SYSTEMS AND METHODS FOR SEGMENTING BUSINESS CUSTOMERS

• US 20140188564A1
• Filed: 12/31/2012
• Published: 07/03/2014
• Est. Priority Date: 12/31/2012
• Status: Abandoned Application

• Current Assignee: Pitney Bowes Incorporated (Böwe Systec AG)
• Original Assignee: Pitney Bowes Incorporated (Böwe Systec AG)
• Inventors: Ghatti, Venkat Ram, Merola, John A.
• Application Number:
  US13/731,333
• Publication Number:
  US 20140188564A1
• Time in Patent Office: Days
• US Class Current: 705/7.33
• CPC Class Codes: G06Q 30/0204 Market segmentation

Abstract

Systems and methods for providing market segmentation using a unique two-stage clustering system are provided. The system may also employ regional interpolation and estimation methods that account for local business environment. In certain additional configurations, a generic geo-firmographic model is enhanced with seller data such as data specific to a particular vertical market and/or data specific to a particular seller'"'"'s business customers.

Claims

1. A computer implemented method for processing a multi-stage clustering of potential customers comprising:

obtaining data directly related to the potential customers;

processing the data directly related to the potential customers;

processing a first stage clustering of the processed data directly related to the potential customers;

obtaining data indirectly related to the potential customers;

processing the data indirectly related to the potential customers;

combining the processed, first-stage clustered data directly related to the potential customers and the processed data indirectly related to the potential customers; and

processing a second stage clustering of the combined data.

2. The method of claim 1, further comprising:

obtaining profiling data related to the potential customers;

attaching clustering identifiers from the second stage clustering to the profiling data; and

outputting a representation of important attributes by variable distributions.

3. The method of claim 1, wherein,

processing the data directly related to the potential customers includes:

removing a plurality of columns having at least a threshold number of values missing.

4. The method of claim 3, wherein,

processing the data directly related to the potential customers further includes:

removing a plurality of outlier rows using median absolute deviation.

5. The method of claim 4, wherein,

processing the data directly related to the potential customers further includes:

removing duplicate columns by correlation.

6. The method of claim 5, wherein,

processing the data directly related to the potential customers further includes:

scaling by percentage and centering by size.

7. The method of claim 6, wherein,

processing the data directly related to the potential customers further includes:

performing a principal components analysis with scaling and centering disabled.

8. The method of claim 7, wherein,

processing the data indirectly related to the potential customers includes:

removing a plurality of columns having at least a threshold number of values missing.

9. The method of claim 8, wherein,

processing the data indirectly related to the potential customers further includes:

removing a plurality of outlier rows using median absolute deviation.

10. The method of claim 9, wherein,

processing the data indirectly related to the potential customers further includes:

removing duplicate columns by correlation.

11. The method of claim 10, wherein,

processing the data directly related to the potential customers further includes:

scaling by percentage.

12. The method of claim 10, wherein,

before processing a second stage clustering of the combined data,

scaling the combined by percentage.

13. The method of claim 1, wherein:

the potential customers consist of businesses.

14. The method of claim 13, wherein:

the potential customers consist of small and medium businesses.

15. The method of claim 1, wherein:

the first stage clustering includes application of a two-step clustering process.

16. The method of claim 1, wherein:

the first stage clustering includes application of a K-means clustering process.

17. The method of claim 1, wherein:

the second stage clustering includes application of the K-means clustering algorithm.

18. The method of claim 1, wherein:

data directly related to the potential customers includes proxy data.