FORM 2
THE PATENTS ACT, 1970
(39 of 1970) & THE PATENTS RULES.. 2003
COMPLETE SPECIFICATION
(Sec section 10, rule 13)
1. Title of the invention: CUSTOMER RETENTION SYSTEM
Application(s)
NAME NATIONALLY ADDRESS
TATA CONSULTANCY Indian Nirtnal Building, 9th Floor,
SERVICES LIMITED Nariman Point,
Mumbai 400021, Maharashtra, India
3. Preamble to the description
COMPLETE SPECIFICATION
The following specification particularly describes the invention and the manner in which it
is to be performed.

TECHNICAL FIELD
[0001] The present subject matter, in general, relates to customer retention, and in
particular, to a system for customer retention.
BACKGROUND
[0002] With growing competitive conditions in this dynamic and ever-changing global
economy, customer retention has become a critical success factor for almost all companies. Nearly every business is concerned about retaining its customers. One reason for this trend may be the cost of opening a new account, which is usually more than the cost of handling an existing account.
[0003] For this reason, companies are willing to refine their account acquisition,
retention, and growth strategies in a bid to grow their businesses. They also aim at understanding factors that influence customer interests when making decisions like continuing with a company or abandoning it. To this end, many companies try to come up with policies, for example, a client retention policy; however, for this, they would require a considerable amount of planning and resources.
[0004] Customer retention policies, in general, aim at providing over-and-above services
to existing customers. However, most of the time, the companies are unable to determine the exact measure of success or failure of such policies at the end of a term and therefore are apprehensive about implementing the policies.
[0005] As a possible solution, some companies hire consulting firms to help them plan
and develop policies that are rightly suited to their needs. However, a consulting firm may or may not have the industry insight that is required for a specific industry-driven company and, as a result, may fatter in accurately providing a stable solution or at least relate to needs and/or reasons of dissatisfaction of a particular group of customers the company is catering to.
SUMMARY
The subject matter described herein relates to a computer implementable system and method for customer retention. In one implementation, a customer profile object is tagged to an

associated profile type from among a plurality of profile types that are predefined based on a
plurality of profile parameters. Based on the profile type, one or more retention policies
corresponding to the associated profile type are selected from a plurality of retention policies and
provided to a customer for customer retention. The customer may select a retention policy from
amongst the one or more retention policies. Further, based on the selection, a weightage of at
least one retention policy from among the one or more retention policies is updated.
[0006] These and other features, aspects, and advantages of the present subject matter
will be better understood with reference to the following description and appended claims. This summary is provided to introduce a selection of concepts in a simplified form. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
BRIEF DESCRIPTION OF DRAWINGS
[0007] The above and other features, aspects and advantages of the subject matter will be
better understood with regard to the following description, appended claims, and accompanying drawings, where:
[0008] Fig. 1 illustrates an exemplary network environment for implementing a customer
retention system, according to an embodiment of the present subject matter.
[0009] Fig. 2 illustrates an exemplary computing device implementing the customer
retention system as described in Fig. 1, according to an embodiment of the present subject matter.
[00010] Fig. 3 illustrates an exemplary method for implementing a customer retention
strategy, according to an embodiment of the present subject matter.
DETAILED DESCRIPTION
[00011] Customer retention is one of the key necessities for organizations to adapt
themselves to meet the new constraints and take advantage of new opportunities. In this ever growing consumer market, the organisations may have thousands of customers making it difficult to implement customized retention schemes that can cater to such a huge clientele.

