What is Convergent Cluster &
Ensemble Analysis (CCEA)?
Cluster analysis is a way of categorizing a collection of "objects,"
such as survey respondents, into groups or "clusters." Markets may
be composed of distinct segments, consisting of customers who have
different needs and desires, and who want products with different
capabilities. Cluster analysis, widely used within marketing
research for the past 25 years, can be especially helpful in
identifying potential market segments. Ensemble Analysis is a newer
approach that leverages multiple cluster solutions (an ensemble of
potential solutions) to find an even better, "consensus" solution.
Cluster/Ensemble analysis lets you see whether survey respondents
"cluster" naturally into identifiable groups. You can use product
preferences, desired benefits, usage habits, product requirements,
or other variables to explore the underlying structure of your
Once identified, clusters can be characterized in terms of
demographic or lifestyle variables, product consumption, company
size, SIC codes, etc. Cluster analysis can help identify market
segments and evaluate their potentials as targets for strategic
marketing. It can provide insights into: how to position your
products, opportunities for new products, targeting sales efforts,
and distribution channels to use.
A problem faced by users of cluster analysis is that every cluster
analysis always produces clusters, whether there is any underlying
structure in the data or not. Because we humans have the ability to
read meaning into even the most random of patterns, the fact that a
solution seems reasonable is no guarantee that the results would be
reproducible with a different sample of customers, a different set
of variables, or at a different time of the day.
Our Convergent Cluster & Ensemble Analysis (CCEA) System addresses
this problem. It uses a "k means" method of determining clusters (or
for developing a consensus solution, based on the notion of
"clustering on clusters") that involves iterating from random but
strategically chosen starting points. CCEA automatically replicates
each analysis 30 times. Each replication is compared to every other
to assess its reproducibility. The most reproducible solution is
chosen automatically, and its level of reproducibility is reported.
You can use CCEA with data from any source, so long as you format
the data as .csv (comma-separated values) files. Such files are
saved easily from common programs such as Excel.
Our Flagship Platform and General Interviewing Tool
SSI Web - Our flagship platform that includes a variety of
components for general interviewing (CiW), conjoint analysis (ACBC,
CBC, ACA, and CVA) and MaxDiff.
Choice-Based Conjoint (CBC) Related Products
ACBC - Adaptive Choice-Based Conjoint -
CBC - Choice-Based Conjoint
CBC Advanced Design Module
CBC/HB - Hierarchical Bayes estimation for CBC.
CBC Latent Class Module - Latent Class estimation for CBC.
MBC - Menu-Based Choice
Ratings-Based Conjoint Products
ACA - Adaptive Conjoint Analysis
ACA/HB - Hierarchical Bayes estimation for ACA.
CVA - Conjoint Value Analysis
CVA/HB - Hierarchical Bayes estimation for CVA.
Market Simulation Tools
ASM - Advanced Simulation Module
CCS - Client Conjoint Simulator
Maximum Difference Scaling (MaxDiff)
MaxDiff - Maximum Difference Scaling
MaxDiff Analyzer - Maximum Difference Analyzer
Other Analytical Tools
CCEA - Convergent Cluster & Ensemble Analysis.
HB-Reg - Hierarchical Bayes estimation for general regression-based