Automated Credit Database Analysis for Targeted Prospect Notifications
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Solution Overview
Problem
Consumer credit databases are not effectively utilized to identify and target consumers who are both credit-worthy and currently interested in obtaining credit, due to technical obstacles such as complex and time-consuming analytical classifications, and the inability to leverage recent data for timely marketing efforts.
Innovation Solution
A system and method for automatically analyzing online consumer credit database information to identify consumers who meet credit provider criteria and are indicated by recent 'trigger events' as interested in credit, generating timely notifications for targeted marketing efforts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex analytical classifications are performed on database records to identify prospective customers, then the accuracy of identifying credit-worthy consumers is improved, but the time required for analysis increases significantly
Solution Approach 1:
The system performs preliminary complex analytical classifications during off-peak hours or in advance to pre-identify credit-worthy consumers, storing these classifications in the database. This allows real-time queries to retrieve pre-computed results without performing time-consuming analysis during critical decision-making moments, thus resolving the contradiction between analysis accuracy and time requirements.
Solution Approach 2:
The database is segmented into multiple specialized indexes and data structures that organize consumer information by different criteria (creditworthiness, recent activity, demographic factors). This segmentation allows the system to quickly retrieve relevant subsets of data without scanning the entire database, improving both the speed and precision of identifying prospective customers who meet specific criteria.
2Speed
If the database is optimized for quick extraction of simple information, then the speed of data retrieval is improved, but the ability to perform complex analytical classifications deteriorates
Solution Approach 1:
The system introduces an intermediary layer of pre-computed analytical results and derived data structures that bridge the gap between simple data storage and complex analysis. This intermediary layer contains pre-calculated creditworthiness scores, risk assessments, and other complex classifications that can be quickly retrieved without performing full analytical computations, thus maintaining both speed and analytical capability.
3Quantity of substance
If blanket marketing campaigns are used to contact large numbers of consumers, then the coverage of potential customers is improved, but the conversion rate deteriorates due to contacting uninterested or ineligible consumers
Solution Approach 1:
The system applies local quality by tailoring marketing communications to the specific characteristics, preferences, and creditworthiness of each individual consumer rather than using uniform blanket campaigns. By segmenting the consumer base and customizing offers based on pre-computed analytical classifications and recent trigger events, the system maintains high coverage while significantly improving conversion rates through targeted, relevant communications.
4Loss of time
If recent consumer activity data is monitored continuously to identify trigger events, then the timeliness of identifying interested consumers is improved, but the computational resources required increase
Solution Approach 1:
The system extracts and monitors only the most relevant trigger events and recent consumer activities that are most likely to indicate interest in credit products, rather than continuously analyzing all consumer data. By identifying and focusing on key indicators such as recent credit inquiries, account openings, or specific behavioral patterns, the system achieves timely identification of interested consumers while minimizing computational resource requirements.
Data Source
AI summary
Systems and methods are described for identifying a subset of interest from a general population and for monitoring a database of daily activity logs associated with the general population in order to identify database entries indicative of an occurrence of a pre-defined trigger event that is associated with a member of the subset of interest. In particular, systems and methods are described that allow a massive database of daily activity logs to be monitored to identify trigger events that have occurred within the past twenty-four hours or other very recent time period. Embodiments are described that may be advantageously used by a provider of credit-related products and/or services who wishes to accurately target prospective customers, identified by the system, based on occurrence of a trigger event, as being in a decision-making phase of credit shopping, for purposes of making a timely and targeted offering relevant to the customers' current activities.