[000121 Conventionally, the organisations devise a set of retention policies that are offered
to all the customers as an incentive for subscribing to the organizations' service. The retention policies, generally, are driven on a trial and error basis. Generally, the retention policies are saved in a central server and accessed by employees of the organizations, while interacting with the customers. The employees select a policy from the server based on their experience and thus the success of the policies depends a lot on the judgement and the experience of the employees. This is because not all the customers may have same preferences due to which same set of retention policies may not be effective for all the customers. Thus, determining a retention policy that would be most suitable for a particular customer requires an expertise. However, many a
imes, employees of the organisation that interact with the customers lack such expertise,
esulting in a loss to the organisation.
000131 Additionally, conventional customer retention solutions employed by the
irganizations lack a system to keep a track of the needs of the customers and to establish reasons if their dissatisfaction with products and services of the organizations and consequently fail to pply the retention policies effectively.
00014] To this end, a system and method for implementing a customer retention strategy
s disclosed. The method and system for customer retention involve obtaining of a customer
irofile object of a target customer, i.e., a customer who is to be retained by the organization. The
ustomer profile object defines a profile of the customer and may include information regarding
profile parameters, such as, the lifetime value of the customer, the customer holdings, the income
racket, reasons for dissatisfaction with the organization. A profile type associated with the
ustomer profile object is then identified from amongst a number of profile types predefined in
the system based on various profile parameters. The profile type represents a category to which a
customer's profile belongs. Once the profile type is identified, one or more retention policies in
accordance with the identified profile type are provided to the target customer. Separate retention
policies specific to each of the profile type are devised. The retention policies being based on the
profile type are, thus, customized for the target customer and more suitable for the customer.
This enhances the likelihood of acceptance of the retention policy and in turn customer retention.
[ 00015] In one embodiment, this customer retention system may be implemented for
organizations in an industries that provide products and services directly to the customers, for

example, in industries such as insurance, telecom, and direct selling companies. These industries interact with and offer services directly to the customers. In another example, the customer retention system may be implemented in industries where sales and/or services are delivered to a customer through a value chain.
[00016] The customer retention system may be any computing device connected to a
network. The customer retention system may interact with a customer relationship management (CRM) system, for example, over the network to obtain a list of target customers and customer profile object the target customers.
[00017] The customer retention system determines a profile type for every target customer
from the plurality of predefined profile types that are categorized based on a plurality of profile parameters, such as, lifetime value of the customer, customer holdings, i.e., products and services subscribed by the customer, income bracket, family background, reasons for dissatisfaction with the organization, experience from prior interactions with the customer, if any. For example, a profile type A may be defined for male customers having high lifetime value and high income bracket while a profile type B may be defined for ladies from an medium income family background. Based on the customer profile object and the associated profile type of the target customer the customer retention system presents one or more retention policies corresponding to the associated profile type to the user. For example, the retention policy offered to the customers of profile type A may include policies that are particularly preferred by them while the customers associated with the profile type B may be offered various other retention policies more suitable for them.
[00018] The retention policies, in one example, are classified into preferred policies and
non-preferred policies for each profile type. The preferred policies are the retention policies having weightage more than a threshold value and are likely to be accepted by the target customer as a bargain for continuing association with the organization. The non-preferred policies are the retention policies having weightage less than a threshold value and are less likely to be accepted by the target customer as compared to the preferred policies.
[00019] In one embodiment, the customer retention system provides an interaction feature
to the user. The interaction feature facilitates a user to interact with other users for taking

suggestions relating to various retention policies. For example, the user may seek opinion of an expert in dealing with target customers of a certain profile type prior to suggesting a retention policy to a particular target customer having a similar profile type. The user may alternately go through interactions and unstructured knowledge saved by the expert for similar customers.
[00020] Once the response of the customer on the suggested retention policy is observed,
the system updates a weightage assigned to the retention policies. Further, information, such as behavioral traits of the target customer, observed in response to the retention policies offered to him may be recorded in the system. The system uses the information to update the customer profile of the target customer and further provide the updated customer profile to the CRM system.
[00021] While aspects of described systems and methods for network-based innovation
can be implemented in any number of different computing systems, environments, and/or configurations, the embodiments are described in the context of the following exemplary system(s).
Exemplary Systems
[00022] Fig. 1 illustrates an exemplary network environment 100 implementing a system
for customer retention strategy, according to an embodiment of the present embodiment. The network environment 100 includes a customer retention system 102, a network 104, one or more users devices 106-1, 106-2, 106-n, collectively referred to as user device(s) 106, and a customer relationship management (CRM) system 108. In one implementation, the user devices 106 may be accessed by users such as employees of an organization that provides various products and services directly to the customers. For example, the organization may be an insurance agency selling insurance policies and having employees working as insurance agents working for the organization. Further, the system 102 may be implemented in industries that provide products and services directly to the customers, for example, in industries such as direct selling companies that interact directly with the customers. The user device 106 interacts with the customer retention system 102, hereinafter referred to as system 102, over the network 104. The system 102 also interacts with the CRM system 108 over the network 104. In one embodiment, the user device 106 may also interact with the CRM system over the network 104.

