Customer-Based Targeting for Promotional Offer Precision

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Solution Overview

Problem

Conventional promotional targeting methods are inefficient, leading to wasted offers and customer annoyance due to lack of precision and inability to effectively control the number of promotional messages, often resulting in disparities in offer distribution and misrepresentation of customer purchasing behavior.

Innovation Solution

A Customer-Based targeting approach that selects products for each customer based on individual purchasing history and preferences, using statistical methods and Bayes techniques to optimize offer distribution and reduce variance, ensuring that only the most appealing offers are delivered, thereby increasing acceptance rates and reducing costs.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If conventional promotional methods are used to distribute offers to customers, then the number of offers distributed is high, but the acceptance rate is low and customer annoyance increases

Engineering Contradiction:

Improveoffer distribution efficiencyVSAvoidcustomer acceptance rate

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The patent segments the customer base into distinct groups based on purchasing behavior, demographics, and engagement metrics. By dividing the homogeneous promotional approach into heterogeneous targeted segments, the system delivers relevant offers to specific customer groups, thereby increasing acceptance rates while reducing overall distribution volume.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent applies local quality by customizing promotional content, timing, and channel based on individual customer characteristics and preferences. Each customer receives tailored offers aligned with their specific interests and behavior patterns, improving relevance and acceptance while reducing wasted promotions.

Inventive Principle:

Principle #3Local quality

2Quantity of substance

If the number of promotional offers distributed is increased to reach more customers, then coverage is improved, but customer annoyance and resentment increase

Engineering Contradiction:

Improvenumber of offers distributedVSAvoidcustomer annoyance

Core Design Contradiction:

Quantity of substanceVSObject-affected harmful factors

Solution Approach 1:

The patent implements partial action by selectively distributing offers only to customers with demonstrated interest or likelihood of acceptance, rather than universally distributing to all customers. This targeted approach maintains adequate coverage of potential responders while avoiding excessive exposure to uninterested customers, thereby reducing annoyance.

Inventive Principle:

Principle #16Partial or excessive action

3Measurement precision

If promotional offers are targeted based on past purchase history, then targeting precision is improved, but disparities in offer distribution among customers increase

Engineering Contradiction:

Improvetargeting precisionVSAvoidoffer distribution equity

Core Design Contradiction:

Measurement precisionVSQuantity of substance

Solution Approach 1:

The patent incorporates feedback mechanisms that continuously monitor customer responses, acceptance patterns, and engagement metrics. This feedback loop allows the system to adjust targeting parameters and offer distribution strategies to maintain precision while ensuring equitable treatment across customer segments, preventing extreme disparities.

Inventive Principle:

Principle #23Feedback

4Device complexity

If conventional targeting methods are used, then offer distribution is simplified, but the ability to control the number of offers per customer is limited

Engineering Contradiction:

Improvetargeting system complexityVSAvoidoffer distribution control

Core Design Contradiction:

Device complexityVSAdaptability or versatility

Solution Approach 1:

The patent implements dynamic control mechanisms that allow real-time adjustment of offer distribution parameters, including maximum offers per customer, frequency caps, and segment-specific limits. This dynamic approach provides versatile control over offer quantities while maintaining manageable system complexity through automated decision rules and algorithms.

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS8412566B2High-precision customer-based targeting by individual usage statistics

Publication Date: 2013.04.02 YOU TECHNOLOGY LLC

AI summary

A system for distributing limited numbers of promotional offers to individual customers, the promotional offers being targeted to customers based on the customers' individual probabilities of accepting the offers in such a way that each customer can receive a limited number of offers that are estimated to be most likely to be acceptable by the customer. Customer-Based targeting analyzes each customer's past purchasing behavior relative to a master list of promotional offers made available to all customers. From that master list Customer-Based targeting selects a preset limit of promotional offers for each individual customer according to the likelihood that, given the opportunity to select any offers of the master list, each customer would prefer those few offers selected specifically for the customer. Various techniques are disclosed for providing an offer acceptance probability profile tailored for individual customers for use in the Customer-Based targeting technique. Product groupings and market segments are taken into account. Empirical Bayes techniques are applied to the estimation of the offer acceptance profile, and techniques suitable for handling sparse data are applied. Various marketing strategies are incorporated into the system. A graphical technique is provided for adjusting the offer acceptance profile that enables a user to override a system computation and manually set the relative offer acceptance probabilities for an individual user or class of users.