CLIAMS:1. A method for predicting Customer Lifetime Value (CLV), the method comprising:
segmenting a dataset into a plurality of segments based on weighted RFM scores associated with data of the dataset, wherein the dataset includes data representative of purchasing behavior of customers over a predefined time period, and wherein data corresponding to customers with similar weighted RFM scores is placed in one segment;
computing a churn value for each segment from amongst the plurality of segments based on thebuying behavior of customers within each of the plurality of segments, wherein the churn value is associated with transaction characteristics associated with customers corresponding to data in each segment; and
predicting the CLV for each customer of each segment based on expected lifetime value of each customer , wherein the CLV is indicative of profitability associated with customers corresponding to each segment, and wherein the profitability is based on association of the customers with an organization.

2. The method as claimed in claim 1 further comprising storing the CLV value for each customer of each segment in a Hbase database.

3. The method as claimed as claim 1, wherein the weighted RFM scores are computed by performing data analysis on the dataset based on Recency Frequency and Margin (RFM) parameters associated with the data corresponding to the customers.

4. The method as claimed in claim 3, wherein the data analysis comprises:
applying data cleansing on the dataset to eliminate at least one of incomplete data and corrupted data; and
analyzing the dataset based on Recency Frequency and Margin (RFM) to generate the weighted RFM scores.

5. The method as claimed in claim 1, wherein the data set is segmented by utilizing distributive processing capability of MapReduce technique.

6. The method as claimed in claim 1, wherein the churn value is computed based on an exponential moving average technique.

7. The method as claimed in claim 1, wherein predicting the CLV comprises:
computing expected lifetime period for each customer of a segment based on the churn value, wherein the expected lifetime period corresponds to a time period for which the customer is expected to perform transactions with the organization;
comparing the expected lifetime period with a predetermined threshold to determine whether the expected lifetime period is one of greater than and less than the predetermined threshold, wherein the predetermined threshold is determined by the organization; and
estimating the CLV based on the expected lifetime period being one of greater than and less than the predetermined threshold.

8. A Data Analysis System (DAS) (104) for predicting Customer Lifetime Value, the DAS (104) comprising:
a processor (202);
a data collection module (212) coupled to the processor (202), wherein the data collection module (212) is to collect data related to transactions conducted by customers;
an analysis module (108) coupled to the processor (202), wherein the analysis module (108) is to:
segment the dataset into a plurality of segments based on weighted RFM scores associated with data of the dataset, wherein the dataset includes data representative of purchasing behavior of customers over a predefined time period, and wherein data corresponding to customers with similar weighted scores is placed in one segment; and
compute a churn value for each segment from amongst the plurality of segments based on the buying behavior of the customers within each segment, wherein the churn value is associated with transaction characteristics associated with customers corresponding to data in each segment; and
a prediction module (216) coupled to the processor (202), wherein the prediction module (216) is to predict the CLV for each segment based on expected lifetime in years of each customer, wherein the CLV is indicative of profitability associated with customers corresponding to each segment, and wherein the profitability is based on association of the customers with an organization.

9. The DAS (104) as claimed in claim 8, further to store the CLV value for each of the customers in a Hbase database.

10. The DAS (104) as claimed in claim 8, wherein the analysis module (108) is to:
apply data cleansing on the dataset to eliminate at least one of incomplete data and corrupted data; and
analyze the dataset based on Recency Frequency and Margin (RFM) to generate the weighted RFM scores.

11. The DAS (104) as claimed in claim 8, wherein the analysis module (108) is to:
segment the dataset into the plurality of segments by utilizing MapReduce technique; and
compute the churn value based on an exponential moving average technique.

12. The DAS (104) as claimed in claim 8, wherein the prediction module (216) is to:
compute expected lifetime period for each customer of a segment based on the churn value, wherein the expected lifetime period corresponds to a time period for which the customer is expected to perform transactions with the organization;
compare the expected lifetime period with a predetermined threshold to determine whether the expected lifetime period is one of greater than and less than the predetermined threshold, wherein the predetermined threshold is determined by the organization; and
estimate the CLV based on the expected lifetime period being one of greater than and less than the predetermined threshold.

13. A non-transitory computer-readable medium comprising instructions for predicting Customer Lifetime Value (CLV) executable by a processor resource to:
segment the customers into a plurality of segments based on weighted RFM scores associated with data of the dataset, wherein the dataset includes data representative of purchasing behavior of customers over a predefined time period, and wherein data corresponding to customers with similar weighted RFM scores is placed in one segment;
compute a churn value for each segment from amongst the plurality of segments based on the buying behavior of the customers within each segment, wherein the churn value is associated with transaction characteristics associated with customers corresponding to data in each segment; and
predict the CLV for each customer based on expected lifetime value of each customer, wherein the CLV is indicative of profitability associated with customers corresponding to each segment, and wherein the profitability is based on association of the customers with an organization.

14. The non-transitory computer-readable medium as claimed in claim 13, wherein the instructions for predicting CLV are further to:
apply data cleansing on the dataset to eliminate at least one of incomplete data and corrupted data; and
analyze the dataset based on Recency Frequency and Margin (RFM) to generate the weighted RFM scores.
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