Download choice of multivariate technique and its used for widgecorp

PHASE 4: INDIVIDUAL PORTION OF GROUP PROJECT
KENNETH C HOLMES
MGMT600-1502A-01
PROFESSOR HENRIETTA OKORO
MAY 11, 2015
CHOICE OF MULTIVARIATE TECHNIQUE AND ITS USES FOR WIDECORP
Introduction:
Previously I was charged with researching three multivariate techniques: Factor Analysis;
Multidimensional Scaling; and Cluster analysis. Now I am charged with choosing which of the
three techniques is most suitable for WidgeCorp to apply for its own business purposes, explain
why this technique is the most suitable, and how this technique will benefit WidgeCorp. This
document explains my findings.
The three types of Multivariate Analyses:
Factor Analysis:
The mission of Factor Analysis is to design the perfect product for the customer base to
increase sales. Factor Analysis helps companies determine which marketing efforts to pursue,
which need further evaluation, and which to eliminate. Factor Analysis involves changing one
variable at a time to determine the results, because there may be many variable, and each
variable must be tested independently to determine the outcome. The process is based on
obtaining focus group preferences based on comparisons of several products, determining the
product they liked best, and why?, enabling the manufacturer to gather information from
respondents without making costly changes to the product. Changes can include: improved
flavor, more flavor choices, improved quality, improved features, improved performance, more
appealing packaging, product size, more color options, enhanced product distribution, product
advertising, level of service, etc. Once the favored product has been determined, the
manufacturer can then design and manufacture the (ideal) perfect product to place in the market,
and increase sales. After the redesigned product has been placed in the market, the manufacturer
can then compare the sales before and after the changes, and properly conclude through Factor
Analysis the changes made are important to their customer base. The fact is Factor Analysis is
not a science, it takes large scale testing to determine the relationship of the variables, and it is
best to have an outside market research expert to conduct the Factor Analysis and evaluate the
results to ensure accurate cause and effect relationships (Lorette, 2015).
Multidimensional Scaling:
The mission of Multidimensional Scaling (MDS) is to design the (ideal) perfect product
for their customer base. MDS is a statistical techniques commonly used in business marketing
and social science used to ascertain consumer attitudes and preferences about product
similarities. The process requires respondents to evaluate the similarities between products,
compare them to what they see as the perfect product, and then their responses are charted on a
perceptual map, a grid with both X and Y axes representing specific product aspects (N.A.,
Multidimensional Scaling, 2015). Then the manufacturer designs the perfect product for their
customer based on analysis of the perpetual map, meaning the features respondents prefer. After
the perfect product has been placed in the market, the manufacturer can then determine the
success of the product based on sales. Multi Dimensions Scaling is preferred over Factor
Analysis because the process relies on respondent’s preferences, and not preset attributes, or the
researcher judgements (N.A., Multidimensional Scaling in Marketing, 2015).
Cluster Analysis:
Cluster Analysis is popular technique widely used to segment customers, products and
stores based on patterns. Cluster Analysis is an explanatory data analysis tool that takes similar
observations from a larger population and breaks them into smaller groups based on maximal or
minimal correlation. Cluster Analysis has many features including: the ability to find concealed
patterns and structures in data without a specific assumption; the ability to identify similarities in
specific behaviors or parameters; and the ability to reveal patterns in data without explaining
why they are there. There are two types of Cluster Analysis including: non-hierarchical which
divides larger datasets into smaller data sets, until only one cluster remains (the method divides a
larger data set of N objects into M clusters, and K means); and hierarchical which clusters related
objects until only one cluster remains, in a hierarchical manner. Which type used is based on the
objective of clustering, the type of output desired, the hardware and software facilities available,
and the size of the dataset. Clustering is used for several reasons including: performing
segmentation from a larger group of data. For example, clustering similar products based on their
attributes; anomaly detection. For example, identifying fraudulent transactions; and breaking
large data sets into smaller groups for use with other testing techniques (Vohra, 2011).
