Customer
Unified Customer and Household Views
Consolidates individual customer data and household information into a single, accurate record for each entity, creating a golden record for both individuals and households.
Reduces redundancy and inconsistencies across customer and household data, enhancing overall data quality.
Householding Algorithms
Uses advanced algorithms to identify and link individuals belonging to the same household based on shared addresses, financial dependencies, or other relational data.
Allows for dynamic household formation and reconfiguration as customer data evolves.
360-Degree Customer & Household Insights
Offers a holistic view of customers and their household contexts, facilitating targeted marketing campaigns, personalized service offerings, and strategic sales initiatives.
Enhances decision-making with comprehensive analytics and reporting tools that include household-level insights.
Lead and Prospect Nurturing
Entity Resolution
Advanced entity resolution techniques compare prospect records with existing golden records to identify potential matches, enhancing the data quality and reliability.
Householding Algorithms
Uses advanced algorithms to identify and link individuals belonging to the same household based on shared addresses, financial dependencies, or other relational data.
Allows for dynamic household formation and reconfiguration as customer data evolves.
Probabilistic Matching
A higher match score indicates a stronger probabilistic match, suggesting a higher likelihood that the prospect record corresponds to one of the golden records.
Data Deduplication Process
An automated deduplication process is initiated to identify and eliminate any duplicate entries within the prospect data, ensuring each record is unique and relevant.
Match Score Analysis
Upon calculating the match score, the relationship between each prospect record and the corresponding golden records is established. This score is stored alongside the relationship information for each record.
Attribute Matching & Scoring
The match score for each prospect is calculated using a weighted average approach, where the similarity scores of individual attributes are aggregated. This employs various algorithms such as Jaro Winkler, Edit Distance, and Soundex, with the flexibility to include additional algorithms as necessary.
Our Solutions
Hyper-personalize Customer Experience Leveraging Knowledge Graphs while improving the lifetime value and retention. Link to C720



