Clickstream analysis methods and systems related to determining actionable insights relating to a path to purchase
Methods and systems are provided herein for collecting web browser click events across a plurality of web sites from a data collection agent (DCA), as a click-stream, at a data collection server (DCS) to record and provide user on-line activity, filtering the user online activity to include activity from a time period prior to a sale from the sales transaction data and identifying one or more shopping touch-points based on the filtered user online activity and the sales transaction data. Further, an engagement index, an influence index, and an opportunity index is calculated. A digital touch-points facility may perform the identifying and calculating.
1. A method, comprising:
providing a data collection agent (DCA) that:
records web browser click events including uniform resource locator (URL) data across a plurality of web sites, and
transmits a click-stream of the web browser click events to a data collection server (DCS) that resides on a computer, the DCA embodied as a web browser plug-in on a user client station;
receiving the web browser click events from the DCA, as a clickstream, at the DCS;
recording and providing user online activity by:
reading a list of URL rules,
walking through URL characters of the URL data to determine one or more URL rules of the list of URL rules to apply to the URL data, and
applying the one or more URL rules to the URL data;
surveying a user to collect sales transaction data and preference data, the sales transaction data including offline behavior related to sales and the preference data indicating whether the user prefers visiting an offline store and/or a website to view a product prior to purchase;
filtering the user online activity to include activity from a time period prior to a sale from the sales transaction data, the sale associated with the product;
identifying, using a digital touch-points facility, one or more shopping touch-points based on the filtered user online activity, the sales transaction data, and the preference data;
determining a path to purchase of the user based on the one or more shopping touch-points;
identifying cross-shopping behavior for a competitive product of the product based on the filtered user online activity; and
modifying an online promotional process and an offline promotional process for the product and an offline promotional process for a second product complementary to the product, based on the path to purchase of the user and the cross-shopping behavior.
2. The method of claim 1 , further comprising calculating an engagement index based on a percent of total shoppers interacting with a specific touch-point, and an intensity of those interactions, including time and number of repeat visits as determined from the user online activity, wherein the engagement index represents actual shopper engagement with the specific touch-point.
3. The method of claim 2 , further comprising calculating an influence index, wherein the influence index represents a probability of the specific touch-point interaction influencing a shopper's final action.
4. The method of claim 1 , further comprising calculating an opportunity index, wherein the opportunity index shows a competitive view of specific touch-point interactions.
5. The method of claim 1 , wherein the click events occur on at least one of a computer, a tablet, and a mobile device.
6. The method of claim 1 , wherein the sales transaction data include one or more of a date of transaction, a time of transaction, a method of payment, a type of good(s) or service(s) transacted, and a merchant.
7. A method, comprising:
surveying a user to collect sales transaction data and preference data, the sales transaction data including offline behavior related to sales and the preference data indicating whether the user prefers visiting an offline store and/or a website to view a product prior to purchase;
collecting web browser click events including uniform resource locator (URL) data across a plurality of web sites using a data collection agent (DCA), the DCA embodied as a web browser plug-in on a user client station;
transmitting a click-stream of the web browser click events to a data collection server (DCS) that resides on a computer;
recording and providing user online activity by:
reading a list of URL rules,
walking through URL characters of the URL data to determine one or more URL rules of the list of URL rules to apply to the URL data, and
applying the one or more URL rules to the URL data;
matching the sales transaction data to the user online activity;
filtering the user online activity to include activity from a time period prior to a sale from the sales transaction data, the sale associated with the product;
identifying, using a digital touch-points facility, one or more shopping touch-points based on the filtered user online activity, the sales transaction data, and the preference data;
determining a path to purchase of the user based on the one or more shopping touch-points;
identifying cross-shopping behavior for a competitive product of the product based on the filtered user online activity; and
modifying an online promotional process and an offline promotional process for the product and an offline promotional process for a second product complementary to the product, based on the path to purchase of the user and the cross-shopping behavior.
8. The method of claim 7 , wherein the click events occur on at least one of a computer, a tablet, and a mobile device.
9. The method of claim 7 , wherein the data are matched by at least one of address, name, phone number, email address, and credit card number.
10. The method of claim 7 , further comprising calculating an engagement index based on a percent of total shoppers interacting with a specific touch-point, and an intensity of those interactions, including time and number of repeat visits as determined from the user online activity, wherein the engagement index represents actual shopper engagement with the specific touch-point.
11. The method of claim 10 , further comprising calculating an influence index, wherein the influence index represents the probability of the specific touch-point interaction influencing a shopper's final action.
12. The method of claim 7 , further comprising calculating an opportunity index, wherein the opportunity index shows a competitive view of specific touch-point interactions.
13. The method of claim 7 , further comprising calculating at least one of an influence index, an opportunity index and an engagement index for a combination of touch-points.
14. The method of claim 7 , further comprising calculating at least one of an influence index, an opportunity index and an engagement index for a path representing navigation among at least a plurality of touch-points.
15. The method of claim 7 , wherein the sales transaction data include one or more of a date of transaction, a time of transaction, a method of payment, a type of good(s) or service(s) transacted, and a merchant.
16. The method of claim 1 , further comprising modifying an online promotional process for the complementary second product.
17. The method of claim 1 , wherein modifying the online and the offline promotional processes comprises generating an online dialogue between users and a provider of the product.
18. The method of claim 1 , wherein modifying the online and the offline promotional processes comprises providing site tools that influence the path to purchase of the user at a shopping touch-point, the site tools comprising a research tool.
19. The method of claim 1 , further comprising forecasting a demand for the product.
20. The method of claim 1 , wherein recording and providing user online activity further includes:
sessionizing the URL data for a given user based on timestamps of the URL data,
decomposing the URL data into constituent parts, and
hashing the URL data to produce a unique hash identifier based on the constituent parts.
21. The method of claim 1 , wherein the surveyed sales transaction data indicates whether the user purchased the product from a webpage or an offline store.
22. The method of claim 7 , wherein the surveyed sales transaction data indicates whether the user purchased the product from a webpage or an offline store.
23. The method of claim 1 , wherein the surveyed sales transaction data indicates offline research performed by the user prior to the sale.
24. The method of claim 7 , wherein the surveyed sales transaction data indicates offline research performed by the user prior to the sale.
25. The method of claim 1 , wherein the preference data indicates whether the user prefers to visit one or more websites prior to purchase.
26. The method of claim 7 , wherein the preference data indicates whether the user prefers to visit one or more websites prior to purchase.