FIELD

The present invention relates to a system and methods of tracking data within an e-commerce platform.

BACKGROUND

Some online stores employ a user ID and password authentication, which are entered by the customer when accessing an on-line store or platform. In such a store, all transactions by the same customer are easily grouped together by user ID. For example, when a user logs onto Amazon™, the customer enters a user ID and password and the system maintains a history of activity for the customer.

However, some online stores do not employ the concept of a user ID and password; instead, each transaction is a standalone event.

Alternatively, in an e-commerce platform that provides online stores for multiple merchants, while there may be respective user ID and password for one or more of the stores, there is nothing linking the user ID and password for one store to that of another store.

SUMMARY

The present invention relates to a system and method of analyzing data within a large data system such as an ecommerce platform in order to determine specific customers and provide the identified customers with a unique identifier (or “Customer ID”). The system and method may account for various factors that would slow down a data analysis system (such as non-standardized data formatting) or potentially lead to inaccurate data analytic conclusions (such as false information).

According to one aspect of the present invention, there is provided a computer implemented method of generating customer identifiers (IDs) method comprising: collecting evidence associated with a plurality of customer interactions with an e-commerce system; filtering the evidence to remove evidence to be excluded from data correlation, the filtering being configured to exclude evidence that is determined to be unreliable according to a filtering criteria or method; performing correlation on the evidence remaining after filtering, and associating each set of correlated evidence with respective unique customer ID; outputting each customer ID, and the correlated evidence associated with the customer ID.

In some embodiments, the method further comprises: performing evidence standardization before filtering the evidence.

In some embodiments, the method further comprises: receiving an input identifying specific evidence associated with the customers to be excluded; removing the customer ID(s) associated with the specific evidence.

In some embodiments, filtering the evidence comprises: for at least one type of evidence: counting instances of each specific value of the type of evidence; where a number of instances of a specific value of the type of evidence is greater than a threshold, removing that specific value such that it does not remain after filtering.

In some embodiments, the method further comprises: configuring a respective threshold for each of at least two types of evidence.

In some embodiments, filtering the evidence comprises performing a data centrality analysis.

In some embodiments, filtering the evidence comprises removing any evidence matching an entry on a blacklist and/or keeping any evidence matching an entry on a whitelist.

In some embodiments, the method further comprises: receiving an input configuring the filtering.

In some embodiments, the input configures the filtering to perform a selected one of: Threshold based evidence filtering; Centrality analysis based evidence filtering; Blacklist based evidence filtering; Whitelist based evidence filtering.

In some embodiments, collecting evidence comprises collecting evidence associated with transactions with a single merchant through one or multiple channels.

In some embodiments, collecting evidence comprises collecting evidence associated with transactions with any one of a plurality of merchants through one or multiple channels.

According to another aspect of the present invention, a customer identifier (ID) generator system comprising: a customer ID generator configured to generate customer IDs based on customers interactions with an e-commerce system, the customer ID generator configured to: collect evidence associated with a plurality of customer interactions with the e-commerce system; filter the evidence to remove evidence to be excluded from data correlation, the filtering being configured to exclude evidence that is determined to be unreliable according to a filtering criteria or method; perform correlation on the evidence remaining after filtering, and associating each set of correlated evidence with respective unique customer ID; output each customer ID, and the correlated evidence associated with the customer ID.

In some embodiments, the customer ID generator system is further configured to detect customer interactions with the e-commerce system.

In some embodiments, the customer ID generator is further configured to: perform evidence standardization before filtering the evidence.

In some embodiments, the customer ID generator is further configured to: receive an input identifying specific evidence associated with the customers to be excluded; remove the customer ID(s) associated with the specific evidence.

In some embodiments, the customer ID generator is further configured to filter the evidence by: for at least one type of evidence: counting instances of each specific value of the type of evidence; where a number of instances of a specific value of the type of evidence is greater than a threshold, removing that specific value such that it does not remain after filtering.

In some embodiments, a respective threshold is configured for each of at least two types of evidence.

In some embodiments, the customer ID generator is further configured to filter the evidence by performing a data centrality analysis.

In some embodiments, the customer ID generator is further configured to filter the evidence by removing any evidence matching an entry on a blacklist and/or keeping any evidence matching an entry on a whitelist.

In some embodiments, the customer ID generator is further configured to receive an input configuring the filtering.

In some embodiments, the input configures the filtering to perform a selected one of: Threshold based evidence filtering; Centrality analysis based evidence filtering; Blacklist based evidence filtering; Whitelist based evidence filtering.

In some embodiments, the system is configured to collect evidence associated with transactions with a single merchant through one or multiple channels.

In some embodiments, the system is configured to collect evidence associated with transactions with any one of a plurality of merchants through one or multiple channels.

