Propensity-Scored Targeted Messaging for Relevant Insurance Outreach
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Solution Overview
Problem
Traditional insurance marketing campaigns often fail to deliver relevant messages to a large number of people due to the use of limited data and non-preferred delivery channels, resulting in low effectiveness.
Innovation Solution
A message optimization engine that utilizes various data sources, including user profile data, insurer data, and public/private data, to calculate a propensity score for targeted messaging, ensuring messages are sent through preferred channels when the score exceeds a threshold, optimizing content and delivery for enhanced relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional marketing campaigns target large groups of people using limited data, then the campaign reach is improved, but the message relevance deteriorates
Solution Approach 1:
The patent segments the large target group into smaller subgroups based on multiple data dimensions including demographic information, behavioral patterns, and interaction history. This segmentation allows the system to maintain broad campaign reach while delivering personalized messages to each segment, thereby resolving the contradiction between reaching large audiences and maintaining message relevance.
Solution Approach 2:
The system applies local quality by tailoring message content, delivery channels, and timing to the specific characteristics of each customer segment. Instead of using a uniform approach for all recipients, the system customizes messaging strategies for different groups based on their unique data profiles, thus maintaining relevance while scaling to large audiences.
2Device complexity
If traditional campaigns use limited data sources, then the system complexity is reduced, but the targeting precision deteriorates
Solution Approach 1:
The patent implements a universal data architecture that integrates multiple data sources including internal customer data, external demographic data, and behavioral interaction data. This multi-functional data system serves various marketing objectives simultaneously, achieving high targeting precision without proportionally increasing system complexity through standardized data collection and processing mechanisms.
Solution Approach 2:
The system introduces data integration intermediaries that aggregate and harmonize information from diverse sources before applying it to targeting decisions. These intermediary layers process raw data from multiple sources into unified customer profiles, enabling precise targeting while managing complexity through standardized integration protocols rather than direct connections to each data source.
3Productivity
If messages are sent through non-preferred delivery channels, then the distribution efficiency is improved, but the customer engagement deteriorates
Solution Approach 1:
The system dynamically selects delivery channels based on real-time analysis of customer preferences and interaction patterns. Rather than using static channel assignments, the system adapts message delivery to match each customer's preferred channels, whether email, mobile notifications, or other platforms. This dynamic approach maintains distribution efficiency while significantly improving engagement by respecting customer preferences.
Solution Approach 2:
The patent incorporates feedback mechanisms that continuously monitor customer responses to messages delivered through different channels. This feedback loop allows the system to learn and adapt to customer preferences over time, adjusting channel selection to optimize both distribution efficiency and engagement. The system uses this feedback to refine its understanding of preferred channels without requiring manual reconfiguration.
Data Source
AI summary
Method and systems generate optimized and online targeted messages. Various campaign criteria are provided having different advertising or marketing goals that cause the delivery of targeted messages. Messages may be generated based upon customer data, customer preferences, life events, marketing campaigns, predictive models, and/or propensity scores. For instance, messages may be sent when a propensity threshold score is exceeded indicating a high likelihood of a milestone event, which may be indicative of customer behavior or an event that is relevant to the campaign goal, thereby sending more relevant messages to customers. A milestone propensity score may be calculated using a predictive modeling algorithm having weighted data variables, which may include data provided by the customer or accessed through various sources, such as monitoring customer online interactions with their permission. Aspects also include identifying customers that match campaign criteria by comparing propensity scores to thresholds to assist marketing and advertising efforts.