AI Transaction Intent Classification for Cart Abandonment

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Solution Overview

Problem

Conventional e-commerce transaction management techniques fail to effectively determine the reasons for uncompleted transactions, leading to ineffective email reminders and low participation in surveys, which do not address the underlying reasons for cart abandonment.

Innovation Solution

The use of artificial intelligence techniques to analyze user digital behavior during a transaction session, classify user intentions, and identify reasons for cart abandonment, allowing for real-time customized recommendations and offers to be generated and presented to users.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of time

If email reminders are sent to users of uncompleted transactions, then users are contacted about abandoned carts, but the reminders are ineffective because they are sent after users have left the website

Engineering Contradiction:

Improvetime to contact userVSAvoideffectiveness of reminder

Core Design Contradiction:

Loss of timeVSReliability

Solution Approach 1:

The system performs preliminary classification of user intention and identification of abandonment reasons while the user is still on the website, before the user leaves. This allows the system to send targeted communications at the optimal moment when the user is most likely to respond, rather than waiting until after the session ends.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements a feedback loop where user behavior during the session is continuously analyzed and classified, and this information is used to dynamically adjust and send personalized communications. The system learns from user responses and refines its classification model over time.

Inventive Principle:

Principle #23Feedback

2Adaptability or versatility

If additional product recommendations are provided to users, then more options are available to users, but the recommendations do not correspond to the reasons for abandonment and are provided too late

Engineering Contradiction:

Improvecustomization of recommendationsVSAvoidtiming of recommendation

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The system identifies reasons for abandonment and generates personalized recommendations during the user's active session, before the user leaves the website. This preliminary action ensures recommendations are delivered at the optimal moment when users are still engaged and most likely to convert.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system provides different types of recommendations tailored to specific abandonment reasons identified through AI classification. Instead of generic recommendations, the system customizes the content, timing, and channel based on the local context of each user's behavior and inferred intentions.

Inventive Principle:

Principle #3Local quality

3Loss of information

If surveys are carried out to query users for reasons of abandonment, then direct feedback is obtained, but participation rates are low making surveys ineffective

Engineering Contradiction:

Improveinformation about abandonment reasonsVSAvoidparticipation rate

Core Design Contradiction:

Loss of informationVSProductivity

Solution Approach 1:

The system automatically collects and analyzes abandonment reason information through AI classification of user behavior patterns, eliminating the need for users to manually participate in surveys. The system serves itself by inferring reasons from digital footprints such as session duration, pages visited, and interaction patterns.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system replaces the mechanical survey process with an automated AI-based classification system that analyzes user behavior data. This substitution eliminates the need for user participation while still obtaining comprehensive information about abandonment reasons through objective behavioral analysis.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS11556952B2Determining transaction-related user intentions using artificial intelligence techniques

Publication Date: 2023.01.17 DELL PROD LP

AI summary

Methods, apparatus, and processor-readable storage media for determining transaction-related user intentions using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining data pertaining to digital behavior of a user during a transaction-related session on one or more electronic commerce websites; classifying the user into one of multiple categories by processing the obtained data pertaining to the digital behavior of the user using artificial intelligence techniques, wherein the multiple categories correspond to multiple predicted levels of user intention to complete a transaction; determining, based on the classification of the user and the obtained data pertaining to the digital behavior of the user, at least one reason why the user may not complete a transaction during the transaction-related session; and performing one or more automated actions based at least in part on the at least one determined reason.