AUTO-SEGMENTATION
Systems and methods are disclosed herein for automatically identifying segments of customers based on customers having similar characteristics and behaviors. In one embodiment of the invention, event-level records representing customer interactions for multiple customers are received and the event-level records are summarized to combine attributes for respective customers into customer-level records. The customer-level records include attributes for customer characteristics and behaviors based on summarizing the event-level records. Systems and methods further cluster the customer-level records based on the attributes for customer characteristics and behaviors and, based on the clustering, identify segments of clusters having a statistically significant value relative to other clusters. The systems and methods display the identified segments on a user-interface.
1 . In an environment in which customer interactions are tracked, a method for automatically identifying segments of customers based on customers having similar characteristics and behaviors, the method comprising:
a computing device receiving event-level records containing attributes of customer interactions for multiple customers;
the computing device summarizing the event-level records to combine interaction events for respective customers into customer-level records, the customer-level records including attributes for customer characteristics and behaviors based on summarizing the event-level records;
the computing device clustering customer-level records based on the attributes for customer characteristics and behaviors; and
based on the clustering, the computing device identifying segments of clusters having a statistically significant value relative to other clusters.
2 . The method as set forth in claim 1 further comprising reducing the number of attributes for customer characteristics and behaviors from the customer-level records that the clustering considers by statistically assessing distributions of the attributes for customer characteristics and behaviors.
3 . The method as set forth in claim 1 , wherein the attributes for customer characteristics and behaviors include behavioral metrics.
4 . The method as set forth in claim 3 , wherein the behavior metrics include a page view metric, a visits metric, a purchases metric, a last visit date, a last purchase date, a last purchase amount metric, a first visit date, a total revenue metric, or an average time per visit metric.
5 . The method as set forth in claim 1 , wherein the attributes for customer characteristics and behaviors include dimensions.
6 . The method as set forth in claim 5 , wherein the dimensions identify a browser, keyword, or page name used by the respective customers.
7 . The method as set forth in claim 5 , wherein the dimensions identify a geography, location, marketing campaign, or referrer associated with the respective customers.
8 . The method as set forth in claim 1 , wherein the clustering includes at least one of expectation-maximization, hierarchical clustering, and a K-Means algorithmic clustering.
9 . The method as set forth in claim 1 further comprising representing results of the segmenting step on a user-interface.
10 . The method as set forth in claim 1 further comprising:
identifying the most distinguishing attributes for customer characteristics and behaviors segments of the segments; and
presenting segment-specific information on a user-interface, the segment specific information identifying the most distinguishing attributes for customer characteristics and behaviors segments of the segments.
11 . The method as set forth in claim 1 , wherein the attributes for customer characteristics and behaviors further comprise a sequence of attributes occurring over time where the identifying segments of clusters step identifies a cluster based on the sequence of attributes regardless of the time over which the attributes occurred.
12 . In an environment in which customer interactions with a business are tracked, a method for automatically segmenting customers having similar characteristics and behaviors, the method comprising:
a computing device combining event-level records representing customer interactions for multiple customers into customer-level records, the customer-level records including attributes for customer characteristics and behaviors;
the computing device clustering customer-level records based on the attributes for customer characteristics and behaviors;
based on the clustering, the computing device identifying segments with statistically significant distinguishing segments of attributes for customer characteristics and behaviors relative to other segments; and
presenting segment-specific information on a user-interface, the segment specific information representing selected statistically significant distinguishing segments of attributes for customer characteristics and behaviors.
13 . The method as set forth in claim 12 , wherein the attributes for customer characteristics and behaviors further comprise a sequence of attributes occurring over time where the identifying segments step identifies a cluster based on the sequence of attributes regardless of the time over which the attributes were recorded.
14 . The method as set forth in claim 12 further comprising feature selecting out certain attributes having statistically insignificant variability.
15 . The method as set forth in claim 12 further comprising feature selecting out certain attributes having statistically insignificant amounts of data.
16 . The method as set forth in claim 12 , wherein the attributes for customer characteristics and behaviors include behavioral metrics.
17 . The method as set forth in claim 12 , wherein the attributes for customer characteristics and behaviors include dimensions.
18 . A system for automatically segmenting customers having significantly differing characteristics and behaviors from a database of tracked event-level records, the system comprising:
a computing device including a processor for executing computer readable instructions; and
a non-transient storage device in communication with the processor, where the storage device contains non-transient instructions which, upon execution, cause the processor to:
summarize event-level records to combine attributes for respective customers into customer-level records, where the customer-level records include attributes for customer characteristics and behaviors based on summarizing the event-level records;
cluster the customer-level records based on the attributes for customer characteristics and behaviors; and
based on the clustering, identify a segment of clusters having a statistically significant value for certain attributes of customer characteristics and behaviors relative to other clusters.
19 . The system as set forth in claim 18 , wherein the non-transient instructions, upon execution, cause the processor to display the segment of clusters having a statistically significant value for certain attributes of customer characteristics and behaviors relative to other clusters on a user-interface.
20 . The system as set forth in claim 18 , wherein the non-transient instructions, upon execution, cause the processor further to reduce the number of attributes for customer characteristics and behaviors from the customer-level records by statistically assessing distributions of the attributes for customer characteristics and behaviors.