[00023] The network 104 may be a wireless network, wired network, or a combination
thereof. The network 104 can also be an individual network or a collection of many such individual networks, interconnected with each other and functioning as a single large network, e.g., the Internet or an intranet. The network 104 can be implemented as one of the different types of networks, such as intranet, local area network (LAN), wide area network (WAN), the internet, and such. The network 104 may either be a dedicated network or a shared network, which represents an association of the different types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), etc.. to communicate with each other. Further, the network 104 may include network devices, such as network switches, hubs, routers, HBAs, for providing a link between the system 102 and the user device 106. The network devices within the network 104 may interact with the system 102 and the user device 106 through communication links,
[00024] The system 102 may be any computing device connected to the network 104. For
instance, the system 102 may be implemented as mainframe computers, workstations, personal computers, desktop computers, hand-held devices, multiprocessor systems, personal digital assistants (PDAs), laptops, network computers, minicomputers, servers and the like. The system 102 employs various modules for implementing a customer retention strategy for the target customers. In one embodiment, the system 102 includes an analysis module 110 and a tagging module 112.
[00025] The analysis module 110 is configured to generate a target customer list, i.e., a list
of customers who may be unsubscribing products or services of the organization. For example, in case of an insurance company, the target customer list includes the customers subscribed to various insurance policies and desiring to discontinue the subscription to those policies. The target customer list may be generated based on various customer feedbacks recorded in the CRM 108. For example, the target customer list may be identified based on a cancellation request received from a target customer for cancelling the subscription. In addition, the target customer list may also be generated based on a retention probability, i.e., the probability of retaining a customer. In one implementation, the system 102 may determine the retention probability in interaction with an external server that calculates the retention probability. In another

implementation, the system 102 may use predictive analytics to calculate the retention probability.
[00026] The tagging module 112 provides the target customer list to the user device 106.
The user device 106 may be accessed by the user to select a target customer with whom the user intends to interact. The user device 106 based on the selection sends an input request to the tagging module 112 for providing a customer profile object, hereinafter referred to as the customer profile, of the selected target customer. In an implementation, the user through the user device 106 sends the input request to the tagging module 112 for providing the customer profile of the selected target customer. The tagging module 112 sends a customer profile request to the CRM system 108. In response to the customer profile request the CRM 108 provides the customer profile of the target customer. On receiving the customer profile, the tagging module 112 tags the customer profile to an associated customer profile type, hereinafter referred to as a profile type. The tagging module 112 selects a profile type from amongst a plurality of predefined profile types that are defined and categorized in the system 102 based a plurality of profile parameters. In one embodiment, the profile parameters may include lifetime value of the customer, customer holdings, income bracket, family background, gender, age, customer lifestyle, reasons for satisfaction or dissatisfaction with the organization, a customer wish list, behavioral traits, customer experience and expectations etc,. In one embodiment, the profile parameters may be customized by the user.
[00027] The customer lifetime value, for example, may be defined as a value of the target
customer in monetary terms, and is thus used to determine how much the organization should spend to acquire and retain the target customer. The customer holdings, in one example, may be defined as products and services subscribed by the customer. The income bracket, for example, may define the range in which the income of the target customer lies. The customer lifestyle, in one example, may be defined based on the various expenditures made by the customer, for example, by enquiring about the different consumer appliances owned by the customer. The customer lifestyle may also take into account the economical status of the target customer. For example, determination of the lifestyle of the customer may take into consideration whether the customer is a professional, private employee, government employee, a businessperson, or a retired person. The customer wish list, in one example, may be defined as products and services

