Artificial Intelligence(AI) in Marketing

MODULE - 11
DR. ZILLUR RAHMAN
PROFESSOR
DEPARTMENT OF MANAGEMENT STUDIES, IIT ROORKEE

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 Chapter 1: Introduction to AI in Marketing Management
Module 1, 2 & 3: Understanding the basics of AI in Marketing
Module 4: Introduction to AI Algorithms
Module 5: Designs of AI, Transition process and AI matrix

Chapter 2: Developing Marketing Strategies and Plans using AI

Module 6: Customer value and Role of AI in Value Delivery Process
Module 7, 8, 9, 10 & 11: Transforming Marketing Strategy using AI
Module 12: Using AI for STP
Module 13 & 14: Application of AI in Marketing Mix

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Chapter 3: AI For Marketing Research
Module 15: Marketing Information Systems and its Components
Module 16 & 17: What is Marketing Research

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 Chapter 4: Connecting with Customers
Module 18: Individual Dynamics and its influence on Consumer Behaviour
Module 19: Consumer Buying Decision Process
Module 20 & 21: Understanding Customer Journey
Module 22: Customer Experience: Meaning & Characteristics
Module 23 & 24: Personalization: Going Beyond Segmentation
Module 25: Avatar marketing

Chapter 5: Building Strong Brands Using AI

Module 26: Standardization, Personalization & Relationalization of Brands using AI
Module 27: Understanding Networks and Brand Network Effect
Module 28: Understanding the Use of AI for Addressing Competition 3

Module 29: AI and Brand Equity

Module 30 & 31: AI and New Brand Realities

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 Chapter 6: AI For Designing, Delivering and Communicating Value
Module 32: AI for Value Creation and Product Development
Module 33, 34, 35, 36 & 37: Personalization and hyper-personalization Using AI
Module 38: Implementation of AI by Product Managers
Module 39: AI in Service
Module 40, 41, 42 & 43 : Pricing Strategies Using AI
Module 44 & 45: Role of AI in Advertising
Module 46: AI in Sales promotion and Direct Marketing
Module 47 & 48: AI in PR and Publicity and Social Media Marketing
Module 49: Personal Selling using AI
Module 50: Sales management using AI
Module 51: AI and Marketing Channel Management
Module 52: Omnichannel Marketing and Retailing 4

Module 53: Changing face of Retailing in the age of AI

Module 54 & 55: AI in Logistics Management

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 Chapter 7: Ethics in AI: Ethical Concerns in using AI
Module 56, 57, 58 & 59: Navigating Ethical Challenges in AI
Module 60: AI and Sustainability

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 Chapter 2
Transforming Marketing Strategy using AI Part - V
MODULE-11

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 MODULE OVERVIEW

• To Understand How to build sustainable competitive

advantage using AI?
• Understand what is strategy for AI and strategy with AI
• To explore AI backed Key Performance Indicators (KPIs)
• Optimizing and prioritizing KPIs with the help of AI

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 MODULE OVERVIEW

• To study the Integrated Strategy Machine.

• To understand Hybrid Intelligence.
• To recognize different types of transformations needed to
achieve Hybrid Intelligence.

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Building Sustainable Competitive Advantage Using AI

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Building Sustainable Competitive Advantage using AI

• AI now has a firm footing in organizations’ strategic decision-

making processes.
• Five years ago, less than 10% of large companies had adopted
machine learning or other forms of AI, but today 80% of them
make use of the technology.
• Whether it is Amazon integrating algorithms into its recruiting
processes or Walmart using AI for decisions about product
lines.

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Building Sustainable Competitive Advantage using AI

• Such examples show that the use of AI now transcends mere

process automation, and that AI is increasingly being used to
augment decision-making processes at all levels, including top
management.
• In the boardroom, companies can use the power of AI to
analyze information, recognize complex patterns, and even
get advice on strategic issues.

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Building Sustainable Competitive Advantage using AI

• This predictive technology can help executives handle the

increasing complexity of strategic choices by offering new
perspectives and insights for consideration, which can help
organizations gain competitive advantage.