Steps in Cluster Analysis (E. Mooi, 2011):
1) Choose the appropriate variable to cluster: This step is vital because the wrong
assumptions and variables, will lead the wrong segmentation, and the wrong marketing
strategies.
Chart 1 referenced from (E. Mooi, 2011).
Chart 1
TYPES AND EXAMPLES OF CLUSTERING VARIABLES
General
Specific
Observable (directly measurable) Cultural, geographical,
User status, usage frequency, store
demographic, socio-economic and brand loyalty
Unobservable (inferred)
Psychographics, values,
Benefits, perceptions, attitudes,
personality, lifestyle
intentions, preferences
Adapted from Wedel and Kamakura (2000)
2) Determine the clustering method to form the cluster groups: This step is vital because
different methods have different decisions before analysis can begin. Clustering methods
include both hierarchical and non-hierarchical.
3) Determine the number of clusters needed: A few clusters is easier to understand and
determine the necessary marketing strategies, while more clusters helps to determine the
differences between the segments, and provides for more marketing strategy options. In
other words, the target markets must be large enough to be profitable.
4) Label the final clusters and interpret the solution: This step ensures correct interpretation
of the results and determines the appropriate marketing strategies.
Real life companies that use Cluster Analysis:
SABMiller, a leading international beer brewers and producer of Coca Cola products
used Cluster Analysis to: classify their liquids to make communication easier for both marketing
and technical staff; help consumers understand the specific differences between beers; determine
consumer preferences; identify opportunities for their brands; and to determine the acceptability
of their brands in local and international markets (Camo, 2015).
ConnectFast Inc. (a cellular telecom company) used Cluster analysis to: segment their
customers based on local and international calling habits, age, and income, and then developed
advertising and marketing strategies to attract additional customers based on those parameters,
and minimize customers changing cellular service providers, all while optimizing their cost.
ConnectFast offers: prepaid and postpaid billing; internet plans with 2G, 3G, and 4G data plans;
national and international calling; national and international roaming; and national and
international data roaming; all to provide options based on their customer segments (Upadhyay,
2013).
Travel Alberta (the social media, marketing, and advertising organization for travel to
Alberta) used Cluster Analysis to design a profitable advertising and promotion campaign
“Travel Alberta Made To Order” by segmenting domestic tourists based on their decision
making behavior: their travel, climate, and activity preferences (Ritchie, 2002).
Saks Fifth Avenue was facing declining sales, and decreased turnover, and responded by
reducing their inventory and improving their fashion content. They used Cluster Analysis to gain
a better understanding of customer’s preferences, and their attitudes about buying and shopping
through the use of questionnaires’. The information was then used to create marketing and
advertising based on those preferences, expand their Men’s accessories collection in their new
Beverly Hills store, and then expanded the men’s Accessory shop into other stores. This efforts
increased sales, and enhanced their visibility in a market that is frequently ignored because of a
lack of expertise in purchasing the products (E. Mooi, 2011).
The differences between the techniques:
Both Factor Analysis and Multidimensional Scaling (MDS) have the mission to design
the perfect (ideal) product for the customer base to increase sales. Both methods use focus
groups to compare products to determine their preferred product and why, both methods use the
information to determine the favored product without making costly changes, both methods use
the information gathered to design and produce the perfect (ideal) product for the customer base
and place the new product on the market, and both methods evaluate the new product design
based on sales before and after the changes.
The differences lie in how they achieve and analyze the responses. Factor Analysis: uses
preset attributes to determine the respondent’s favorite; uses the researchers’ judgement to
analyze the data and results; uses large scale testing to determine the relationship of the
variables; and usually require an outside market research expert to conduct the analysis and
evaluate the results to ensure accuracy. MDS: uses respondents preferences instead of preset
attributes; has the respondent compare the similarities between products and compare them to
their vision of the perfect (ideal) product; charts the responses on a perceptual map (a grid with
both X and Y axes representing specific product aspects); relies on the results instead of the
researcher’s judgement; and for all those reasons is preferred over Factor Analysis (N.A.,
Multidimensional Scaling, 2015 and N.A., Multidimensional Scaling in Marketing, 2015).
