Cluster analysis is a technique used to segment markets by grouping consumers into clusters based on their characteristics. It aims to maximize similarity within clusters and dissimilarity between clusters. Marketers can use cluster analysis to discover distinct groups of customers and develop targeted marketing programs for each group. Common variables used to segment markets include demographics, psychographics, geographics, product benefits, and behavior.
It is on Conjoint Analysis presented by Radhika Gupta, Shivi Agarwal, Neha Arya, Neha Kasturia, Mudita Maheshwari, Dhruval Dholakia, Chinmay Jaggan Anmol Sahani and Madhusudan Partani of FMG-18A, FORE School of Management
At this webinar, Stephan Sorger, Vice-President of On Demand Advisors and Author of the book, "Marketing Analytics: Strategic Models and Metrics" discussed:
• Trends driving Marketing Analytics adoption
• Important advantages and facets of Marketing Analytics
• Marketing Analytics models vs metrics
• Essential tips on how best to allocate your marketing budget and provide a high ROI
• Promotional metrics for traditional and Social Media
steps included in the analytics process
why marketing analysis.
advantages of marketing analytics
the framework of marketing analytics
future of marketing analytics,
how analytics helped amazon small case study.
Highlights of the Business Analytics seminar by Gary Cokins from October 21, 2014 presentation with Illinois CPA Society.
Gary Cokins is an internationally recognized expert, speaker, and author in performance improvement systems and cost management.
http://www.GaryCokins.com
Operations Research: Significance and limitations Sanjeet Yadav
Operational Research supposed to be a vital aspect to determine the viability of any business concern. However, this field of study does have some inherent strengths and weaknesses. These two aspects have been discussed in the presentation in the form of significance and limitations.
Group work in International Trade (March 2015):
PESTLE model should help us to evaluate the country Brazil in terms of its attractiveness as a trade partner country. My part: "Future Development" of Brazil (Slide p. 35)
In course of the presentation, you get familiar with Brazil's politics, its economical situation, social environment, legal issues and shortly with its external environment. The research was conducted in March 2015.
It is on Conjoint Analysis presented by Radhika Gupta, Shivi Agarwal, Neha Arya, Neha Kasturia, Mudita Maheshwari, Dhruval Dholakia, Chinmay Jaggan Anmol Sahani and Madhusudan Partani of FMG-18A, FORE School of Management
At this webinar, Stephan Sorger, Vice-President of On Demand Advisors and Author of the book, "Marketing Analytics: Strategic Models and Metrics" discussed:
• Trends driving Marketing Analytics adoption
• Important advantages and facets of Marketing Analytics
• Marketing Analytics models vs metrics
• Essential tips on how best to allocate your marketing budget and provide a high ROI
• Promotional metrics for traditional and Social Media
steps included in the analytics process
why marketing analysis.
advantages of marketing analytics
the framework of marketing analytics
future of marketing analytics,
how analytics helped amazon small case study.
Highlights of the Business Analytics seminar by Gary Cokins from October 21, 2014 presentation with Illinois CPA Society.
Gary Cokins is an internationally recognized expert, speaker, and author in performance improvement systems and cost management.
http://www.GaryCokins.com
Operations Research: Significance and limitations Sanjeet Yadav
Operational Research supposed to be a vital aspect to determine the viability of any business concern. However, this field of study does have some inherent strengths and weaknesses. These two aspects have been discussed in the presentation in the form of significance and limitations.
Group work in International Trade (March 2015):
PESTLE model should help us to evaluate the country Brazil in terms of its attractiveness as a trade partner country. My part: "Future Development" of Brazil (Slide p. 35)
In course of the presentation, you get familiar with Brazil's politics, its economical situation, social environment, legal issues and shortly with its external environment. The research was conducted in March 2015.
“How to build and market a new product category” by Niklas Jansen, co-founder...TheFamily
Blinkist distills the key insights of 2 000+ bestselling nonfiction books into powerful 15-minute reads or listens for your mobile device.