that the customer wishes to be provided as supplement to their existing product and services. The reasons for dissatisfaction with the organization may be defined as the reasons because of which the customer may wish to unsubscribe the products and services of the organization. The reasons for dissatisfaction may thus include the reasons for leaving the organization's subscription. For example, if a customer is dissatisfied with subscription charges for a product, then the reason for the customer leaving the organization's subscription may be high subscription charges and provided to system 102 as a part of the customer profile. The reasons for dissatisfaction data may be used by the system 102 to select an appropriate retention policy for the customer.
[00028] Based on the customer profile and the associated profile type of the target
customer the analysis module 110 fetches one or more retention policies associated with the profile type. In one embodiment, the one or more retention policies may be selected from a plurality of predefined retention policies. The retention policies, in one example, may be defined as schemes and programs devised by the organization to be offered to the target customers. The retention policies may include value-additions, rewards or gifts offered to the target customers as a token for the target customer's association with the organization. For example, the retention policies may include rewards for a long time association with the organization, for subscribing to high-end products and services. The retention policies., in one example, may include offering new products at discounted rates, offering holiday packages or gym and sports club memberships, assigning personal agents for customers, etc.
[00029] In one embodiment, separate retention policies are devised for each profile type
using retention policy rules. The retention policy rules, in one implementation are based on the various profile parameters and determine the retention policies that need to be associated with a particular profile type. For example, retention policies for a profile type of customers having high lifetime value, i.e., high monetary value may include more expensive gifts as compared to profile type of customers having low lifetime value. Similarly, the retention policies for a profile type of customers following a healthy lifestyle and having interest in sports may include gym and sports club memberships. However, it will be appreciated by one skilled in the art that retention policy rules may be implicitly included in the retention policies and may be executed automatically when the retention policies are selected. The retention policies may be defined in

the system 102 by a strategy management team comprising experts having experience in the field of marketing and strategy planning.
[00030] In one embodiment, the retention policies for each profile type are classified into
preferred policies and non-preferred policies. The preferred policies are the retention policies having a weightage more than a threshold value and are likely to be accepted by the target customer as a bargain for continuing his subscription. The non-preferred policies are the retention policies having weightage less than a threshold value and are less likely to be accepted-by the target customer as compared to the preferred policies.
[00031] The target customer may be then suggested a retention policy, from the retention
policies, that the target customer is highly probable to accept. Inputs from the target customer, in response to the suggested retention policies are then recorded in the system 102 to update the customer profile of the target customer. For example, if the target customer accepts the retention policy, then the retention policy is updated in the system 102 as an accepted policy whereas if the retention policy is rejected by the target customer then the retention policy is updated as a rejected policy. Further, if the target customer accepts a retention policy, the interaction is termed as a successful interaction, otherwise the interaction is termed as a failed interaction. The system 102 may then update the target customer's profile accordingly.
[00032] Based on the update, the analysis module 110 updates the weightage of the
retention policies in the retention policy data. Further, information, such as behavioral traits of the target customer, observed in response to the retention policies offered to him may be recorded in the system 102, The system 102 updates the customer profile of the target customer and the updated customer profile is sent to the CRM system 108 by the tagging module 112.
[00033] Fig. 2 illustrates exemplary components of the system 102, according to an
embodiment of the present subject matter. The system 102 includes one or more processors) 202. interface(s) 204 and a memory 206. The processor(s) 202 can be a single or multiple processing units. The processor(s) 202 can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, or any devices that manipulate signals based on operational

