A computer-implemented method and an apparatus to facilitate building of prediction models from customer Web logs includes receiving a Web log including unstructured data and structured data corresponding to a customer's journey on a Website. The structured data in the Web log is used to generate structured variables and the unstructured data in the Web log is used to generate unstructured variables. The generated structured and unstructured variables are concatenated to form a session string, which serves as a textual representation of the customer's journey on the Website. The session string is subjected to text-based processing to generate a plurality of features. The plurality of features are used to build one or more prediction models for facilitating prediction of at least one response variable corresponding to the customers visiting the Website.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method, comprising: receiving, by a processor, a Web log comprising unstructured data and structured data corresponding to a customer's journey on a Website; generating, by the processor, using the Web log: a plurality of unstructured variables from the unstructured data, and a plurality of structured variables from the structured data; generating, by the processor, a session string by concatenating the plurality of unstructured variables and the plurality of structured variables, the session string configuring a textual representation of the customer's journey on the Website; performing, by the processor, a text-based processing of the session string to generate a plurality of features; building, by the processor, at least one prediction model using the plurality of features; and using, by the processor, the at least one prediction model facilitate prediction of at least one response variable. 2. The method of claim 1, wherein the structured data corresponding to the customer's journey on the Website comprises information related to at least one of a type of device used by the customer for accessing the Website, an operating system associated with the device, a type of Web browser used for accessing the Website, and a time-stamp associated with the Website access. 3. The method of claim 1, further comprising: causing, by the processor, capture of complete Uniform Resource Locators (URLs) of Web pages visited by the customer during the customer's journey on the Website, the URLs of the Web pages comprising at least a part of the unstructured data. 4. The method of claim 3, further comprising: parsing the URLs, by the processor, to obtain tokens corresponding to at least one of domain names, hierarchy of journey, destination pages, and query strings, wherein each token from among the tokens obtained by parsing the URLs comprises an unstructured variable from among the plurality of unstructured variables. 5. The method of claim 1, wherein the unstructured variables generated from the unstructured data comprises at least one unstructured variable from among variables related to a time spent on each Web page, a sequence of Web page visits, and a search query entered by the customer during the customer's journey on the Website. 6. The method of claim 1, wherein predicting a response variable from among the at least one response variable comprises predicting one of an intention of the customer, a persona of the customer, a sentiment of the customer, a likelihood of the customer to call, an outcome of a chat offer to the customer, a net promoter score (NPS), a customer satisfaction score (CSAT), and a net experience score (NES) for the customer. 7. The method of claim 1, wherein predicting a response variable from among the at least one response variable comprises predicting one of a sale outcome and a no-sale outcome. 8. The method of claim 1, further comprising: arranging textual forms of the plurality of structured variables and the plurality of unstructured variables serially to configure a freeform text string, wherein each variable is separated from a subsequent variable in the freeform text string by at least one of a separator and a buffer string to generate the concatenated session string. 9. The method of claim 1, further comprising: performing at least one of text tokenization, text normalization, and text vectorization on elements of the session string to effect text-based processing of the session string to generate the plurality of features. 10. The method of claim 1, further comprising: training a prediction classifier using the plurality of the features to build the at least one prediction model for facilitating prediction of the at least one response variable. 11. The method of claim 1, further comprising: refining the built at least one prediction model based on at least one of active learning and incremental learning. 12. The method of claim 1, wherein the customer's journey on the Website comprises one or more visits to the Website. 13. An apparatus, comprising: at least one processor; and a memory having stored therein machine executable instructions, that when executed by the at least one processor, cause the apparatus to: receive a Web log comprising unstructured data and structured data corresponding to a customer's journey on a Website; generate using the Web log: a plurality of unstructured variables from the unstructured data, and a plurality of structured variables from the structured data; generate a session string by concatenating the plurality of unstructured variables and the plurality of structured variables, the session string configuring a textual representation of the customer's journey on the Website; perform a text-based processing of the session string to generate a plurality of features; build at least one prediction model using the plurality of features; and use the at least one prediction model to facilitate prediction of at least one response variable. 14. The apparatus of claim 13, wherein the apparatus is further configured to: cause capture of complete Uniform Resource Locators (URLs) of Web pages visited by the customer during the customer's journey on the Website, the URLs of the Web pages comprising at least a part of the unstructured data. 15. The apparatus of claim 14, wherein the apparatus is further caused to: parse the URLs to obtain tokens corresponding to at least one of domain names, hierarchy of journey, destination pages and query strings, wherein each token from among the tokens obtained by parsing the URLs comprises an unstructured variable from among the plurality of unstructured variables. 16. The apparatus of claim 13, wherein predicting a response variable from among the at least one response variable comprises predicting one of an intention of the customer, a persona of the customer, a sentiment of the customer, a likelihood of the customer to call, an outcome of a chat offer to the customer, a net promoter score (NPS), a customer satisfaction score (CSAT) and a net experience score (NES) for the customer. 17. The apparatus of claim 13, wherein the apparatus is further caused to: arrange textual forms of the plurality of structured variables and the plurality of unstructured variables serially to configure a freeform text string, wherein each variable is separated from a subsequent variable in the freeform text string by at least one of a separator and a buffer string to generate the concatenated session string. 18. The apparatus of claim 13, wherein the apparatus is further caused to: perform at least one of text tokenization, text normalization, and text vectorization on elements of the session string for performing text-based processing of the session string to generate the plurality of features. 19. The apparatus of claim 13, wherein the apparatus is further caused to: train a prediction classifier using the plurality of the features to build the at least one prediction model for facilitating prediction of the at least one response variable. 20. A computer-implemented method comprising: receiving, by a processor, unstructured data and structured data corresponding to a customer's journey on one or more enterprise interaction channels associated with an enterprise, wherein an enterprise interaction channel from among the one or more enterprise interaction channels corresponds to an enterprise Website; generating, by the processor: a plurality of unstructured variables from the unstructured data, and a plurality of structured variables from the structured data; generating, by the processor, a session string by concatenating the plurality of unstructured variables and the plurality of structured variables, the session string configuring a textual representation of the customer's journey on the one or more enterprise interaction channels; performing, by the processor, a text-based processing of the session string to generate a plurality of features; building, by the processor, at least one prediction model using the plurality of features; and using, by the processor, the at least one prediction model to facilitate prediction of at least one response variable. 21. The method of claim 20, further comprising: causing, by the processor, capture of complete Uniform Resource Locators (URLs) of Web pages visited by the customer during the customer's journey on the enterprise Website, the URLs of the Web pages comprising at least a part of the unstructured data. 22. The method of claim 21, further comprising: parsing the URLs, by the processor, to obtain tokens corresponding to at least one of domain names, hierarchy of journey, destination pages, and query strings, wherein each token from among the tokens obtained by parsing the URLs comprises an unstructured variable from among the plurality of unstructured variables. 23. The method of claim 20, further comprising: training a prediction classifier using the plurality of the features to build the at least one prediction model for facilitating prediction of the at least one response variable. 24. The method of claim 20, wherein predicting a response variable from among the at least one response variable comprises predicting one of an intention of the customer, a persona of the customer, a sentiment of the customer, a likelihood of the customer to call, an outcome of a chat offer to the customer, a net promoter score (NPS), a customer satisfaction score (CSAT), and a net experience score (NES) for the customer.