Claims: WE CLAIM:

1. A method for determining a predictive model for estimating target output for an enterprise, the method comprising:
receiving, by a predictive model determination system (101), a plurality of entity data (201) from a plurality of data sources (103) associated with an enterprise (102);
determining, by the predictive model determination system (101), a plurality of static variables (203) from the plurality of entity data (201) based on pre-defined metadata;
computing, by the predictive model determination system (101), a plurality of dynamic variables (205), for pre-determined time frames, from the plurality of entity data (201) based on the pre-defined metadata and timing window data;
creating, by the predictive model determination system (101), a data model (207) based on a relationship between the plurality of static variables (203) and the plurality of dynamic variables (205);
modifying, by the predictive model determination system (101), length of the pre-determined time frames of each of the plurality of dynamic variables (205) based on change in historic values of the corresponding plurality of dynamic variables (205), wherein the data model (207) is updated using the plurality of dynamic variables (205) within the modified length of the pre-determined time frames;
determining, by the predictive model determination system (101), one or more predicting variables (209) by analysing the plurality of static variables (203) and the plurality of dynamic variables (205) of the updated data model (207);
forming, by the predictive model determination system (101), a plurality of clusters of predicting variables (209) based on one or more common features of the plurality of predicting variables (209), wherein the data model (207) is reupdated with a clustering schema identified from the one or more common features;
identifying, by the predictive model determination system (101), a plurality of predictive models for each of the plurality of clusters based on the reupdated data model (207); and
determining, by predictive model determination system (101), a predictive model (213) from the plurality of predictive models for each of the plurality of clusters, based on a score assigned to each of the plurality of clusters, for estimating a target output.

2. The method as claimed in claim 1, wherein the plurality of entity data (201) comprises customer related information, organization related information, product related data, campaign related data, Point of Sale (POS) data and transaction information associated with customers.

3. The method as claimed in claim 1, wherein the relationship between the static and dynamic variables is determined based on a pre-stored relationship rule.

4. The method as claimed in claim 1, wherein the plurality of predicting variables (209) are identified using regression technique.

5. The method as claimed in claim 1, wherein the one or more common features of the plurality of predicting variables (209) comprises patterns, attributes and value range.

6. The method as claimed in claim 1, wherein the score to the plurality of clusters is assigned by computing a discrepancy between an observed target outcome and a predicted outcome of the predictive model (213).

7. The method as claimed in claim 1, wherein the predictive model (213) for each of the plurality of clusters is determined based on statistical measurements.

8. The method as claimed in claimed 1, wherein the plurality of clusters is determined using segmentation technique.

9. A predictive model determination system (101) for estimating target output for an enterprise (102), comprising:
a processor (113); and
a memory (111) communicatively coupled to the processor (113), wherein the memory (111) stores processor instructions, which, on execution, causes the processor (113) to:
receive a plurality of entity data (201) from a plurality of data sources (103) associated with an enterprise (102);
determine a plurality of static variables (203) from the plurality of entity data (201) based on pre-defined metadata;
compute a plurality of dynamic variables (205), for pre-determined time frames, from the plurality of entity data (201) based on the pre-defined metadata and timing window data;
create a data model (207) based on a relationship between the plurality of static variables (203) and the plurality of dynamic variables (205);
modify length of the pre-determined time frames of each of the plurality of dynamic variables (205) based on change in historic values of the corresponding plurality of dynamic variables (205), wherein the data model (207) is updated using the plurality of dynamic variables (205) within the modified length of the pre-determined time frames;
determine one or more predicting variables (209) by analysing the plurality of static variables (203) and the plurality of dynamic variables (205) of the updated data model (207);
form a plurality of clusters of predicting variables (209) based on one or more common features of the plurality of predicting variables (209), wherein the data model (207) is reupdated with a clustering schema identified from the one or more common features;
identify a plurality of predictive models for each of the plurality of clusters based on the reupdated data model (207); and
determine a predictive model (213) from the plurality of predictive models for each of the plurality of clusters, based on a score assigned to each of the plurality of clusters, for estimating a target output.

10. The predictive model determination system (101) as claimed in claim 9, wherein the plurality of entity data (201) comprises customer related information, organization related information, product related data, campaign related data, Point of Sale (POS) data and transaction information associated with customers.

11. The predictive model determination system (101) as claimed in claim 9, wherein the processor (113) determines the relationship between the static and dynamic variables based on pre-stored relationship rule.

12. The predictive model determination system (101) as claimed in claim 9, wherein the processor (113) identifies the plurality of predicting variables (209) using regression technique.

13. The predictive model determination system (101) as claimed in claim 9, wherein the one or more common features of the plurality of predicting variables (209) comprises patterns, attributes and value range.

14. The predictive model determination system (101) as claimed in claim 9, wherein the processor (113) assigns the score to the plurality of clusters by computing a discrepancy between an observed target outcome and a predicted outcome of the predictive model (213).

15. The predictive model determination system (101) as claimed in claim 9, wherein the processor (113) determines the predictive model (213) for each of the plurality of clusters based on statistical measurements.

16. The predictive model determination system (101) as claimed in claimed 9, wherein the processor (113) determines the plurality of clusters using segmentation technique.