METHOD AND ARRANGEMENT FOR SEGMENTATION OF TELECOMMUNICATION CUSTOMERS

• US 20130179223A1
• Filed: 09/14/2010
• Published: 07/11/2013
• Est. Priority Date: 09/14/2010
• Status: Abandoned Application

• Current Assignee: Telefonaktiebolaget LM Ericsson
• Original Assignee: Telefonaktiebolaget LM Ericsson
• Inventors: Mohan, Saravanan, Garigipati, Prasad, Parthan, Kasarapu
• Application Number:
  US13/822,712
• Publication Number:
  US 20130179223A1
• Time in Patent Office: Days
• US Class Current: 705/7.33
• CPC Class Codes: G06F 11/3438 monitoring of user actions tracking the activity of the user H04L67/535
• CPC Class Codes: G06F 2201/875 Monitoring of systems including the internet
• CPC Class Codes: G06Q 30/0204 Market segmentation
• CPC Class Codes: G06Q 30/0255 based on user history

Abstract

A method and arrangement in a segmentation manager (200, 600) for forming segments of customers in a communications network for use when offering services to customers jointly in those segments. In the segmentation manager, data relating to the customers'"'"' service usage and websites browsed by the customers is collected (500) and subject domains associated to the browsed websites are identified (502). A browsing behaviour of each customer is also determined (504) based on their browsed websites and associated subject domains, and domain interests of each customer are determined (506) based on their browsing behaviour. At least one customer segment is then assigned (508) to each customer based on his/her service usage and domain interests.

Claims

1. A method of forming segments of customers in a communications network for use when offering services to customers jointly in said segments, the method comprising:

collecting data relating to the customers'"'"' service usage and websites browsed by the customers,

identifying subject domains associated to the browsed websites,

determining a browsing behaviour of each customer based on their browsed websites and associated subject domains,

determining domain interests of each customer based on said browsing behaviour, and

assigning at least one customer segment to each customer based on his/her service usage and domain interests.

2. A method according to claim 1, wherein the collected data relating to the customers'"'"' service usage is analyzed for determining any of:

type of service, number of sessions, number of distinct contacts, session duration, spending, time of day, week or season, and location.

3. A method according to claim 1, wherein the collected data is obtained from Call Detail Records (CDRs) and/or Deep Packet Inspection (DPI).

4. A method according to claim 1, wherein said data relating to browsed websites comprises a URL and a description for each website.

5. A method according to claim 1, wherein the subject domains are identified for said websites based on the presence of keywords in the websites which have been predefined for the subject domains.

6. A method according to claim 5, wherein identifying subject domains for said websites includes computing probabilities for the presence of said keywords in the subject domains and probabilities for the subject domains to contain said keywords.

7. A method according to claim 1, wherein the subject domains are identified for said websites by using the method “

Latent Dirichlet Allocation”

(LDA).

8. A method according to claim 1, wherein determining domain interests of each customer includes computing probabilities for the subject domains being associated to websites browsed by the customer.

9. A method according to claim 1, wherein assigning at least one customer segment to each customer includes determining a correlation between his/her service usage and domain interests and assigning the customer segment(s) based on said correlation.

10. A method according to claim 1, wherein said customer segment(s) is/are selected from an optimal number of customer segments determined by applying a K-means clustering algorithm on the collected information where a mean squared error is plotted against different numbers (K) of customer segments.

11. An arrangement in a segmentation manager configured to form segments of customers in a communications network to be used for offering services to customers jointly in said segments, comprising:

a data collector adapted to collect information on the customers'"'"' service usage (U) and information on websites browsed by the customers (B),

a browsing analyzer adapted to identify subject domains associated to the browsed websites, determine a browsing behaviour of each customer based on their browsed websites and associated subject domains, and to determine domain interests of each customer based on said browsing behaviour, and

a segmentation module adapted to assign a customer segment to each customer based on his/her service usage and domain interests.

12. An arrangement according to claim 11, further comprising a service usage analyzer adapted to analyze the collected data relating to the customers'"'"' service usage for determining any of:

type of service, number of sessions, number of distinct contacts, session duration, spending, time of day, week or season, and location.

13. An arrangement according to claim 11, wherein the data collector is further adapted to obtain the collected data from Call Detail Records (CDRs) and/or Deep Packet Inspection (DPI).

14. An arrangement according to claim 11, wherein said data relating to browsed websites comprises a URL and a description for each website.

15. An arrangement according to claim 11, wherein the browsing analyzer is further adapted to identify the subject domains for said websites based on the presence of keywords in the websites which have been predefined for the subject domains.

16. An arrangement according to claim 15, wherein the browsing analyzer is further adapted to identify subject domains for said websites by computing probabilities for the presence of said keywords in the subject domains and probabilities for the subject domains to contain said keywords.

17. An arrangement according to claim 11, wherein the browsing analyzer is further adapted to identify the subject domains for said websites by using the method “

Latent Dirichlet Allocation”

(LDA).

18. An arrangement according to claim 11, wherein the browsing analyzer is further adapted to determine domain interests of each customer by computing probabilities for the subject domains being associated to websites browsed by the customer.

19. An arrangement according to claim 11, wherein the segmentation module is further adapted to assign at least one customer segment to each customer by determining a correlation between his/her service usage and domain interests and assigning the customer segment(s) based on said correlation.

20. An arrangement according to claim 11, wherein the segmentation module is further adapted to select said customer segment(s) from an optimal number of customer segments determined by applying a K-means clustering algorithm on the collected information where a mean squared error is plotted against different numbers (K) of customer segments.