Cluster Analysis is widely used to segment customers, products and stores. Cluster
Analysis does not use focus groups to determine customer preferences. Cluster Analysis is used
to: reduce a larger data set of similar observations into smaller clusters based on level of
correlation; can be used to cluster data in a hierarchical manner; identifies specific behaviors or
parameters; reveals patterns in data without explaining why they are there; can be used to create
new products for the customer based on consumer patterns identified; can be used for anomaly
detection including fraudulent transactions; and for breaking large data sets into smaller groups
for use with other testing techniques (Vohra, 2011).
In conclusion, Factor Analysis and Multidimensional Analysis are used to design and
produce the perfect (ideal) product based on respondents preferences, while Cluster Analysis is
used to segment customers, products, and stores based on specific parameters including: gender;
ethnicity; age; income; frequency of use; price of the product they use; type of product they use;
the variety of product a store carries; the sales of the products the store carries; and then create
advertising and marketing strategies, and new or improved products for each segment of the
customer base.
Business Clustering:
Clustering is more than just categorizing your customer base by various parameters.
Regarding business and industry: clusters are connected firms and institutions in related or
unrelated industries; they can be the competition or complementary businesses; they are located
in the same industrial park or area; and usually use the same supporting services. Supporting
service usually include: parts, component, and raw material suppliers; tech services; and shipping
and delivery services. The combined force of these businesses enables resource and solution
sharing to create and take advantage of market opportunities. Examples include: London’s
fashion district and industry; London’s film industry including the BBC: Los Angeles and the
film, music, and fashion industries; Miami’s fashion and film industry; New York City’s fashion
district; New York City’s financial district, including brokerage houses and banking; New York
City’s SoHo art district; Paris’s fashion district and industry; Paris’s art district; and many more
(Advameg Inc., 2015).
Business clustering benefits include (Advameg Inc., 2015):
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Productivity increases related to specialization, the sharing of information, coalitions, and
public goods access.
Can create a competitive advantage through cooperative purchasing of products, supplies,
and services.
Encourages product innovation due to competition and research cooperatives.
Encourages expansion of business into new markets and territories.
Encourages local economic growth and development, and employment recruitment.
Attracts investors both domestic and foreign.
Encourages relationships with the local chambers of commerce.
The chosen Multivariate technique:
What technique was selected and why:
The group as a whole determined Cluster Analysis to be the most useful and practical for
WidgeCorp to use. This conclusion is based on extensive research of the subject, well thought
out comparisons of the three techniques, the purposes of each technique, case studies from
various industries, and the next step for WidgeCorp based on their situation. Since they are a
market leader in the snack food industry with a recently added cold beverage line, we determined
Widgecorp needs to know their customer bases, have a full understanding of the products they
are purchasing, and to create effective marketing and advertising campaigns, and all of this
requires separating and identifying the market segments to make the process cost effective.
The application of Factor Analysis or Multidimensional Scaling would only slow down
progress, since both techniques have the mission of designing and producing the perfect product,
both require trending data on markets we have just entered, and that we are lacking trending data
on. At this point WidgeCorp needs to establish advertising and marketing campaigns to create
more exposure for their snack food line, and encourage consumers to try our cold beverage
line. The combined force of the snack food and cold beverage lines, with an effective advertising
and marketing strategy have the potential to make WidgeCorp a highly profitable and worthy
competitor in the snack and beverage industry, and potentially make WidgeCorp a snack food
and cold beverage GIANT. Once we have produced a successful marketing and advertising
campaign, then we can take that information and concentrate our efforts on enhancing the lines
with fabulous new snacks and beverages, while being cost efficient in the process.
I must also add that Cluster Analysis is useful for segmenting customer, products, and
stores, and for clustering a business based on the industry and region or regions of operation. The
benefits are immense, and the results will make a big difference on: sales, profits, and valuable
business connections and arrangement with the industry supply chain and the competition.
Benefits of using Cluster Analysis for WidgeCorp:
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Segmenting their retailer base by types and quantities of products sold to maximize the
availability and distribution of those products, and drive sales.