instructions. Among other capabilities, the processor(s) 202 are configured to fetch and execute computer-readable instructions and data stored in the memory 206.
[00034] The interfaces 204 may include a variety of software and hardware interfaces, for
example, interface for peripheral device(s) such as a keyboard, a mouse, an external memory, a printer, etc. Further, the interfaces 204 may enable the system 102 to communicate with other computing devices, such as web servers and external databases. The interfaces 204 may facilitate multiple communications within a wide variety of protocols and networks, such as the network 104, including wired networks, e.g.. LAN, cable, etc., and wireless networks, e.g., WLAN, cellular, satellite, etc. For the purpose, the interfaces 204 may include one or more ports for connecting to a number of computing devices, such as the user device(s) 106.
[00035] The memory 206 can be implemented using any computer-readable medium
known in the art including, for example, volatile memory (e.g., RAM) and/or non-volatile memory (e.g., flash, etc.). The memory 206 includes program module(s) 208, and program data 210, The program module 208 includes routines, programs, objects, components, data structure, etc., that perform particular task or implement particular abstract data types. In one implementation, the program module 208 includes the analysis module 110, the tagging module 112, an interaction module 212, and other module(s) 214. Other module(s) 214 includes programs that supplement applications implemented by the system 102.
[00036] The program data 210 includes a profile data 216, an analysis data 218, a
retention policy data 220, and other data 222. The other data 222 includes data that is generated as a result of the execution of one or more modules in the other modules 216.
[00037] As mentioned previously, the analysis module 110 generates the target customer
list, based either on a cancellation request received from target customers for cancelling the subscription or on the retention probability. The analysis module 110 saves the target customer list in the profile data 216. The target customer list, as mentioned previously, provides the names or account numbers of all the target customers who intend to or are highly likely to cancel their subscription. In one implementation, the user through the user device 106 may request the system 102 for accessing the target customer list. In another implementation, the system 102 may

automatically prompt the user device 106 with the target customer list on the happening of a particular event, say when the user device 106 logs onto the system 102.
[00038] The tagging module 112 accesses the profile data 216 and transmits the target
customer list to the user device 106. The user accessing the user device 106 may select a target customer from the target customer list and send an input request through the user device 106 to the tagging module 112 for providing the customer profile of the target customer. The tagging moduJe 112, on receiving the input request, sends the customer profile request to the CRM system 108. In one implementation, the customer profile may include information such as the lifetime value of the customer, the customer holdings, the income bracket, the family background, gender, age, the customer lifestyle, customer interactions, customer experience, behavioral aspects of the customer, reasons for dissatisfaction with the organization, etc. On receiving the customer profile, the tagging module 112 evaluates the customer profile to ascertain an associated profile type of the target customer. As explained above, the profile type is selected from a plurality of profile types that are categorized based on various profile parameters. Based on the evaluation, the tagging module 112 tags the customer profile to the associated profile type and saves the customer profile and the associated profile type in the profile data 216.
J00039] The analysis module 110, based on the profile data 216, fetches one or more
retention policies associated with the associated profile type from the plurality of retention policies saved in the retention policy data 220. In one embodiment, the retention policies for each profile type may be classified into preferred policies and non-preferred policies. As explained in the description of figure 1, the preferred policies are the retention policies having a weightage more than a threshold value, whereas the non-preferred policies are the retention policies having a weightage less than the threshold value. The weightage of each retention policy for each profile type is calculated by the analysis module 110 based on the number of times a retention policy has been accepted by the target customers. Further, the retention policies may be tested with few customers before being included in the retention policy data.

