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Brand Penetration and Lifestyle Analysis of Customer Base From Machine Generated non-promotional SMS CDRs

IP.com Disclosure Number: IPCOM000235061D
Publication Date: 2014-Feb-26
Document File: 6 page(s) / 126K

Publishing Venue

The IP.com Prior Art Database

Abstract

Disclosed is a technique that creates insight of brand affinity and customer's lifestyle analysis based on activity that causes machine generated SMS alerts on customer's mobile. Non-promotional SMS CDRs are generated from machine and triggered by certain activities (like banking transaction, booking travel ticket, shopping) hence collection and analysis of such data, on the top of existing telecom EDW, provides additional attributes to understand customer's behavior across multiple interest area and take informed decision accordingly.

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Brand Penetration and Lifestyle Analysis of Customer Base From Machine Generated non-promotional SMS CDRs

The telecom service provider generates CDR from various network components when a mobile customer originates or receives the call/SMS. Based on peer-to-peer calls, various solutions have been implemented to identify customer's community that can be further used for churn prediction and viral marketing. These solution are largely covering peer-to-peer calls (i.e. human interaction on mobile) intended for telecom marketing team.

Now with increasing trend of authenticating customers from his/her mobile number, most of customer service organizations (e.g. Bank/ Online shopping / utilities / travel etc) sends regular alerts or event base based communication over mobile as SMS. These machine generated SMS are providing new source of information. The scope of the proposed technique is to explore this new set of data for comprehensive brand and lifestyle analysis that is not possible within the current landscape of telecom customer analysis.

These machine generated SMS CDRs do not carry conventional fixed digit mobile or PSTN. Different telecom network providers generate SMS originator's address in specific hexadecimal format. therefore a new metadata setup is required to cater details of all (or selective, as required ) SMS originator

Insight derived from machine generated SMS will be able to answer following


- What is the favorite lifestyle brand of customer base


- What is the financial preference of customer


- Which Lifestyle Brand association will send positive sentiment among customer base


- How to monetize hidden customer non telecom behavior discovery


- How combining customer profile as mobile user with machine generated SMS will lead to new pattern discovery

The proposed technique provides a solution that identifies SMS CDRs generated from machines and delivered to individual's mobile device. These SMS are caused by customer action (e.g. financial transactions, daily/periodic update etc). Sometime these machine generated SMS are sent to authenticate/confirm genuine user. these SMS are getting triggered based on certain condition that confirm customer's association with certain brand dealing in particular aspect of lifestyle.

This technique provides a framework to build metadata of all (or required) originator so that type of brand and other details can be collected.

By combining the data points from Machine SMS Metadata and Customer profile with SMS transaction data (CDRs). Powerful Brand and Lifestyle insight can be derived.

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Figure.1 is an illustration of various activities being performed by an individual that results in SMS generation as response.

Figure.2 illustrates how various brands reaches to a large customer base. Based on number of transactions, type of customer base coverage lot of marketing insights can be derived.

Figure.3 is high level process flow on how to generate Brand and Lifestyle Insight fr...