Download Basic Marketing, 16e

Chapter 4
Analytics, Decision
Support, and
Artificial Intelligence
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STUDENT LEARNING
OUTCOMES
1.
2.
3.
Compare and contrast decision
support systems and geographic
information systems.
Describe the decision support role of
specialized analytics (predictive and
text analytics).
Describe the role and function of an
expert system in analytics.
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STUDENT LEARNING
OUTCOMES
4.
5.
6.
Explain why neural networks are
effective decision support tools.
Define genetic algorithms and the
types of problems they help solve.
Describe data-mining agents and
multi-agent systems.
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ONLINE LEARNING
Notice the increase in online learning and the
decrease in traditional enrollments.
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Questions
1.
2.
3.
Have you taken or are taking an
online course? Fully online or
hybrid?
Why do students opt to take online
courses over traditional classroom
courses?
Is this transformation occurring at
the K-12 level?
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INTRODUCTION
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Businesses make decisions everyday
Some big and some small
Many IT tools can aid in the
decision-making process
Analytics is now key to the success
of any business
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CHAPTER ORGANIZATION
1.
Decisions and Decision Support

2.
Geographic Information Systems

3.
Learning Outcome #2
Artificial Intelligence
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5.
Learning outcome #1
Data-Mining Tools and Models

4.
Learning outcome #1
Learning outcomes #3, 4, and 5
Agent-Based Technologies

Learning outcome #6
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DECISIONS AND DECISION
SUPPORT
Find or recognize the problem, need,
or opportunity
Consider ways of solving the
problem
Examine the merits of each solution
and choose the best one
Carry out the chosen solution and
monitor the results
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Types of Decisions You Face
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Structured decision – processing a
certain information in a specified way so
you always get the right answer
Nonstructured decision – may be several
“right” answers, without a sure way to get
the right answer
Recurring decision – happens repeatedly
Nonrecurring (ad hoc) decision – one
you make infrequently
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Types of Decisions You Face
EASIEST
MOST
DIFFICULT
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Decision Support Systems

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Decision support system (DSS) –
a highly flexible and interactive
system that is designed to support
decision making when the problem is
not structured
Decision support systems help you
analyze, but you must know how to
solve the problem, and how to use
the results of the analysis
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Components of a DSS
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Model management component
– consists of both the DSS models
and the model management system
Data management component –
stores and maintains the information
that you want your DSS to use
User interface management
component – allows you to
communicate with the DSS
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Components of a DSS
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GEOGRAPHIC INFORMATION
SYSTEMS

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Geographic information system
(GIS) – DSS designed specifically to
analyze spatial information
Spatial information is any
information in map form
Businesses use GIS software to
analyze information, generate
business intelligence, and make
decisions
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Google Earth as a GIS
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DATA-MINING TOOLS AND
MODELS

Business need IT-based analytics
tools
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Databases and DBMSs
Query-and-reporting tools
Multidimensional analysis tools
Digital dashboards
Statistical tools
GISs
Our remaining
Specialized analytics
focus
Artificial intelligence
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Data-Mining Tools and Models
Support

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Association/dependency modeling –
cross-selling opportunities,
recommendation engine
effectiveness
Clustering – groups of entities that
are similar (without using known
structures)
Classification – use historical data to
derive future inferences
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Data-Mining Tools and Models
Support
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Regression – find corollary and often
causal relationships between data
sets
Summarization – basic, but powerful
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Sums, averages
Standard deviations
Histograms, frequency distributions
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Predictive Analytics

Predictive analytics – highly
computational data-mining
technology that uses information and
business intelligence to build a
predictive model for a given business
application

Insurance, retail, healthcare, travel,
financial services, CRM, SCM, credit
scoring, etc
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Predictive Analytics


Prediction goal – the question you
want addressed by the predictive
analytics model
Prediction indicator – specific
measurable value based on an
attribute of the entity under
consideration
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Predictive Analytics
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Predictive Analytics Example


Prediction goal – What customers are
most likely to respond to a social
media campaign within 30 days by
purchasing at least 2 products in the
advertised product line?
Prediction indicators
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Frequency of purchases
Proximity of date of last purchase
Presence on Facebook and Twitter
Number of multiple-product purchases
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Text Analytics
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Text analytics – uses statistical, AI,
and linguistic technologies to convert
textual information into structured
information
Gaylord Hotels uses text analytics to
make sense of customer satisfaction
surveys
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Text Analytics Support
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Lexical analysis – word frequency
distributions
Named entity recognition –
identifying peoples, places, and
things
Disambiguation – meaning of a
named entity recognition

