Customer Experience Content Effectiveness Scoring

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Solution Overview

Problem

Companies face challenges in determining the effectiveness of customer experience content over time, as new information about customers or products can render existing testimonials or case studies less persuasive, making it difficult for sales teams to select relevant marketing materials.

Innovation Solution

A system that utilizes machine learning models to generate effectiveness scores for customer experience content based on new information, such as news stories and service requests, and adjusts the content's availability in a database by purging, flagging, or maintaining it based on these scores, ensuring that only relevant and positive content is used for marketing purposes.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If customer experience content is maintained in the database without continuous evaluation, then the content is readily available for marketing, but the content may become less effective or inappropriate over time as new information about customers or products emerges

Engineering Contradiction:

Improvecontent availability for marketingVSAvoideffectiveness of customer experience content

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system continuously monitors customer experience content by analyzing new information from multiple sources (news articles, service requests, customer interactions) and uses this feedback to update effectiveness scores. This feedback loop ensures content remains relevant and effective while maintaining availability for marketing purposes.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system performs preliminary evaluation of content effectiveness before it is used for marketing. By continuously assessing content against new customer and product information in advance, the system prevents ineffective content from being deployed while ensuring effective content is ready for use.

Inventive Principle:

Principle #10Preliminary action

2Ease of operation

If salespeople manually select testimonials without automated assistance, then they can exercise judgment on each case, but it becomes challenging to determine which testimonial to provide when facing numerous clients with different characteristics

Engineering Contradiction:

Improvemanual selection processVSAvoidcomplexity of content selection

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The system introduces an intermediary layer between salespeople and the content database. The machine learning model acts as a mediator that automatically filters and ranks content based on client characteristics, reducing the complexity of selection while preserving the salesperson's ability to exercise judgment on nuanced cases.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system enables self-service content selection by automatically determining which testimonials are most relevant to each client based on their characteristics and history. This eliminates the need for manual searching and selection, allowing salespeople to focus on delivery rather than content curation.

Inventive Principle:

Principle #25Self-service

3Reliability

If the system continuously monitors and updates content effectiveness, then content relevance is maintained, but additional processing time and computational resources are required

Engineering Contradiction:

Improverelevance of customer experience contentVSAvoidprocessing time for content evaluation

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system implements periodic monitoring of content effectiveness at key intervals and triggered by significant events (new customer interactions, product updates, major news events). This periodic approach maintains content relevance while reducing continuous processing requirements compared to real-time monitoring.

Inventive Principle:

Principle #19Periodic action

Solution Approach 2:

The system dynamically adjusts monitoring intensity and update frequency based on content age, customer engagement levels, and market conditions. By changing parameters such as evaluation frequency and depth based on contextual factors, the system maintains high relevance while optimizing processing time and resource consumption.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20230368262A1Managing customer experience content

Publication Date: 2023.11.16 ORACLE INT CORP

AI summary

Techniques for managing customer experience content are disclosed. A system detects new information, such as a news story, a new service request, or a modification to a testimonial or case study, associated with a set of customer experience content, such as a customer testimonial. The system analyzes the new information to identify a sentiment associated with the new information. The system generates an effectiveness score for a particular set of customer experience content based on the new information. The system provides attribute data associated with the new information, and attribute data associated with the customer experience content, to a machine learning model to generate the effectiveness score. The system compares the effectiveness score to one or more threshold values to determine an action to perform associated with the customer experience content.