AUTOMATED ENTITY CLASSIFICATION USING USAGE HISTOGRAMS & ENSEMBLES

• US 20150127455A1
• Filed: 11/06/2014
• Published: 05/07/2015
• Est. Priority Date: 11/06/2013
• Status: Abandoned Application

• Current Assignee: Amplero Inc.
• Original Assignee: Globys Inc
• Inventors: Penzotti, Julie, Mehanian, Courosh, Downs, Oliver, Cazzanti, Luca
• Application Number:
  US14/534,862
• Publication Number:
  US 20150127455A1
• Time in Patent Office: Days
• US Class Current: 705/14.49
• CPC Class Codes: G06F 18/213 Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
• CPC Class Codes: G06F 18/23 Clustering techniques
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0251 Targeted advertisements

Abstract

Techniques disclosed herein employ entity-activity data expressed in a discrete distribution (histogram) form having one or many dimensions to dynamically classify the entity'"'"'s usage and/or behavior patterns, where groupings or segmentations of different entities that exhibit similar usage patterns are identified using various approaches, including dimensionality reduction, and/or clustering procedures. A consensus or ensemble clustering may be generated that represents a clustering of clusters, based on subclusterings themselves, and/or any combination of subclusters with entity-activity data to selectively execute a market offering campaign. In one embodiment, the resulting ensemble clusterings enable selective directing of targeted offerings to a telecommunication provider'"'"'s customers.

Claims

1. A network device, comprising:

a transceiver to send and receive data over a network; and

a processor that performs actions, comprising;

receiving telecommunications customer data for a plurality of customers;

extracting from the customer data a usage histogram for each of the plurality of customers;

computing for each of the plurality of customers, a reduced dimensionality usage histogram from the extracted usage histograms;

performing a clustering from the reduced dimensionality usage histograms to generate a plurality of clusters; and

classifying each customer time series within one of the plurality of clusters, the classifications selectively usable to dynamically market to a customer identified by a cluster.

2. The network device of claim 1, wherein for each of the plurality of customers the processor performs actions, further comprising:

combining the cluster classification from the usage histogram content with cluster classifications from other clustering solutions;

performing a consensus clustering of the combined cluster classifications;

classifying each customer with the consensus cluster assignment, the classifications usable to dynamically market to at least one customer identified by the consensus cluster.

3. The network device of claim 2, wherein combining the cluster classifications further comprises combining the cluster classifications with at least some of the received telecommunications customer data prior to performing the consensus clustering.

4. The network device of claim 1, wherein the clusters being selectively usable to dynamically market to a customer identified by a cluster, further comprises:

employing a threshold value that is applied to data within a cluster to determine whether to provide an offering at a given time or location to a given customer; and

when it is determined that the offering has a likelihood of not being accepted by the given customer based on the threshold for the given time and location, then inhibiting sending of the offering to the given customer; and

otherwise,

sending the offering to the given customer at the given time or location.

5. The network device of claim 1, wherein performing a clustering from the reduced dimensionality usage histogram to generate a plurality of clusters, further comprising:

determining a number of clusters to generate in the plurality of clusters using a statistical measure of an orthogonality of data types within the telecommunications customer data for the plurality of customers.

6. The network device of claim 1, wherein the usage histograms are represented using matrix-factorized histogram coefficients.

7. The network device of claim 1, wherein classifying each customer time series is based on training of a behavioral classification model that employs a cross-validation mechanism to select a minimum number of training patterns to satisfy a selected criteria.

8. The network device of claim 1, wherein for each of the plurality of customers the processor performs actions, further comprising:

combining the cluster classification from the usage histogram content with cluster classifications from a defined number of other clustering solutions, the number of clusters that are combined is determined based on a number of basis vectors obtained in a non-negative matrix factorization decomposition of a training set of data.