Segment their consumer base by: age; gender; income; marital status; family status (have
children); ethnicity; and dietary habits (vegetarian or non-vegetarian, or healthy snacks
and beverages vs. traditional snacks and beverages).
Segment their product base according to product purchases and frequency.
Use their market segmentation, and information from their consumer hotline to create
additional products specifically for their market segments.
Create advertising and marketing campaigns to target specific segments of their
consumer base, and drive sales.
Create advertising to educate their consumers regarding WidgeCorp’s line of healthy and
traditional snacks and beverages, all to encourage existing and new customers to try their
products, and drive sales.
Applications of business clustering for WidgeCorp:
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Increase their productivity by sharing industry and consumer preference research
information.
Create a competitive edge over the competition through the use of cooperative
purchasing agreements with cooperative competitors and support services.
Increase their innovation, and expand their product line through competition.
Expanding their business into untapped national and international markets.
They can use their success to attract investors from around the world.
Develop a competitive edge through strong relationships with the local chambers of
commerce, and local businesses and schools to create brand loyalty to WidgeCorp and
their product line.
Marketing ideas for WidgeCorp:
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Create a magazine and television advertising campaign using a series of advertisements
depicting women, men, children, and families from each market segment with both the
traditional and healthy lines of snacks and beverages, and drive sales.
Create a series of in store promotional boards depicting women, men, children, and
families with both the traditional and healthy lines of snacks and beverages, and drive
sales.
Create magazine, television, and internet advertising campaigns promoting WidgeCorp’s
participation in the Student Healthy Lunch Program, to create positive publicity for
WidgeCorp, potentially establish new clients, and drive sales.
Product innovation ideas for WidgeCorp:
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WidgeCorp could use customer segmentation to expand their product lines by using
products purchased by that segment as a reference, without the need for additional
research, and cost effectively.
WidgeCorp could add prepackaged snack and lunch combinations (snack, sandwich and
beverage), both traditional and healthier versions, to make lunch choices for kids easier,
and to make product purchasing easier for the consumer.
Conclusion:
Based on extensive research and detailed case studies, Cluster Analysis would provide
the most benefits for WidgeCorp. By using Cluster Analysis: Widgecorp will be able to segment
their customer base, product line, and retailer base; determine which products are purchased by
what segment; the volume of the purchases; and enable advertising and marketing to create
campaigns geared toward those segments. In addition to those benefits Widgecorp will also be
able to determine: if adjustments are needed for production and shipping of their products; if
replenishment levels are acceptable or need adjustments; if the current advertising and promotion
of their products need adjustments; and develop invaluable relationships with business and
institutions to create the brand loyalty essential for a competitive edge. WidgeCorp will also be
able to educate the consumer about their product line, and the difference between theirs and the
competition’s, and enhance their strategy on meeting consumer demand and incorporate
customer feedback. In conclusion, addressing these questions will help WidgeCorp address any
issues, add new snack and beverage products, increase product exposure, increase their brand
loyalty, increase sales, and achieve the most results for their money.
REFERENCES
Advameg Inc. (2015). Clusters - advantage, benefits, Benefits of clustering. Retrieved from
Reference for Business: EncyclopediafFor Business: www.referenceforbusiness.com ›
Bo-Co
Camo. (2015). SABMiller: Bringing science to the art of brewing better beers . Retrieved from
Case Studies: Multivariate Data Analysis-Camo: www.camo.com/resources/casestudies.html
E. Mooi, a. M. (2011). Cluster Analysis. Retrieved from Springer.com:
www.springer.com/cda/content/document/cda_downloaddocument/...
Lorette, K. (2015). Importance of Factor Analysis in Marketing. Retrieved from
smallbusiness.chron.com › … › Importance of Marketing
N.A. (2015). Multidimensional Scaling. Retrieved from
www.allbusiness.com/barrons_dictionary/dictionary-multidimensional...