[00040] In one implementation, the weightage (w) is calculated by the analysis module
110 using the following equation:

[00041] Where, n is the total number of times the retention policy has been suggested to
different target customers of the same profile type in a given time period and score is a rating given by the system 102 each time the retention policy has been suggested in the given time period. The system 102 gives a rating to the retention policy based on whether the retention policy has been accepted or rejected by the target customer. For example, the system may give a rating of '+T when the retention policy has been accepted or a rating of '0' when the retention policy has been rejected. In one embodiment, the system 102 may also monitor the target customer for a cooling period, i.e., a time period during which the target customer is highly probable to cancel his subscription despite of having accepted the retention policy. If the target customer cancels the subscription during the cooling period, the rating is reduced. Thus, in the above example, if the target customer cancels the subscription during the cooling period, the rating is reduced to '+1'.
[00042] The analysis module 110 updates the weightage of the retention policy for a
particular profile type of the target customer by updating the value of n and the score, every time the retention policy is suggested to a target customer of a particular profile type. For example, if at a given time a particular retention policy has been suggested 10 times, i.e., n = 10 and it has been accepted 7 times, and no target customer has cancelled the subscription during the cooling period, then the score = J 4 and weightage (w) of the particular retention policy is 70%.
[00043] Next time the particular retention policy is suggested to a target customer, the
value of n is updated to 11 and if the target customer accepts the policy the score is updated to 16 and the weighatge is increased to 72.72%. However, if the retention policy is rejected by the target customer, the score is updated to 6 and the weighatge is decreased to 63.63%. Further, if the threshold value in the said example is 40%, then at present the particular retention policy will be in the list of preferred policies for the particular profile type. However, when the weightage is

reduced below 40%, the particular retention policy will be moved in the list of non-preferred policies for the particular profile type.
[00044] The analysis module 110 further provides the retention policies associated with
the associated profile type to the user device 106 in the form of a report displaying the retention policies along with their corresponding weightage. Further, the preferred and non-preferred retention policies may be displayed as separate columns, making it easy and simple for the user accessing the user device 106 to identify the preferred policies. In one embodiment, the analysis module 110 may analyze the customer profile to obtain the analysis data 218 such as customer experience during previous interactions with the users, reasons for customer's dissatisfaction with the products or services of the organization, behavioral trait of the target customer, etc. The analysis data 218 may be provided to the user device 106 in the form of reports, charts, and graphs generated by the analysis module 110. The analysis data 218 may include various reports, such as a customer satisfaction report, a performance monitoring report, a customer feedback report, and a retention policy success report etc.
[00045] The customer satisfaction report, for example, may include reasons for a
customer's dissatisfaction with the products or services of the organization and a customer's experience during previous interactions with the users. The customer satisfaction report may thus be used by the system 102 or the strategy management team to devise new retention policies or improve the existing retention policies. The performance monitoring report, in one example, may include a list of successful interactions between the users and the customers. The performance monitoring report may further provide a comparison of the number of successful interactions against a target number of successful interactions that an user or a team of users using the system 102 are required to achieve in a given time period, say one month. The users may thus use the performance monitoring report to track the number of successful interactions that need to be achieved for the given time period. The retention policy success report, for example, may include a record of retention policies having highest and lowest weightage for each profile type. The retention policy success report may also include a percentage of successful interactions against total interactions that have taken place between the users and the customers. Thus, the retention policy success report may be used to monitor the effectiveness of a retention program and the retention policies. The system 102 thus allows the organization to derive and propose