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Building Sustainable Competitive Advantage using AI

• We are now at the point where competitive advantage will

derive from the ability to capture, analyze, and utilize
personalized customer data at scale and from the use of AI to
understand, shape, customize, and optimize the customer
journey.
• The long-term sustainable competitive advantage hinges on a
firm's ability to collect first-party data and use it to ease the
purchase process by personalizing the consumer’s
experience—online and everywhere.

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Building Sustainable Competitive Advantage using AI

Strategy for and Strategy with AI

• Developing a strategy for and a strategy with a capability is
complementary.
• For example, a strategy for sustainability (such as lowering
one’s carbon footprint or reducing waste) can not be divorced
from having a sustainable overall strategy enabling the
business to operate in a sustainable manner.

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Strategy for and Strategy with AI

• Similarly, a strategy for AI shouldn’t be viewed as a substitute

for creating a strategy with AI.
• Like any corporate strategy, a strategy with AI expresses what
enterprise leaders deliberately seek to emphasize and
prioritize over a given time frame.

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Strategy for and Strategy with AI

• Strategies articulate how and why an organization expects to

succeed in its chosen market.
• Whatever the specific strategy, virtually all organizations
create corresponding measures to characterize and
communicate desirable strategic outcomes.

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Building Sustainable Competitive Advantage using AI

Key Performance Indicators

• In the age of AI, strategy is defined by the key performance
indicators (KPI), a firm chose to optimize.
• These are the measures organizations use to create value,
accountability, and competitive advantage.
• In the data-rich, digitally instrumented, and algorithmically
informed markets, AI plays a critical role in determining what
KPIs are measured, how they are measured, and how best to
optimize them.
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Key Performance Indicators

• In an always-on big data world, your system of measurement

is your strategy.
• Prioritizing KPIs, i.e., ranking them according to what matters
most and what the organization must learn— is essential to
enterprise strategy.
• Determining the optimal “metrics mix” for key enterprise
stakeholders becomes an executive imperative.

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Optimizing Key Performance Indicators (KPI)

• Further, optimization does not mean maximization or

minimization here.
• On the contrary, it means computationally learning and
understanding trade-offs among and between competing —
and complementary KPIs.
• For example, are customer-centric strategies better optimized
via customer lifetime value (CLV) or balanced blends of
earnings before interest, taxes, depreciation, and amortization
(EBITDA) and net promoter score?

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Optimizing Key Performance Indicators (KPI)

• For what customer segments should profitability be privileged

over satisfaction or loyalty?
• This optimization demands a rigorous rethinking of the
metrics chosen to define desirable (and undesirable) strategic
outcomes.
• AI transforms the strategist’s choices about which KPIs to
optimize and how to optimize them.

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Optimizing Key Performance Indicators (KPI)

• Leaders need to recognize that nowadays strategy is about

optimizing KPIs with AI/ML and AI has a strategic role in
achieving KPI outcomes (and more importantly suggesting
new KPIs).
• Optimizing known KPIs is important but not strategically
sufficient.
• When appropriately trained, machine learning models can
learn to identify and recommend novel or emergent KPIs.

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Prioritizing Key Performance Indicators (KPI)

• That is, machines can “learn to discover” enterprise KPIs on

their own, without expert guidance.
• For example, GE Healthcare marketing/data team increasingly
use machine learning to find KPIs they might never have
discovered on their own.
• In marketing, promotion, interaction, and engagement
domains, technology can go beyond “learning to optimize” to
suggest what can and should be optimized.

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Prioritizing Key Performance Indicators (KPI)

• Google’s YouTube division introduced a new internal metric in

the past two years for gauging how well videos are
performing.
• It is called “quality watch time,” a statistic with a noble goal:
to spot content that achieves something more constructive
than just keeping users glued to their phones.

