Customer Receptiveness Metrics for Optimal Financial Offer Timing

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Solution Overview

Problem

Existing targeted marketing systems struggle to determine an optimal time to provide financial offers to customers, often relying on general behavioral characteristics that may not apply to individual customers, and fail to consider timely and relevant data reflecting current conditions.

Innovation Solution

A financial institution computing system analyzes individual customer transactions to determine receptiveness metrics, including time, location, and communication mode, to tailor offers in real-time or near real-time, optimizing the likelihood of customer conversion.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If targeted marketing systems send offers to customers based on general behavioral characteristics, then they can reach a broad audience, but they fail to determine optimal timing for individual customers leading to consumer fatigue and decreased conversion rates

Engineering Contradiction:

Improveconversion rateVSAvoidconsumer fatigue

Core Design Contradiction:

ProductivityVSObject-affected harmful factors

Solution Approach 1:

The system performs preliminary analysis of customer transaction data, location information, and behavioral patterns before sending offers. It predicts optimal timing and receptiveness metrics in advance, allowing offers to be sent at the most appropriate moment for each individual customer, thereby increasing conversion rates and avoiding consumer fatigue from poorly timed offers

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system transitions from general behavioral characteristics applicable to broad audiences to individualized analysis of each customer's specific transaction history, location patterns, and receptiveness metrics. This localized approach tailors offer timing and content to each customer's unique behavior patterns, improving conversion rates while reducing fatigue from irrelevant or mistimed offers

Inventive Principle:

Principle #3Local quality

2Productivity

If marketing systems increase the quantity of offers sent to customers, then they may improve coverage, but they waste resources and exacerbate consumer fatigue

Engineering Contradiction:

Improveoffer conversion efficiencyVSAvoidresource consumption

Core Design Contradiction:

ProductivityVSLoss of energy

Solution Approach 1:

Instead of sending offers to all customers or increasing offer quantity, the system applies partial action by selectively targeting only those customers with high predicted receptiveness metrics at optimal times. This approach concentrates resources on the most promising opportunities, improving conversion efficiency while reducing waste from sending offers to unreceptive customers

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The system changes the parameter of offer timing from arbitrary or batch-based scheduling to dynamically optimized timing based on predicted receptiveness metrics. By adjusting when offers are sent based on real-time analysis of customer behavior parameters, the system maximizes conversion efficiency without increasing resource consumption

Inventive Principle:

Principle #35Parameter changes

3Adaptability or versatility

If marketing systems use general behavioral characteristics for targeting, then they can simplify the process, but they lack real-time relevance and individualization

Engineering Contradiction:

Improvepersonalization capabilityVSAvoiddata analysis complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system performs preliminary analysis of customer transaction data, location information, and behavioral patterns before offers are sent. By pre-processing and storing relevant customer data and computing receptiveness metrics in advance, the system enables real-time personalization without adding complexity to the offer delivery process itself

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system creates a predictive model or copy of customer behavior patterns based on historical transaction data and location information. This behavioral copy allows the system to predict optimal timing and receptiveness for future offers without requiring complex real-time analysis, balancing personalization capability with system simplicity

Inventive Principle:

Principle #26Copying

Data Source

PatentUS12620002B2Systems and methods for facilitating optimal customer engagement via quantitative receptiveness analysis and presentation

Publication Date: 2026.05.05 WELLS FARGO BANK NA

AI summary

A financial institution computing system includes an account database with a plurality of transaction parameters with respect to a financial account of a customer, a receptiveness metrics circuit structured to extract the plurality of transaction parameters from the account database, the transaction parameters including at least one financial transaction record, and indicative of a mode of the customer, and determine one or more receptiveness metrics attributed to the customer based on the mode of the customer, the one or more receptiveness metrics indicating likelihoods of the customer converting an interaction from the financial institution, wherein the interaction includes an avatar that is an aged version of the customer, and an interaction generation circuit structured to transmit the interaction to the customer at an optimal time based on the one or more receptiveness metrics.