personalized retention policies based on the reports and success rates of the retention policies with similar customers.
[00046] Based on the analysis data 218, a retention policy that the target customer is
highly probable to accept may be selected and provided to the target customer. In one implementation, the user accessing the user device 106 may use the analysis data 218 and the customer profile to select the retention policy. Further, an interaction facility provided by the user through the user device 106 may use an interaction facility provided by the interaction module 212 to interact with experts, i.e.. the users who may be expert in dealing with customers having the same profile type as the target customer. In one embodiment, the system 102 may identify experts based on past experience and success rates. For example, the user may seek opinion of an expert in dealing with target customers of a certain profile type prior to suggesting a retention policy to a particular target customer having a similar profile type. In one embodiment, the user may alternately access interactions and unstructured knowledge saved in a knowledge database by the expert for similar customers.
[00047] If the target customer accepts the retention policy, then the retention policy is
updated in the system 102. For example, if the target customer accepts the retention policy then the user through the user device 106 updates the system 102 using the interaction module 212. As explained in the description of Fig. 1. the retention policy accepted by the target customer is updated as accepted policy and any retention policy rejected by the target customer is updated as rejected policy. Based on the update the analysis module 110 updates the weightage of the retention policies in the retention policy data 220. Further, information, such as behavioral traits of the target customer, observed in response to the retention policies offered to him may be recorded in the system 102. In one implementation, the user through the user device 106 may interact with the interaction module 212 to provide information, such as behavioral traits of the target customer, observed during interaction with the target customer. The interaction module 212 uses the information provided by the user device 106 to update the customer profile of the target customer in the profile data 216. The updated customer profile is sent to the CRM system 108 by the tagging module 112.

Exemplary Methods
[00048] Fig. 3 illustrates an exemplary method 300 for customer retention, according to an embodiment of the present subject matter. The exemplary method 300 may be described in the general context of computer executable instructions. The method 300 may be a computer implementable method. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, functions, and the like that perform particular functions or implement particular abstract data types. The method may also be practiced in a distributed computing environment where functions are performed by remote processing devices that are linked through a communication network. In a distributed computing environment, computer executable instructions may be located in both local and remote computer storage media, including memory storage devices.
[00049] The order in which the method is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method, or an alternate method. Additionally, individual blocks may be deleted from the method without departing from the spirit and scope of the subject matter described herein. Furthermore, the method can be implemented in any suitable hardware, software, firmware, or combination thereof.
[00050] In accordance with one embodiment of the present subject matter, the method 300 may be implemented in the previously described system 102. However, it will be appreciated by one skilled in the art that such an implementation is not limiting. The method 300 may be implemented in a variety of systems such as customer relationship management, customer retention systems and the like.
[00051] At block 302, a customer profile request is sent to a customer relationship management (CRM) system, such as the CRM system 108, by a customer retention system, for example, the system 102. In one embodiment, a target customer is selected from the target customer list, and the user device 106 sends an input request to the system 102 for providing a customer profile object of the target customer. The system 102 sends a customer profile request to the CRM system 108 requesting for the customer profile object of the target customer. In one implementation, the customer profile request is sent by the tagging module 112 included in the system 102.

[00052] At block 304, a customer profile object is received based on the customer profile request sent to the CRM system 108. For example, on receiving the customer profile request from the system 102, the CRM system 108 transmits the customer profile object of the target customer. In one implementation, the customer profile object is received by the tagging module 112 included in the system 102.
[00053] At block 306, the customer profile object is tagged to an associated customer profile type. In one embodiment, the tagging module 112 evaluates the customer profile object to ascertain the associated profile type of the target customer. Based on the evaluation, the tagging module 112 tags the customer profile object to the associated profile type and saves the customer profile object and the associated profile type in the profile data 216.
[00054] At block 308, the customer profile object is analyzed to generate an analysis data. In one embodiment, the analysis module 110 analyzes the customer profile object to generate analysis data 218. For example, the analysis data 218 may be based on the feedback provided by the customer based on experience of the customer with the organization of previous occasions, reasons for customer's dissatisfaction with the products or services of the organization, behavioral trait of the target customer, etc.
[00055] At block 310, the analysis data and retention policies are provided to the user device 106. In one embodiment, the analysis module 110 fetches one or more retention policies corresponding to the associated profile type from the retention policy data 220. The analysis module 110 provides the retention policies along with the analysis data 218 to the user device 106. Based on the retention policies and the analysis data 218 an appropriate retention policy, which the target customer is highly probable to accept, may be selected and provided to the target customer. The target customer may then select or reject a retention policy. The retention policy accepted by the target customer is updated in the system 102 as an accepted policy and any retention policy rejected by the target customer is updated as a rejected policy.
[00056] At block 312, updates from the user are received. In one embodiment, the user device 106, through the interaction module 212, updates the system 102 about the accepted policies and the rejected policies. The user device 106 may further update the system 102 about target customer related information, such as behavioral traits of the target customer, observed during

