Campaign Optimization Platform for Targeted Message Delivery

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Solution Overview

Problem

Large retail enterprises face inefficiencies in communicating with customers through messaging campaigns, leading to wasted costs and lower consumer engagement due to ineffective targeting of communications, as existing methods struggle to accurately identify which customers are most interested in specific offers or incentives.

Innovation Solution

A platform utilizing statistical and learning models to optimize campaign delivery by identifying user groups based on past performance, determining relevant offers, and generating redemption scores to allocate messages effectively, thereby maximizing redemption rates and optimizing budget usage.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Quantity of substance

If communications are delivered to a large number of customers in an incentive program, then the coverage and potential reach of the campaign is improved, but the likelihood that individuals will disregard the communication increases and costs are wasted on unredeemed deliveries

Engineering Contradiction:

Improvenumber of customers reachedVSAvoidwasted costs on unredeemed deliveries

Core Design Contradiction:

Quantity of substanceVSLoss of energy

Solution Approach 1:

The patent segments the customer base into distinct groups based on their likelihood to redeem offers. Using machine learning models, customers are scored and divided into high-propensity and low-propensity segments. Communications are then targeted specifically to high-propensity segments, eliminating waste on low-propensity customers while maintaining effective reach.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent changes the parameter of customer selection from broad inclusion to precision targeting based on predicted redemption probability. By adjusting the threshold for inclusion in the campaign based on model-generated scores, the system optimizes the balance between reach and redemption rate, sending communications only to customers above a certain propensity threshold.

Inventive Principle:

Principle #35Parameter changes

2Quantity of substance

If communications are delivered to too many individuals within a given incentive program, then the coverage is improved, but the individual likelihood of engagement decreases and overall efficiency is reduced

Engineering Contradiction:

Improveaudience sizeVSAvoidcampaign efficiency

Core Design Contradiction:

Quantity of substanceVSProductivity

Solution Approach 1:

The patent performs preliminary action by predicting customer redemption propensity before the campaign launches. Machine learning models analyze historical data and customer characteristics to pre-score each potential recipient. This preliminary assessment allows the system to pre-select the optimal audience size and composition, ensuring high efficiency from the start rather than adjusting during or after the campaign.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system enables self-service by allowing the optimization platform to automatically determine the optimal audience segmentation and communication strategy without manual intervention. The machine learning models autonomously analyze data, identify patterns, and generate targeting recommendations, making the efficiency optimization a self-executing process.

Inventive Principle:

Principle #25Self-service

3Ease of operation

If traditional messaging methods are used without precise targeting, then the simplicity of implementation is maintained, but wasted effort and costs increase while consumer engagement decreases

Engineering Contradiction:

Improvemessaging implementation simplicityVSAvoidwasted effort and costs

Core Design Contradiction:

Ease of operationVSLoss of energy

Solution Approach 1:

The patent introduces an intermediary optimization platform that sits between the campaign management system and the customer database. This intermediary automatically performs the complex machine learning analysis and customer segmentation, translating simple campaign parameters into precise targeting lists. The intermediary handles the complexity internally while presenting a simple interface to users.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces manual customer selection and messaging strategy development with automated machine learning systems. Instead of manual analysis of customer data and iterative testing of targeting strategies, the system uses algorithms to automatically identify patterns and optimize messaging delivery, substituting mechanical human processes with computational automation.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20240362676A1Method and system for optimization of campaign delivery to identified user groups

Publication Date: 2024.10.31 TARGET BRANDS INC

AI summary

Methods and systems for optimizing campaign delivery of messages, such as offers or incentives, are provided. A set of statistical and learning models identify similar campaigns, and generate recommendations for the current campaign based on past performance as measured by engagement with and performance of identified previous campaigns. An optimization tool may be used in conjunction with an offer distribution platform that identifies individual user groups, and develops recommended offers to be included within the campaign for use with specific users or user groups to achieve optimized results within provided campaign objectives.