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Prioritizing Key Performance Indicators (KPI)

• The changes are supposed to reward videos that are more

palatable to advertisers and the broader public.
• Creating the right metric for success could help marginalize
videos that are inappropriate or popular among small but
active communities with extreme views.
• It could also help YouTube ward off criticism that its service is
addictive and socially corrosive and help in curbing the spread
of toxic content.

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Building Sustainable Competitive Advantage using AI

The Integrated Strategy Machine

• What does it take to translate technological advances into
strategic advantage? Can Strategy be trusted with AI?
• Technology-enhanced strategy can be realized only in the
context of an integrated strategy machine: a collection of
resources—both technological and human—that act in
concert to develop and execute business strategy.

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The Integrated Strategy Machine

• It comprises a range of conceptual and analytical operations—

including problem definition, signal processing, pattern
recognition, abstraction and conceptualization, analysis, and
prediction—that connect into a seamless whole.
• This alignment of individual operations toward the overall aim
makes the strategy machine integrated.

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The Integrated Strategy Machine

• Effective business strategy development, with or without

technology, must accommodate reframing, the process of
redefinition and reanalysis of the problem considered to be at
the heart of effective business thinking.
• To enable reframing, the strategy machine must span the end-
to-end process of strategy development and implementation.
Rather than formulating strategy in a vacuum, the strategy
machine must continuously update and improve strategy by
analyzing feedback and execution data.

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The Integrated Strategy Machine

• There needs to be a constant interplay between upstream and

downstream elements of the strategy machine.
• Although machines and algorithms can play increasingly large
and important roles in strategy making and execution, the
integrated strategy machine must, at least for now, be
designed by human beings: people must assemble the
machine and direct it toward a strategic aim.

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The Integrated Strategy Machine

• It is important to understand why. Human beings are still

unique in their capacity to “go meta”—that is, to think outside
the immediate scope of a task or problem.
• Machines can’t yet do that well; they are good at executing a
well-defined task or solving a well-defined problem, but they
can’t pose new questions or connect a problem to a different
one they previously faced.

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The Integrated Strategy Machine

• In other words, artificial intelligence is still far from being

general. Of course, this is not to say that machines are
incapable of learning these higher-order skills. Technology will
play a larger and larger role within the strategy machine.
• Amazon provides an excellent example of an integrated
strategy machine.

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The Integrated Strategy Machine

• The company has at least 21 data science systems, including

several for supply chain optimization, an inventory forecasting
system, a sales forecasting system, a profit optimization
system, a recommendation engine, and many others.
• These systems are intertwined with one another and with
human strategists to create an integrated, well-oiled machine.

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The Integrated Strategy Machine

• For example, if the sales forecasting system detects that the

popularity of an item is increasing, it triggers a cascade of
changes: the inventory forecast is updated, causing the supply
chain system to optimize inventory across warehouses; the
recommendation engine pushes the item more, and the profit
optimization system adjusts pricing; these changes in turn
update the sales forecast.
• These are only some of the first-order effects, and further
interactions occur downstream.

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The Integrated Strategy Machine

The Requirements and the Pitfalls

• How can businesses create an effective integrated strategy
machine? Boston Consulting Group (BCG) has highlighted six
requirements for the Integrated strategy machine-
1. A Relevant, Specific Strategic Aim
• The integrated strategy machine must be directed at a
relevant aim, a desired outcome. Humans must provide the
initial question or insight into where the opportunity lies.

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The Integrated Strategy Machine

• Whereas a group of people may be able to accommodate

ambiguity and find the right aim through self-organization,
the strategy machine needs an explicit aim. The opposite of
having a relevant, specific aim is asking the wrong question.
• Powerful technology can cause us to do that by preoccupying
us with what it can do rather than what it should do. The
machine’s capabilities, rather than human needs, then dictate
the problems that we solve. In other words, if all you have is a
hammer, everything looks like a nail.