“Ford” can refer to how many different
things?
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Text Analytics Support
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Coreference – handling of differing
noun phrases that refer to the same
object
Sentiment analysis – discerning
subjective business intelligence such
as mood, opinion, and emotion
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Endless Analytics

Web analytics – understanding and
optimizing Web page usage

Search engine optimization (SEO) –
improving the visibility of Web site using
tags and key terms

HR analytics – analysis of human
resource and talent management
data
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Endless Analytics
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Marketing analytics – analysis of
marketing-related data to improve
product placement, marketing mix,
etc
CRM analytics – analysis of CRM
data to improve sales force
automation, customer service, and
support
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Endless Analytics
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Social media analytics – analysis
of social media data to better
understand customer/organization
interaction dynamics
Mobile analytics – analysis of data
related to the use of mobile devices
to support mobile computing and
mobile e-commerce (m-commerce)
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ARTIFICIAL INTELLIGENCE

Artificial intelligence, the science
of making machines imitate human
thinking and behavior, can replace
human decision making in some
instances
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Expert systems
Neural networks (and fuzzy logic)
Genetic algorithms
Agent-based technologies
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Expert Systems


Expert (knowledge-based)
system – an artificial intelligence
system that applies reasoning
capabilities to reach a conclusion
Used for


Diagnostic problems (what’s wrong?)
Prescriptive problems (what to do?)
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Traffic Light Expert System
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What Expert Systems Can
and Can’t Do
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An expert system can
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Reduce errors
Improve customer service
Reduce cost
An expert system can’t

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Use common sense
Automate all processes
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Neural Networks and Fuzzy
Logic

Neural network (artificial neural
network or ANN) – an artificial
intelligence system that is capable of
finding and differentiating patterns
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Neural Networks Can…
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Learn and adjust to new
circumstances on their own
Take part in massive parallel
processing
Function without complete
information
Cope with huge volumes of
information
Analyze nonlinear relationships
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Fuzzy Logic



Fuzzy logic – a mathematical
method of handling imprecise or
subjective information
Used to make ambiguous information
such as “short” usable in computer
systems
Applications

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Google’s search engine
Washing machines
Antilock breaks
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Genetic Algorithms

Genetic algorithm – an artificial
intelligence system that mimics the
evolutionary, survival-of-the-fittest
process to generate increasingly
better solutions to a problem
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Genetic Algorithm Examples
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Staples – determine optimal package
design characteristics
Boeing – design aircraft parts such as
fan blades
Many retailers – better manage
inventory and optimize display areas
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Genetic Algorithms Can…
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Take thousands or even millions of
possible solutions and combine and
recombine them until it finds the
optimal solution
Work in environments where no
model of how to find the right
solution exists
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AGENT-BASED TECHNOLOGIES

Agent-based technology
(software agent) – piece of
software that acts on your behalf (or
on behalf of another piece of
software) performing tasks assigned
to it
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AGENT-BASED TECHNOLOGIES
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Types of Agent-Based
Technologies
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Autonomous agent – can adapt
and alter the manner in which it
works
Distributed agent – works on
multiple distinct computer systems
Mobile agent – can relocate itself
onto different computer systems
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Types of Agent-Based
Technologies
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Intelligent agent – incorporates
artificial intelligence capabilities such
as reasoning and learning
Multi-agent system – group of
intelligent agents that can work
independently and also together to
perform a task
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Types of Intelligent Agents

Information agents (buyer
agents) – search for information
and bring it back

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Monitoring-and-surveillance
agents – constantly observe and
report on some entity of interest, a
network, or manufacturing
equipment
User agents – take action on your
behalf (e.g., sorting your email)
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Types of Intelligent Agents

Data-mining agents – operate in a
data warehouse discovering
information
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Important analytics tool for data
warehouse data
Can find hidden patterns in the data
Can also classify and categorize
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Multi-Agent Systems &
Biomimicry

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Biomimicry – learning from
ecosystems and adapting their
characteristics to human and
organizational situations
Used to
1.
2.
3.
Learn how people-based systems behave
Predict how they will behave under
certain circumstances
Improve human systems to make them
more efficient and effective
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Swarm Intelligence

Swarm (collective) intelligence –
the collective behavior of groups of
simple agents that are capable of
devising solutions to problems as
they arise, eventually learning to
coherent global patterns

A subfield of biomimicry
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Characteristics of Swarm
Intelligence
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Flexibility – adaptable to change
Robustness – tasks are completed
even if some individuals are removed
Decentralization – each individual has
a simple job to do
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