9. A system, comprising:

one or more non-transitory storage devices usable to store customer data; and

one or more processors that perform actions, comprising;

receiving telecommunications customer data for a plurality of customers;

extracting from the telecommunications customer data a usage histogram for each of the plurality of customers, wherein each histogram includes a customer'"'"'s usage pattern over a given time window;

computing for each of the plurality of customers, a reduced dimensionality usage histogram from the extracted usage histograms;

performing a clustering from the reduced dimensionality usage histogram to generate a plurality of clusters; and

classifying each customer time series within one of the plurality of clusters, the classifications selectively used to dynamically identify an occasion when to perform an interaction directed towards a customer identified by a cluster.

10. The system of claim 9, wherein computing for each of the plurality of customers, a reduced dimensionality usage histogram includes using a non-negative matrix factorization to generate a number of basis vectors.

11. The system of claim 9, wherein classifying each customer time series is based on training of a behavioral classification model that employs a cross-validation mechanism to select a minimum number of training patterns to satisfy a selected criteria.

12. The system of claim 9, wherein for each of the plurality of customers the one or more processors perform actions, further comprising:

combining the cluster classification from the usage histogram content with cluster classifications from other clustering solutions;

performing a consensus clustering of the combined cluster classifications;

classifying each customer within the consensus cluster assignment, the classifications being used to dynamically market to at least one customer identified by a consensus cluster.

13. The system of claim 12, wherein combining the cluster classifications further comprises combining the cluster classifications with at least some of the received telecommunications customer data prior to performing the consensus clustering.

14. The system of claim 9, wherein at least one of the other clustering solutions is determined using a different clustering technique than that used for determining the cluster classification.

15. The system of claim 9, wherein the clusters being selectively used to dynamically market to a customer identified by a cluster, further comprises:

employing a threshold value that is applied to data within a cluster to determine whether to provide an offering at a given time or location to a given customer; and

when it is determined that the offering has a likelihood of not being accepted by the given customer based on the threshold for the given time and location, then inhibiting sending of the offering to the given customer; and

otherwise,

sending the offering to the given customer at the given time or location.

16. The system of claim 9, wherein performing a clustering from the reduced dimensionality usage histogram to generate a plurality of clusters, further comprising:

determining a number of clusters to generate in the plurality of clusters using a statistical measure of an orthogonality of data types within the telecommunications customer data for the plurality of customers.

17. An apparatus comprising a non-transitory computer readable medium, having computer-executable instructions stored thereon, that in response to execution by a special purpose computing device, cause the special purpose computing device to perform operations, comprising:

receiving telecommunications customer data for a plurality of customers;

extracting from the telecommunications customer data a usage histogram for each of the plurality of customers, wherein each histogram includes a customer'"'"'s usage pattern over a given time window;

computing for each of the plurality of customers, a reduced dimensionality usage histogram from the extracted usage histograms;

performing a clustering from the reduced dimensionality usage histogram to generate a plurality of clusters; and

classifying each customer time series within one of the plurality of clusters, the classifications selectively being used to dynamically identify an occasion when to perform an interaction directed towards a customer identified by a cluster.

18. The apparatus of claim 17, wherein for each of the plurality of customers the special purpose computing device to perform operations, further comprising:

combining the cluster classification from the usage histogram content with cluster classifications from other clustering solutions;

performing a consensus clustering of the combined cluster classifications;

classifying each customer with the consensus cluster assignment, the classifications selectively used to dynamically market to at least one customer identified by a consensus cluster.

19. The apparatus of claim 18, wherein combining the cluster classifications further comprises combining the cluster classifications with at least some of the received telecommunications customer data.

20. The apparatus of claim 17, wherein the clusters being selectively used to dynamically market to a customer identified by a cluster, further comprises:

employing a threshold value that is applied to data within a cluster to determine whether to provide an offering at a given time or location to a given customer; and

when it is determined that the offering has a likelihood of not being accepted by the given customer based on the threshold for the given time and location, then inhibiting sending of the offering to the given customer; and

otherwise,

sending the offering to the given customer at the given time or location.

21. The apparatus of claim 17, wherein performing a clustering from the reduced dimensionality usage histogram to generate a plurality of clusters, further comprising:

determining a number of clusters to generate in the plurality of clusters using a statistical measure of an orthogonality of data types within the telecommunications customer data for the plurality of customers.