interaction with the target customer. Further, the user device may also update the system 102 about the reasons for dissatisfaction of the customer with the organization's products and services. The reasons for dissatisfaction of the customer or the reasons that lead to the customer's decision of discontinuance of availing the services of the organization may be updated in the customer profile.
[00057] At block 314, a retention policy data and the customer profile object are updated based on the updates received from the user device 106. In one embodiment, the analysis module 110 updates the weightage of the retention policies in the retention policy data 220. Further, the interaction module 212 uses the updates provided by the user device 106 to update the customer profile object of the target customer in the profile data 216. The updated customer profile object is sent to the CRM system 108 by the tagging module 1.12.
[00058] Although embodiments for a customer retention system have been described in language specific to structural features and/or methods, it is to be understood that the invention is not necessarily limited to the specific features or methods described. Rather, the specific features and methods are disclosed as exemplary implementations for the customer retention system.

I/We claim:
1. A computer implementable method for customer retention comprising:
tagging a customer profile object to an associated profile type from among a plurality of predefined profile types, wherein the plurality of profile types is based at least in part on a plurality of profile parameters;
selecting, based on the associated profile type, one or more retention policies corresponding to the associated profile type from a plurality of retention policies; and
updating a weightage of at least one retention policy from among the one or more retention policies based on the selection.
2. The method as claimed in claim 1 comprising applying retention policy rules for selecting the one or more retention policies corresponding to the associated profile type.
3. The method as claimed in claim 1 further comprising:
generating a target customer list; and
providing the customer profile object of a target customer selected from the target customer list.
4. The method as claimed in claim 3, wherein the generating is based at least in part on a retention probability of the target customer.
5. The method as claimed in claim 3, wherein the generating is based at least in part on a cancellation request received from the target customer.
6. The method as claimed in claim 1 further comprising:
sending a customer profile request to a customer relationship management system; and receiving the customer profile object, in response to the customer profile request, from the customer relationship management system.
7. The method as claimed in claim 1, wherein the selecting comprises categorizing the plurality of retention policies into preferred policies and non-preferred policies, based on the associated profile type.
8. The method as claimed in claim 1, wherein the plurality of profile parameters is selected from the group consisting of lifetime value, customer holdings, customer wish list, income bracket, family background, gender, age, customer lifestyle, behavioral traits, customer experience and expectations, satisfaction reasons and dissatisfaction reasons.
9. A system (102) comprising:

a processor (202); and
a memory (206) coupled to the processor (202), the memory (206) comprising,
a tagging module (112), configured to tag a customer profile object to an associated profile type, based on a plurality of profile parameters; and
an analysis module (110) configured to provide a retention policy from amongst a plurality of retention policies based on the associated profile type.
10. The system (102) as claimed in claim 9, wherein the system (102) is communicatively coupled to a customer relationship management system (108) to obtain the customer profile object.
11. The system (102) as claimed in claim 9, further comprising an interaction module (212) configured to update the customer profile object, based on updates received from a user device (106).
12. The system (102) as claimed in claim 11, wherein the tagging module (112) transmits the updated customer profile object to a customer relationship management system (108).
13. The system (102) as claimed in claim 9, further comprising an interaction module (212) configured to provide an interaction facility, wherein the interaction facility facilitates a user to interact with experts.
14. The system (102) as claimed in claim 9, wherein the system (102) is configured to identify experts based on past experience and success rates.
15. The system (102) as claimed in claim 9, wherein the analysis module (110) is further configured to analyze the customer profile object for generating an analysis data (218), and wherein the analysis data (218) is generated in the form of graphs and charts.