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The Integrated Strategy Machine

2. A Design Appropriate to the Aim

• No strategy machine can be effective in all situations. Just as
different environments call for different strategies, different
strategies call for different designs of the integrated strategy
machine.
• For example, strategies in stable classical environments
require a process of “analyze, plan, execute,” and adaptive
strategies in less predictable conditions require a process that
can be characterized as “vary, select, scale.”

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The Integrated Strategy Machine

• The degree of variability and malleability that a strategy must

accommodate suggests the optimal approach to developing
and implementing it.
• Form must follow function. Much as the fox cannot eat out of
a narrow-necked vessel and the stork cannot drink out of a
bowl, a strategy machine without the right design cannot
achieve its aim.

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The Integrated Strategy Machine

• For example, a strategy machine designed to be most

effective in a classical environment would fail in a more
malleable environment, where companies must engage in
such activities as collaborating with a diverse set of
stakeholders and building an ecosystem.

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The Integrated Strategy Machine

3. An Integrated Approach
• The components of the machine, both human and
technological, must communicate with one another to create
an integrated whole. This integration is critical, because
aggregating local optima rarely leads to a global optimum. For
example, a strategist who wants to evaluate a new business
opportunity may need to consider competitive threats and
strategic fit.

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The Integrated Strategy Machine

• Different components of the strategy machine could analyze

those issues separately, but the strategist will be no closer to
an answer unless there is a mechanism to integrate the
analyses and resolve trade-offs by generating new insights.
• It’s easy to ensure that the components of the strategy
machine share insights and coordinate their actions when
only a few components are involved. However, as we ask
increasingly complex questions, we risk losing coherence in
our search for a solution.

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The Integrated Strategy Machine

• Therefore, human beings, with their unique ability to

understand broad contexts and connect insights from
disparate spheres, must design and optimize the flow of
information and insights in the strategy machine.
• Their role is to make sure that the components of the
machine—both people and technology—optimize for the
global aim rather than for individual operations.

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The Integrated Strategy Machine

4. The Right Human-Machine Division of Labor

• In an effective strategy machine, human beings and machines
must each do what they are good at. Machines can usually
perform tasks with a specific, well-defined context more
accurately and more quickly than people can, and they can
process more data while doing so.
• Human beings are better at thinking beyond the specified
context and dealing with ambiguity by, for example, reframing
a problem, asking new questions, or applying common sense.

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The Integrated Strategy Machine

• When people and machines are not engaged in activities

suited to their respective strengths, thinking often stagnates.
• For example, machines can facilitate abstraction or the
formation of new concepts by detecting signals and patterns.
Nevertheless, they are still inferior to human beings in
abstracting and conceptualizing, much less adding rigor to
their thinking through iterative reframing.

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The Integrated Strategy Machine

5. A Well-Designed Human-Machine Interface

• The strategy machine can benefit from an appropriate division
of labor only if there is the right human-machine interface.
• Machines must be able to communicate their observations to
people; conversely, people need to be able to understand,
examine, and validate those observations and provide
feedback to machines.

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The Integrated Strategy Machine

• An ineffective human-machine interface turns the strategy

machine into a black box that creates outputs that are
“untraceable”: people cannot interpret them and therefore
cannot build deeper and richer insights through successive
reframing. To avoid this pitfall, architects of the strategy
machine must avoid the temptation to turn machine outputs
into reductive visualizations or simplified patterns. People
need to be able to probe the messy data from diverse
perspectives in order to gain rich insights.
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The Integrated Strategy Machine

6. Unique Tools, Data, People, or Process

• The strategy machine has not fulfilled its ultimate purpose if it
does not create advantage. Therefore, it must do something
better than competitors can.
• Some aspect of the machine must be advantaged, whether
it’s the tools, the data, the people, or the design.

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The Integrated Strategy Machine

• The risk of relying on off-the-shelf solutions, readily available

data, or old designs is that the strategy machine becomes
commoditized, a mere cost of doing business.
• Its outputs may be “good enough,” but they are not a source
of differentiation. The integrated strategy machine must itself
be capable of evolving.

