Method and system for interpreting customer behavior via customer journey embeddings

Patent metadata

Field	Value
Publication number	US-2022414685-A1
Application number	US-202117359076-A
Country	US
Kind code	A1
Filing date	Jun 25, 2021
Priority date	Jun 25, 2021
Publication date	Dec 29, 2022
Grant date	—

Abstract

A method and a system for generating an interpretable embedding that corresponds to a sequence of events is provided. The method includes: receiving information that corresponds to a sequence of events that respectively correspond to interactions between a customer and an organization; determining, for each respective event, a respective product associated with the organization and a respective channel via which the event has occurred; assigning a respective sentiment to each event; computing a respective weight for each event; aggregating the computed weights with respect to the products and the channels; and using the aggregated weights to generate the interpretable embedding for the customer. The interpretable embedding is then usable for generating targeted offers to the customer, handling complaints, and preventing subsequent complaints.

First claim

What is claimed is:

1 . A method for generating an interpretable embedding that corresponds to a sequence of events, the method being implemented by at least one processor, the method comprising:
receiving, by the at least one processor, information that corresponds to a plurality of events that respectively correspond to interactions between a customer and an organization; determining, by the at least one processor for each respective event from among the plurality of events, a respective product associated with the organization that relates to the respective event and a respective channel associated with the organization via which the respective event has occurred; assigning, by the at least one processor, a respective sentiment to each respective event from among the plurality of events; computing, by the at least one processor, a respective weight for each respective event from among the plurality of events; aggregating, by the at least one processor, the computed respective weights with respect to the determined products and the determined channels; and using, by the at least one processor, the aggregated weights to generate the interpretable embedding for the customer.

2 . The method of claim 1 , further comprising generating a targeted offer to the customer based on the generated interpretable embedding.

3 . The method of claim 1 , wherein when at least one event from among the plurality of events includes a customer complaint, the method further comprises using the generated interpretable embedding to generate a response to the customer complaint.

4 . The method of claim 1 , wherein when at least two events from among the plurality of events include customer complaints, the method further comprises using the generated interpretable embedding to reduce a frequency of subsequent customer complaints.

5 . The method of claim 1 , wherein the assigning of a respective sentiment comprises tagging the respective event as being one from among a positive event, a negative event, and a neutral event.

6 . The method of claim 5 , wherein the assigning of the respective sentiment further comprises extracting at least one keyword from the respective event and comparing the extracted at least one keyword with each of a first list of keywords associated with a positive event, a second list of keywords associated with a negative event, and a third list of keywords associated with a neutral event.

7 . The method of claim 1 , wherein the computing of the respective weight comprises using a term frequency inverse document frequency (tf-idf) algorithm to compute the respective weight.

8 . The method of claim 1 , wherein the computing of the respective weight comprises using a feed-forward artificial neural network architecture that includes a plurality of perceptron layers to compute the respective weight.

9 . The method of claim 8 , wherein the computing of the respective weight further comprises adding at least one hidden layer that uses Rectified Linear Unit (ReLu) that indicates intersections of the determined products and the determined channels to the feed-forward artificial neural network.

10 . A computing apparatus for generating an interpretable embedding that corresponds to a sequence of events, the computing apparatus comprising:
a processor; a memory; and a communication interface coupled to each of the processor and the memory, wherein the processor is configured to:
receive, via the communication interface, information that corresponds to a plurality of events that respectively correspond to interactions between a customer and an organization;
determine, for each respective event from among the plurality of events, a respective product associated with the organization that relates to the respective event and a respective channel associated with the organization via which the respective event has occurred;
assign a respective sentiment to each respective event from among the plurality of events;
compute a respective weight fro each respective event from among the plurality of events;
aggregate the computed respective weights with respect to the determined products and the determined channels; and
use the aggregated weights to generate the interpretable embedding for the customer.

11 . The computing apparatus of claim 10 , wherein the processor is further configured to generate a targeted offer to the customer based on the generated interpretable embedding.

12 . The computing apparatus of claim 10 , wherein when at least one event from among the plurality of events includes a customer complaint, the processor is further configured to use the generated interpretable embedding to generate a response to the customer complaint.

13 . The computing apparatus of claim 10 , wherein when at least two events from among the plurality of events include customer complaints, the processor is further configured to use the generated interpretable embedding to reduce a frequency of subsequent customer complaints.

14 . The computing apparatus of claim 10 , wherein the processor is further configured to assign the respective sentiment by tagging the respective event as being one from among a positive event, a negative event, and a neutral event.

15 . The computing apparatus of claim 14 , wherein the processor is further configured to extract at least one keyword from the respective event and compare the extracted at least one keyword with each of a first list of keywords associated with a positive event, a second list of keywords associated with a negative event, and a third list of keywords associated with a neutral event.

16 . The computing apparatus of claim 10 , wherein the processor is further configured to use a term frequency—inverse document frequency (tf-idf) algorithm to compute the respective weight.

17 . The computing apparatus of claim 10 , wherein the processor is further configured to use a feed-forward artificial neural network architecture that includes a plurality of perceptron layers to compute the respective weight.

18 . The computing apparatus of claim 17 , wherein the processor is further configured to add at least one hidden layer that uses Rectified Linear Unit (ReLu) that indicates intersections of the determined products and the determined channels to the feed-forward artificial neural network.

19 . A non-transitory computer readable storage medium storing instructions for generating an interpretable embedding that corresponds to a sequence of events, the storage medium comprising executable code which, when executed by a processor, causes the processor to:
receive information that corresponds to a plurality of events that respectively, correspond to interactions between a customer and an organization; determine, fix each respective event from among the plurality of events, a respective product associated with the organization that relates to the respective event and a respective channel associated with the organization via which the respective event has occurred; assign a respective sentiment to each respective event from among the plurality of events; compute a respective weight for each respective event from among the plurality of events; aggregate the computed respective weights with respect to the determined products and the determined channels; and use the aggregated weights to generate the interpretable embedding for the customer.

20 . The storage medium of claim 19 , wherein the executable code is further configured to cause the processor to assign

Assignees

• Jpmorgan Chase Bank Na

Inventors

• Lamba Simran
• Potluru Vamsi Krishna
• Veloso Maria Manuela
• Reddy Prashant P

Classifications

• G06Q30/016

  After-sales · CPC title

• G06Q30/0201 Primary

  Market modelling; Market analysis; Collecting market data · CPC title

• G06N3/088

  Non-supervised learning, e.g. competitive learning · CPC title

• G06F16/313

  Selection or weighting of terms for indexing · CPC title

• G06F40/279

  Recognition of textual entities · CPC title