Methods used : k-Means, RFM Model, K-means clustering algorithm, EM clustering, Generalized Differential RFM Method (GDRFM)
Customer Segmentation is to provide a full range of management perspective, enable to have a great chance for enterprises to communicate with customer and to enhance the return rate of customers.
Commonly used ones are : RFM Method, Customer Value matrix and CLV Method.
It will cost 5 times more to gain a new customer than to keep an existing one, and ten times more to get a dissatisfied customer back (Marcus C., 1998) - Harward
Statistical Way of Clustering algorithms include : partitioned-clustering, density-based clustering, fuzzy clustering , and hierarchical clustering.
In RFM analysis, there is sometime co-linearity found between Frequency and Monetary. Founder of RFM suggested to used Average value rather than total sum as Monetary, and frequency of purchases was converted to number of purchases.
A customer value matrix - used by Boston Consulting Group.
Frequency of Purchase (F) and Average Purchase Amount (M) are used for segmentation in 2*2 matrix used by Boston Consulting Group as Growth-Share.
Data mining consists of more than collecting and managing data; it also includes analysis and prediction. Data mining includes association, sequence or path analysis, classification, clustering and future activities.
Data Mining is the main step of the knowledge discovery in database (KDD) process. Data mining tasks are very distinct and divers because many patterns exist in a huge database. The data mining functionalities and the variety of knowledge they discover are: Characterization, Discrimination, Association Analysis, Classification, Prediction and Clustering.
Clustering Methods can be categorized into two different types of algorithms which are Hierarchical Algorithms and Non-Hierarchical or Partition Algorithms.
In Hierarchical algorithms, number of clusters is unknown in the beginning, which is a strong advantage of these algorithms over non-hierarchical methods. On the other hand once an instance is assigned to a cluster, the assignment is irrevocable. Therefore, we can say that the output of hierarchical methods can be used to generate some interpretations over the data set and may be used as an input for a non-hierarchical method, in order to improve the resulting cluster. (Similar to RFM and then using K-means is what i am proposing).
Non-hierarchical or Partition algorithms (NHC) typically determine all clusters initially, but they can also be used as divisive algorithms in the hierarchical clustering. Here the advantage is that the algorithm iterates for all possible movements of data points between the formed clusters until a stop-ping criterion met. The NHC algorithms are sensitive to initial partitions and due to this fact, there exists too many local minima.