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The Integrated Strategy Machine
People and
technology must
each play their
roles, and
humans must
constantly evolve
the design of the
machine.

Fig.- Integrated
Strategy Machine
(Source: BCG)

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The Integrated Strategy Machine

The Implications
• What strategic aims do I want to achieve with an integrated
strategy machine?
• The initial aim must come from human beings. As Bruce
Henderson of BCG stated, “The first definition of a problem is
inescapably intuitive. It must be in order to be recognized as a
problem at all.” The initial problems that the strategy machine
intends to solve, in other words, must be defined outside the
machine itself.

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The Integrated Strategy Machine - Implications

What technology, people, and design do I need in order to achieve

those aims?
• Different aims require different capabilities, which are often costly
and difficult to procure.
• The technology giants that have developed effective strategy
machines—such as Amazon and Google—have done so by
continually investing in the integration of technology into strategy
and paying a premium to attract the best talent. Companies
without such advantages must remain realistic about what it takes
to build an advantaged strategy machine.

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The Integrated Strategy Machine - Implications

How can people and machines complement each other?

• The goal of the integrated strategy machine is to enhance,
rather than inhibit, human thought. To do so, technology
needs to stimulate people’s ability to create new concepts,
challenge their own thinking, and reframe their
understanding.
• Conversely, human beings must interpret the outputs and
actions of machines in their broader context and guide them
to perform increasingly relevant analyses.

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The Integrated Strategy Machine - Implications

How can the strategy machine evolve?

• A successful strategy machine must be able to improve itself
over time. It needs a mechanism to learn from its experience
and continue to answer the right questions and provide novel
insights.
• People who manage the machine must have the courage and
discipline to periodically reevaluate and challenge its design.

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The Integrated Strategy Machine - Implications

How can the broader organization embrace the strategy

machine?
• A strategy machine is valuable only to the extent that the
organization embraces and uses it. Business leaders must pay
attention to organizational realities and design the strategy
machine accordingly.
• If the organization is not ready to rely on it, the machine may
become irrelevant and ineffectual in driving actual change.

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Building Sustainable Competitive Advantage using AI

Hybrid Intelligence
• Traditionally, strategic decisions making has been a human
task, and business leaders often rely on their intuition for
decision-making.
• The rise in AI has exposed the flaws in traditional decision-
making and provides an opportunity to make more informed
and fool-proof strategies.

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Hybrid Intelligence

• However, artificial and human intelligence thrive at very

different tasks. While AI is superior at data-intensive
prediction problems, humans are uniquely suited to the
creative thought experiments that underpin the best
decisions.
• A key to effective collaboration is to recognize which parts of a
problem to hand off to the AI and which the managerial mind
will be better at solving and how to collaborate between the
two forms of intelligences (artificial and human).

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Hybrid Intelligence

• The majority of work in the digital age will be performed by

Hybrid Intelligence, which combines human and artificial
intelligence (AI), using complementary qualities that, when
joined, boost each other.
• Companies not only need to enhance their levels of AI while
continuing to develop their HI.
• Rather, they additionally need a higher-order intelligence for
transforming the two types of intelligence in line with
corporate strategy and business strategies.

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Hybrid Intelligence

• This dynamic transformation over time may not be achieved

by only relying on human and artificial intelligence.
• Rather than independent management of multiple types of
intelligence, hybrid intelligence can generate completely new
solutions and business models.
• Specifically, this hybrid intelligence involves the renewal and
recombination of the different types of intelligence.

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Hybrid Intelligence

• Depending on the extent of renewal and recombination, there

are four transformations (Figure 4), which are consistent with
extant insights into the transformation of innovation
processes and product features.

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Hybrid Intelligence
High

Modular Radical
Transformation Transformation

Renewal of
Intelligence

Incremental Architectural
Transformation Transformation

Low

Low Recombination High

of Intelligence

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Incremental Transformation

• Incremental transformation involves only a limited renewal

and recombination of AI and HI.
• Examples are updates of AI technology to enable new
functionalities or the selective implementation of a new
creativity technique to better leverage human idea generation
and intelligence.

