex
situations, can go through an adjustment, transformation, restructuring
or reconversion phase depending on whether the specific structure, the
extended structure, the actual structure or the business idea are
modified.
These changes imply a different use of knowledge that is characterized
by the information variety of the decision-maker, made up of
information units, interpretative schemes and value categories.
The ability of the decision maker to acquire
data, also through new technologies, can
lead to a change in its variety of information
as an increase in data determines a change
in the information units held by the decision
maker.
Is it sufficient to improve his/her
ability to understand the context?
Information Variety
According to vSa, the Intelligence Augmentation, as a
perfect integration between wise people and Artificial
Intelligence systems, may amplify the decision makers’
ability to deal with a problem by changing their information
variety and enabling their decision-making process.
Intelligence Augmentation
Studies on Intelligence Augmentation are
developed to predict a possible positive impact of
the use of AI systems on humans in terms of wise
decision-making skills increased in conditions of
complexity.
The implications concern how value is co-created in
the digital age based on human-machine
interactions for the benefit of people, organizations
and the planet as a whole.
Intelligence Augmentation
Intelligence Augmentation implies a perfect
combination of artificial intelligence technologies and
people who, through technologies, can amplify their
decision-making skills thanks to a change in their
information variety, not only due to the increase in
information units but the capacity of the decision
maker to use the latter to create new interpretative
schemes.
Intelligence Augmentation
According to the vSa, Intelligence Augmentation
favors an increase in information units that allows
increased potential of interpretative schemes, as well
as the ability to promptly form new ones, with a
consequent overall optimization of the decision
maker's thinking structure.
Intelligence Augmentation
When humans interact effectively with intelligent
machines, an enhanced learning effect emerges
that strengthens the subject's interpretative and
decision-making abilities, including through a
better understanding of contextual dynamics.
This effect determines a greater ability of the
decision maker to border the problem, and
therefore, to approach the solution.
Intelligence Augmentation
The vSa proposes an interpretation of IA as the
result of the interaction between human and
machine which is not, however, to be understood
as a mere amplification of human cognitive abilities
in terms of intellectual growth but as a
collaborative integration that allows the system to
evolve from a configuration intelligent to a wise
one, in which the rational component of the
machine integrates perfectly with the emotional
component of humans.
Intelligence Augmentation
Intelligence Augmentation involves perfect humanmachine
interaction in which machines do what
they do best to help humans do what humans do
best: think.
Intelligence Augmentation is an intelligence that
derives from a collaborative and effective humanmachine
interaction and integration.
Intelligence Augmentation
Intelligence Augmentation allows systems to evolve
into configurations that are not only smarter but
wiser and also more dynamic and potentially
reactive in solving complex situations thanks to a
better ability to understand the dynamics of the
context and of its market.
Intelligence Augmentation
Antonietta Megaro
amegaro@unisa.it
THANK YOU.
Questions? Comments?