One of the most exciting things you can do as an entrepreneur is create an entirely new product category. Some of the world’s most successful companies owe their success to the fact that they did not focus on incremental innovation but succeeded in creating, building, and converting people to a brand new market space ✈️
Creating a new category is incredibly hard. So the question is: how can you do it and make it stick? ✅
In this talk, Niklas provides actionable tips to help you:
- Build a team culture that focuses on learning
- Spot market opportunities and anticipate market changes
- Validate your ideas
- Convince users to adapt to this category and be catalysts for change
In order to craft your value proposition, you need to make certain assumptions about your target market. It is important to validate these assumptions through market research techniques and analysis.
In this lecture, market research experts introduce the principles of market research and analysis, including analytical frameworks such as the PEST analysis and Porter’s Five Forces.
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters).
Cluster analysis is a technique to group similar observations into a number of clusters based on the observed values of several variables for each individual. Cluster analysis is similar in concept to discriminant analysis. The group membership of a sample of observations is known upfront in the latter while it is not known for any observation in the former. As an application of cluster analysis to education, Everitt (1990) describes a data set that has achievement test scores on reading and arithmetic for children in the fourth and sixth grades of 25 schools and the interest is in identifying different levels of performance and assessing similarities and differences in the patterns of change from fourth to sixth grade
SPSS Step-by-Step Tutorial and Statistical Guides by StatsworkStats Statswork
Statswork help to analyse your data, before our step-by-step SPSS Statistics guides show you how to carry out these statistical tests using SPSS Statistics, as well as interpret and analysis the document.
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Different Algorithms used in classification [Auto-saved].pptxAzad988896
In this article, we will discuss top 6 machine learning algorithms for classification problems, including: logistic regression, decision tree, random forest, support vector machine, k nearest neighbour and naive bayes. The best example of an ML classification algorithm is Email Spam Detector. The main goal of the Classification algorithm is to identify the category of a given dataset, and these algorithms are mainly used to predict the output for the categorical data.
Get involved with the steps of Kmeans and Hierarchical clustering and also understand how scaling affects the clustering with Agglomerative and Divise modes.
Do let me know if anything is required. Ping me at google #bobrupakroy
UNIT - 4: Data Warehousing and Data MiningNandakumar P
UNIT-IV
Cluster Analysis: Types of Data in Cluster Analysis – A Categorization of Major Clustering Methods – Partitioning Methods – Hierarchical methods – Density, Based Methods – Grid, Based Methods – Model, Based Clustering Methods – Clustering High, Dimensional Data – Constraint, Based Cluster Analysis – Outlier Analysis.
Safalta Digital marketing institute in Noida, provide complete applications that encompass a huge range of virtual advertising and marketing additives, which includes search engine optimization, virtual communication advertising, pay-per-click on marketing, content material advertising, internet analytics, and greater. These university courses are designed for students who possess a comprehensive understanding of virtual marketing strategies and attributes.Safalta Digital Marketing Institute in Noida is a first choice for young individuals or students who are looking to start their careers in the field of digital advertising. The institute gives specialized courses designed and certification.
for beginners, providing thorough training in areas such as SEO, digital communication marketing, and PPC training in Noida. After finishing the program, students receive the certifications recognised by top different universitie, setting a strong foundation for a successful career in digital marketing.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Normal Labour/ Stages of Labour/ Mechanism of LabourWasim Ak
Normal labor is also termed spontaneous labor, defined as the natural physiological process through which the fetus, placenta, and membranes are expelled from the uterus through the birth canal at term (37 to 42 weeks
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
2. • It is a class of techniques used to classify cases into groups that are -
• relatively homogeneous within themselves and
• heterogeneous between each other
• Homogeneity (similarity) and heterogeneity (dissimilarity) are measured on
the basis of a defined set of variables
• These groups are called clusters.
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3. • The nature of Cluster Analysis is data exploration that conducted in
repetitive fashion. Clusterization is not a single grouping, but the
process of getting well interpretable groups of objects under
consideration.
