Personalization at Scale: Role of Identity Resolution in CDP and its Approaches for Better Customer Reach
The Blog Encapsulates
- An Introduction and Revisit to the Part 1 of the blog series
- The Identity Resolution feature of CDPs and the Tools used
- Applicability of Identity Resolution to Data Clean Rooms
- The Two Approaches for Identity Resolution
- Types of Matching in Identity Resolution
- How Identity Resolution enhances Marketing and Enterprise level Experiences
Introduction
In today’s bustling business landscape, where every click is a potential sale, the world is a playground for savvy shoppers and digital enthusiasts. As customers, we create profiles across multiple devices, with endless options at our fingertips, contributing to an ever-growing sea of customer data. However, leveraging this wealth of information to understand and influence customer online behavior can be daunting without accurately identifying individuals within this vast pool of data. This is where Identity Resolution emerges as the most promising solution, addressing challenges such as data deduplication, privacy concerns, and centralization.
A recent statistic says that 52% of customers1 expect offers to be always personalized. It is inherent that the significance of leveraging Identity Resolution and consequently providing personalized customer experiences cannot be stressed enough.
Our previous blog we introduced how Identity Resolution works and its benefits. This section of the series delves deeper into the approaches underpinning it and how it works as a feature of Customer Data Platforms (CDPs) to create a unified and comprehensive view of each customer.
What is Identity Resolution in CDP
Customer Data Platform (CDP) is a powerful tool that unifies your customer data, enabling marketers and enterprises to connect with their customers through a single source of truth. CDPs offer advanced Identity Resolution features using tools like Salesforce Data Cloud, Snowflake, Microsoft, Oracle, and Databricks which generate customer profiles by consolidating data from various sources.
Tools Providing Identity Resolution Features
- Snowflake: With Snowflake for identity resolution, you can establish a single source of truth for unique customer ID aggregation and matching, seamlessly tracking customers across marketing, sales, and operational platforms. Its highly scalable data platform, combined with machine learning and customer data profiling, accurately matches and unifies customer identities, providing a clear and precise view of each customer.
- Salesforce Data Cloud: As an identity resolution tool, it integrates data from various sources using AI-powered matching and cleansing algorithms to consolidate customer records and reduce duplicates, achieving a single customer view. Salesforce’s Einstein Trust Layer enhances security with data and privacy controls for GenAI, ensuring every transaction is secure. Partnerships with leading cloud providers allow seamless access to your data across existing data lakes and Data Cloud, as if housed in a single location.
- Oracle: Oracle provides identity resolution for Customer Data Platforms (CDPs) through its integrated cloud-based tools, including Oracle Data Integrator for data integration and Oracle Autonomous Data Warehouse for scalable processing. Oracle Customer Data Management (CDM) unifies and enriches customer profiles using deterministic and probabilistic matching techniques. Advanced machine learning and AI enhance identity resolution by detecting and merging duplicates. Real-time processing is supported by Oracle Stream Analytics, and collaborative analytics are facilitated through Oracle Analytics Cloud, ensuring accurate and up-to-date customer profiles.
- Microsoft: Microsoft provides identity resolution in Customer Data Platforms (CDPs) through its Azure and Dynamics 365 ecosystems. Azure Data Factory and Azure Synapse Analytics handle data integration and large-scale processing, while Dynamics 365 Customer Insights unifies data from multiple sources and uses AI-driven deterministic and probabilistic matching to merge duplicate records. Azure Machine Learning allows for the development of custom predictive models for complex identity resolution tasks. Additionally, Azure Stream Analytics ensures real-time data processing, and Azure Databricks offers a collaborative environment for refining identity resolution strategies.
- Databricks: Databricks supports identity resolution for Customer Data Platforms (CDPs) by leveraging its scalable data engineering and machine learning capabilities. It integrates data from various sources using robust ETL pipelines and a unified data lakehouse architecture. Databricks employs fuzzy matching algorithms and machine learning models to cleanse, deduplicate, and match records, creating unified customer profiles. