Using cluster analysis, a customer

Using cluster analysis, a customer ‘type’ can represent a homogeneous market segment.
Identifying their particular needs in that market allows products to be designed with greater
precision and direct appeal within the segment. Targeting specifi c segments is cheaper and
more accurate than broad-scale marketing. Customers respond better to segment marketing
which addresses their specifi c needs, leading to increased market share and customer
retention. This is valuable, for example, in banking, insurance and tourism markets. Imagine
four clusters or market segments in the vacation travel industry. They are: (1) The elite –
they want top level service and expect to be pampered; (2) The escapists – they want to get
away and just relax; (3) The educationalist – they want to see new things, go to museums,
have a safari, or experience new cultures; (4) the sports person – they want the golf course,
tennis court, surfi ng, deep-sea fi shing, climbing, etc. Different brochures and advertising is
required for each of these.
Brand image analysis, or defi ning product ‘types’ by customer perceptions, allows
a company to see where its products are positioned in the market relative to those of its
competitors. This type of modelling is valuable for branding new products or identifying
possible gaps in the market. Clustering supermarket products by linked purchasing patterns
can be used to plan store layouts, maximizing spontaneous purchasing opportunities.
Banking institutions have used hierarchical cluster analysis to develop a typology of
customers, for two purposes, as follows:
To retain the loyalty of members by designing the best possible new fi nancial products
to meet the needs of different groups (clusters), i.e. new product opportunities.
To capture more market share by identifying which existing services are most profi table
for which type of customer and improve market penetration.
One major bank completed a cluster analysis on a representative sample of its members,
according to 16 variables chosen to refl ect the characteristics of their fi nancial transaction
patterns. From this analysis, 30 types of members were identifi ed. The results were useful
for marketing, enabling the bank to focus on products which had the best fi nancial performance;
reduce direct mailing costs and increase response rates by targeting product promotions
at those customer types most likely to respond; and consequently, to achieve better
branding and customer retention. This facilitated a differential direct advertising of services
and products to the various clusters that differed inter alia by age, income, risk taking
levels, and self-perceived fi nancial needs. In this way, the bank could retain and win the
business of more profi table customers at lower costs.
•
•
Cluster analysis. The statistical method of partitioning a sample into
homogeneous classes to produce an operational classifi cation.
Cluster analysis, like factor analysis, makes no distinction between dependent and
independent variables. The entire set of interdependent relationships are examined. Cluster
analysis is the obverse of factor analysis. Whereas factor analysis reduces the number of
variables by grouping them into a smaller set of factors, cluster analysis reduces the number
of observations or cases by grouping them into a smaller set of

Hasil (Bahasa Indonesia) 1:

Using cluster analysis, a customer ‘type’ can represent a homogeneous market segment.Identifying their particular needs in that market allows products to be designed with greaterprecision and direct appeal within the segment. Targeting specifi c segments is cheaper andmore accurate than broad-scale marketing. Customers respond better to segment marketingwhich addresses their specifi c needs, leading to increased market share and customerretention. This is valuable, for example, in banking, insurance and tourism markets. Imaginefour clusters or market segments in the vacation travel industry. They are: (1) The elite –they want top level service and expect to be pampered; (2) The escapists – they want to getaway and just relax; (3) The educationalist – they want to see new things, go to museums,have a safari, or experience new cultures; (4) the sports person – they want the golf course,tennis court, surfi ng, deep-sea fi shing, climbing, etc. Different brochures and advertising isrequired for each of these.Brand image analysis, or defi ning product ‘types’ by customer perceptions, allowsa company to see where its products are positioned in the market relative to those of itscompetitors. This type of modelling is valuable for branding new products or identifyingpossible gaps in the market. Clustering supermarket products by linked purchasing patternscan be used to plan store layouts, maximizing spontaneous purchasing opportunities.Banking institutions have used hierarchical cluster analysis to develop a typology ofcustomers, for two purposes, as follows:To retain the loyalty of members by designing the best possible new fi nancial productsto meet the needs of different groups (clusters), i.e. new product opportunities.To capture more market share by identifying which existing services are most profi tablefor which type of customer and improve market penetration.One major bank completed a cluster analysis on a representative sample of its members,according to 16 variables chosen to refl ect the characteristics of their fi nancial transactionpatterns. From this analysis, 30 types of members were identifi ed. The results were usefulfor marketing, enabling the bank to focus on products which had the best fi nancial performance;reduce direct mailing costs and increase response rates by targeting product promotionsat those customer types most likely to respond; and consequently, to achieve betterbranding and customer retention. This facilitated a differential direct advertising of servicesand products to the various clusters that differed inter alia by age, income, risk takinglevels, and self-perceived fi nancial needs. In this way, the bank could retain and win thebusiness of more profi table customers at lower costs.••Cluster analysis. The statistical method of partitioning a sample intohomogeneous classes to produce an operational classifi cation.Analisa cluster, seperti faktor analisis, tidak membuat perbedaan antara tergantung danvariabel independen. Seluruh rangkaian hubungan saling diperiksa. GugusAnalisis adalah teman imbangan faktor analisis. Sedangkan faktor Analisis mengurangi jumlahvariabel dengan mengelompokkan mereka ke dalam satu set yang lebih kecil dari faktor, analisa cluster mengurangi jumlahpengamatan atau kasus dengan mengelompokkan mereka dalam satu set lebih kecil