Introduction
This page illustrates how Redpoint enables Identity Resolution.
Redpoint offers a comprehensive solution for identity resolution through its robust platform, Redpoint Customer Data Platform (CDP). This platform is equipped with advanced capabilities in data management, identity handling, and interaction facilitation, ensuring that organizations can effectively manage customer identities and interactions across various touchpoints.
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Platform: Redpoint CDP integrates sophisticated data management functionalities, identity resolution features, and interaction capabilities that empower businesses to understand and engage with their customers more effectively.
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Data Management: Redpoint's data management system excels in several critical areas, including:
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Parsing: breaks down complex data sets into manageable components
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Normalization: ensures that data is consistent and standardized
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Validation: checks for accuracy and completeness
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Enrichment: enhances the existing data with additional relevant information to provide a fuller picture of customer identities
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Configurable matching: This system utilizes a rule hierarchy that ranges from hard keys, which are definitive identifiers, to fuzzy tiers, which allow for more flexible matching criteria. This flexibility is coupled with explainability, ensuring that users can understand how matches are made, and steward-managed scoring, which allows for oversight and adjustments based on organizational needs.
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Golden Record stewardship: The concept of a "golden record" is central to effective identity resolution, and Redpoint provides robust stewardship for these records, including:
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Survivorship policies that dictate which data points should prevail in the event of conflicting information
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Lineage tracking to understand the history of data changes
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Versioning to maintain records of updates
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Householding capabilities that group related identities for a more holistic view
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Real-time identity services: These services include APIs that facilitate visitor recognition and enable event-driven updates, ensuring that organizations can respond promptly to changes in customer behavior and preferences.
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Governance & Privacy: This includes consent-aware matching, which respects customer preferences regarding data usage, as well as providing policy templates, subject rights APIs, and workflows that help organizations comply with privacy regulations and maintain trust with their customers.
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Activation: Users can build segments based on unified identities, control the frequency of communications, and personalize customer journeys, all of which are essential for enhancing customer engagement and driving business success.
Implementation approach
The initial phase of the implementation approach, spanning the first 90 to 120 days, is crucial for laying a solid foundation for the project.
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Discovery & success criteria: Thoroughly define the various use cases that will guide the project. This includes identifying relevant data domains and establishing key performance indicators (KPIs) that will measure the success of the implementation.
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Data profiling & quality plan: Conduct a comprehensive assessment of the existing data to establish a baseline for data quality. Identify and address any critical gaps that may exist, ensuring that the data is reliable and accurate for subsequent phases.
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Rule design: Draft the match hierarchy, setting appropriate thresholds for data matching, and determining survivorship rules. Ensure that the data integration process is effective and meets the project's objectives.
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Back-testing & tuning: Simulate matches to evaluate the effectiveness of the rules designed in the previous phase. Review these simulations with business stakeholders and data stewards to ensure alignment and make necessary adjustments.
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UAT & steward playbooks: Conduct User Acceptance Testing (UAT) to validate the processes for merging and un-merging data. Additionally, creating steward playbooks will provide guidelines for maintaining audit trails, ensuring that all actions taken during the data management process are documented and can be reviewed.
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Rollout & monitoring: Finally, the project moves into production. This includes deploying dashboards for ongoing monitoring and establishing quarterly reviews to assess the effectiveness of the implementation and make any necessary improvements.
By following this structured approach, the project aims to achieve its goals effectively within the designated timeframe.