N.A. (2015, May 3). Multidimensional Scaling in Marketing. Retrieved from Econonomic
Expert.com: www.economicexpert.com/a/Multi:dimensional:scaling:in:marketing.htm
Ritchie, S. H. (2002, July 8). Understanding the domestic market using cluster analysis: A case
study of the marketing efforts of Travel Alberta . Retrieved from Sage Publications:
jvm.sagepub.com/content/8/3/263.abstract
Upadhyay, R. (2013, November 10). Customer Segmentation & Cluster Analysis – Telecom Case
Study (Part 1). Retrieved from YOU CANanalytics: ucanalytics.com/blogs/customersegmentation-cluster-analysis...
Vohra, G. (2011, February 23). Cluster Analysis for Business. Retrieved from ezinearticles.com ›
Business
Peer evaluations:
Appendix 1
Peer Evaluation Form
Kenneth C Holmes/Group #3
My Group Members:
(not including me)
NAME
(First, Last)
1
Nicole,
Paulson
2
Jean Renel,
Casimir
3
Joni, Allen
4
Jeanette,
Pitchford
5
Marlon,
Ashley
6
Irina,
Knight
7
Xin Hui, Li
1.Timeliness
18/20
18 /20
20/20
18/20
0/20
20/20
18/20
2.Neatness
20/20
20/20
20/20
20/20
0/20
20/20
20/20
3.Correctness
20/20
18/20
20/20
18/20
0/20
15/20
20/20
4.Cooperativeness
20/20
20/20
20/20
20/20
0/20
20/20
20/20
5.Contributed his/her Fair Share
20/20
20/20
20/20
20/20
0/20
20/20
20/20
98/100
96/100
100/100
96/100
0/100
95/100
TOTAL
My experience with group 3:
Since I was the first to communicate with the entire group, I was by default chosen as the
group leader. The group discussion board started out with two posts by 5/5/15, but by 5/7/15 the
last three participating group members posted their research, and the communication flowed
rather well. The participating members include: Joni Allen, Kenneth C Holmes, Irina Knight,
Xin Hui Li, Jean Renel Casimir, Nicole Paulson, and Jeanette Pitchford. The final responses
regarding the consensus arrived on Saturday 5/10/15, which prompted me to post the final
consensus. This delay annoyed my because I hoped for an earlier consensus, but it worked out.
I scored the members as follows. Joni Allen received a (100%), because her posts were
on time, neat and easy to understand, her research is accurate and her case studies are excellent,
she was cooperative and a big help with gaining consensus, and she contributed her fair share.
Irina Knight received a (95%), because her posts were on time, neat and easy to read, her
research was accurate: but she only discussed Cluster Analysis and used an excellent case study,
she was very cooperative, and contributed her fair share. Xin Hui Li received a (98%), because
her posts were timely: except she waited till Saturday evening to post her consensus, her posts
were neat and easy to read, her research was accurate and used some good case studies: except
for the last one, where I do not know what they do or used it for, she was cooperative, and she
contributed her fair share. Jean Renel Casimir received a (96%), because her posts were timely:
except she also waited until Saturday night to post her consensus, her posts were neat and easy to
read, her research was accurate: but she did not provide a case study for MDS, she was
cooperative, and she contributed her fair share. Nicole Paulson: received a (98%), because her
post was late: she posted on Sunday night and I understand why, her posts were neat and easy to
read, her research was accurate and she used good case studies, she was cooperative, and she
contributed her fair share. Jeanette Pitchford: received a (96%), because her post was late: she
also posted on Sunday night and I understand why, her posts were neat and easy to read, her
research was accurate and she used examples of how the methods are used: but she did not
discuss an example of Cluster Analysis, she was cooperative, and she contributed her fair share.
Since we were missing one member there results are as follows. Marlon Ashley received
a (zero) because he did not participate. He emailed me asking what his part of the project is, and
that he will do it on the weekend. I emailed him all relevant information regarding his part, and
we have no posts from him.
Overall it was a good experience. I am just not a fan of waiting for responses from others.
I am the type who wants to get things done, and does not like delays. For future reference, I think
you would get a much better mix of subjects if we were able to choose what technique we
98/100
wanted to write about. That would avoid problems and delays in consensus, and provide you a
larger mix of techniques to read about.