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4. –Market segmentation is one of the most fundamental strategic marketing
concepts:
•grouping people (with the willingness, purchasing power, and the authority
to buy) according to their similarity in several dimensions related to a
product under consideration.
–Markets can be segmented based on:
•Demographics
•Psychographics
•Geographics
•Product Benefits
•Behavioral Segmentation
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5. •Cluster analysis is especially useful for market segmentation.
•Segmenting a market means dividing its potential consumers into
separate sub-sets where
•Consumers in the same group are similar with respect to a given set of
characteristics
•Consumers belonging to different groups are dissimilar with respect to the
same set of characteristics
•This allows one to calibrate the marketing mix differently according to
the target consumer group.
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6. • Help marketers discover distinct groups in their customer bases, and then use this
knowledge to develop targeted marketing programs
• The underlying definition of cluster analysis procedures mimic the goals of market
segmentation:
- to identify groups of respondents that minimizes differences among members of
the same group
• highly internally homogeneous groups
- while maximizing differences between different groups
• highly externally heterogeneous groups
• Market Segmentation solution depends on
variables used to segment the market
method used to arrive at a certain segmentation
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7. • Product characteristics and the identification of new product opportunities.
• Clustering of similar brands or products according to their characteristics allow
one to identify competitors, potential market opportunities and available niches
• Data reduction
• Factor analysis and principal component analysis allow to reduce the number of variables.
• Cluster analysis allows to reduce the number of observations, by grouping them into
homogeneous clusters.
• Maps profiling simultaneously consumers and products, market opportunities and
preferences as in preference or perceptual mappings.
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8. • Select a distance measure
• Select a clustering algorithm
• Define the distance between two clusters
• Determine the number of clusters
• Validate the analysis
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9. • To measure similarity between two observations a distance measure is needed
• With a single variable, similarity is straightforward
• Example: income – two individuals are similar if their income level is similar
and the level of dissimilarity increases as the income gap increases
• Multiple variables require an aggregate distance measure
• Many characteristics (e.g. income, age, consumption habits, family
composition, owning a car, education level, job…), it becomes more difficult
to define similarity with a single value
• The most known measure of distance is the Euclidean distance, which is the
concept we use in everyday life for spatial coordinates.
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10. • Other distance measures: Chebychev, Minkowski, Mahalanobis
• An alternative approach: use correlation measures, where
correlations are not between variables, but between observations.
• Each observation is characterized by a set of measurements (one
for each variable) and bi-variate correlations can be computed
between two observations.
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11. • Hierarchical procedures
• Agglomerative (start from n clusters to get to 1 cluster)
• Divisive (start from 1 cluster to get to n clusters)
• Non hierarchical procedures
• K-means clustering
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12. • Agglomerative:
• Each of the n observations constitutes a separate cluster
• The two clusters that are more similar according to same distance rule are
aggregated, so that in step 1 there are n-1 clusters
• In the second step another cluster is formed (n-2 clusters), by nesting the two clusters
that are more similar, and so on
• There is a merging in each step until all observations end up in a single cluster in the
final step.
• Divisive
• All observations are initially assumed to belong to a single cluster
• The most dissimilar observation is extracted to form a separate cluster
• In step 1 there will be 2 clusters, in the second step three clusters and so on, until the
final step will produce as many clusters as the number of observations.
• The number of clusters determines the stopping rule for the
algorithms
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13. • These algorithms do not follow a hierarchy and produce a single partition
• Knowledge of the number of clusters (c) is required
• In the first step, initial cluster centres (the seeds) are determined for each of
the c clusters, either by the researcher or by the software (usually the first c
observation or observations are chosen randomly)
• Each iteration allocates observations to each of the c clusters, based on their
distance from the cluster centres
• Cluster centres are computed again and observations may be reallocated to
the nearest cluster in the next iteration
• When no observations can be reallocated or a stopping rule is met, the
process stops
03/02/15 Cluster analysis for market segmentation
14. • Given k, the k-means algorithm is implemented in four
steps:
1. Partition objects into k nonempty subsets
2. Compute seed points as the centroids of the clusters of the
current partition (the centroid is the center, i.e., mean point,
of the cluster)