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Incremental Transformation

• Both of these approaches could recently be observed in many

large companies as diverse as Facebook and Harley-Davidson.

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Modular Transformation

• Modular transformation includes a significant renewal of

intelligence, whereas the level of recombination is relatively
limited.
• The intelligence architecture is largely untouched, but the
individual intelligence types are substantially reconfigured.

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Modular Transformation

• With regard to HI, many companies, such as Apple and the

German software company SAP, have strongly transformed
their human-based innovation processes to focus on design-
thinking logic instead of traditional innovation processes.
• This has often occurred quite independently from AI activities,
and the overall intelligence architecture has only been
affected to a limited degree.

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Modular Transformation

• With regard to AI, this modular transformation also involves

the substitution of one type of intelligence by the other.
• This transformation has received most public attention in
recent years because many firms’ strategic initiatives have
focused on replacing HI by means of AI, which would deliver
nearly identical work results.

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Architectural Transformation

• Architectural transformation focuses on the recombination of

the two types of intelligence, whereas the level of renewal is
limited.
• This transformation has often been overlooked so far because
most firms do not yet take an integrative perspective on their
different types of intelligence.

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Architectural Transformation

• They rather have started somewhat isolated AI initiatives, and

they continue to nurture the HI of their experts.
• An example is an update of a firm’s investment guidelines
which makes the consultation of existing AI a requirement
before taking decisions.

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Architectural Transformation

• This is what the investment management firms WorldQuant

and Aspect Capital have done.
• The focus of architectural transformation is not on
implementing completely new technology.
• Rather, the focus is on revising the interdependencies of AI
and HI, thus changing the connections of technology that was
already used at least in some parts of the company.

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Radical Transformation

• Radical transformation involves high levels of both renewal

and recombination.
• Consequently, a firm’s intelligence is updated and enhanced,
while simultaneously changing the intelligence architecture.

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Radical Transformation

• An example here is the implementation of new and advanced

data mining algorithms in a company’s strategic planning
processes, which would involve a close interaction with the HI
of the strategy department.
• There are many new interdependencies of HI with new AI
technology, which requires a substantial adaptation of a firm’s
intelligence architecture.

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Radical Transformation

• Start-up companies such as Talenya use advanced data

analytics to fill roles and preselect candidates for open
positions at their customers.
• The new technology determines whether candidates are
invited to a personal interview, and the final decision is based
on the scoring of AI and experts from the human resources
department.

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CONCLUSION

• AI not only helps in attaining competitive advantage, but

strategically using AI can help build long term and sustainable
competitive advantage.
• ML models can learn to identify and recommend novel or
emergent KPIs.
• AI backed Optimization and Prioritization of KPIs help achieve
sustainable competitive advantage.

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CONCLUSION

• We studied The Integrated Strategy Machine which uses AI to

create advantage.
• Artificial and human intelligence thrive at very different tasks.
The majority of work in the digital age will be performed by
Hybrid Intelligence, which combines human and artificial
intelligence (AI).

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 Reference
1. Sterne J., “Artificial intelligence for marketing: practical applications”, John Wiley
& Sons.
2. Gentsch, Peter., “AI in marketing, sales and service: How marketers without a data
science degree can use AI, big data and bots”, (eBook) Springer.
3. King K., “Using Artificial Intelligence in Marketing: How to harness AI and maintain
the competitive edge”, Kogan Page Publishers.
4. Hosnagar, K, “A human’s guide to machine intelligence”, New York: Viking.
5. Venkatesan, R., and Lecinski J, “The AI Marketing Canvas: A Five-stage Road Map
to Implementing Artificial Intelligence in Marketing”, Stanford University Press.
6. https://www.bcg.com/publications/2016/strategy-technology-digital-integrated-
strategy-machine-using-ai-create-advantage.
7. https://hbr.org/2016/04/welcoming-the-chief-strategy-robot.

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Thank You

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