3. Assign each object to the cluster with the nearest seed point
4. Go back to Step 2, stop when no more new assignment.
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16. Hierarchical Methods Non-hierarchical methods
• No decision about the number of clusters
• Problems when data contain a high level of
error
• Can be very slow, preferable with small data-
sets
• Initial decisions are more influential (one-
step only)
• At each step they require computation of the
full proximity matrix
• Faster, more reliable, works with
large data sets
• Need to specify the number of
clusters
• Need to set the initial seeds
• Only cluster distances to seeds need
to be computed in each iteration
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17. • Algorithms vary according to the way the distance
between two clusters is defined.
• The most common algorithm for hierarchical methods
include
• single linkage method
• complete linkage method
• average linkage method
• Ward algorithm
• centroid method
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18. • Single linkage method (nearest neighbour): distance between two
clusters is the minimum distance among all possible distances
between observations belonging to the two clusters.
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19. • Complete linkage method (furthest
neighbour): nests two cluster using
as a basis the maximum distance
between observations belonging to
separate clusters.
• Average linkage method: the distance
between two clusters is the average
of all distances between observations
in the two clusters
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20. • The distance between two clusters is the distance between the two
centroids,
• Centroids are the cluster averages for each of the variables
• each cluster is defined by a single set of coordinates, the averages of the
coordinates of all individual observations belonging to that cluster
• Difference between the centroid and the average linkage method
• Centroid: computes the average of the co-ordinates of the observations
belonging to an individual cluster
• Average linkage: computes the average of the distances between two
separate clusters.
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22. 1. First perform a hierarchical method to define the number of clusters
2. Then use the k-means procedure to actually form the clusters
The reallocation problem
• Rigidity of hierarchical methods: once a unit is classified into a cluster, it cannot be moved to
other clusters in subsequent steps
• The k-means method allows a reclassification of all units in each iteration.
• If some uncertainty about the number of clusters remains after running the hierarchical
method, one may also run several k-means clustering procedures and apply the previously
discussed statistical tests to choose the best partition.
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23. • The observations are preliminarily aggregated into clusters using an hybrid
hierarchical procedure named cluster feature tree.
• This first step produces a number of pre-clusters, which is higher than the
final number of clusters, but much smaller than the number of observations.
• In the second step, a hierarchical method is used to classify the pre-
clusters, obtaining the final classification.
• During this second clustering step, it is possible to determine the number of
clusters.
The user can either fix the number of clusters or let the algorithm search for
the best one according to information criteria which are also based on
goodness-of-fit measures
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29. • It might seem that cluster analysis is too sensitive to the researcher’s choice.
• This is partly due to the relatively small data-set and possibly to correlation
between variables
• However, all outputs point out to a segment with older and poorer household
and another with younger and larger households, with high expenditures.
• By intensifying the search and adjusting some of the properties, cluster
analysis does help identifying homogeneous groups.
• “Moral”: cluster analysis needs to be adequately validated and it may be risky
to run a single cluster analysis and take the results as truly informative,
especially in presence of outliers.
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30. 03/02/15 Cluster analysis for market segmentation
Sara Dolnicar
University of Wollongong,
sarad@uow.edu.au
36. Leonard Kaufman and Peter Rousseeuw (2005), Finding Groups in Data: An
Introduction to Cluster Analysis, Wiley Series in Probability and Statistics, 337 p.
Mark Aldenderfer and Roger Blashfield (1984), Cluster Analysis (Quantitative
Applications in the Social Sciences), SAGE Publications, Inc., 90 p.
Brian Everitt, Sabine Landau and Morven Leese (2001) Cluster Analysis, Oxford
University Press, 248 p.
Marketing Segmentation (
http://www.beckmanmarketing8e.nelson.com/ppt/chapter03.pps. )
03/02/15 Cluster analysis for market segmentation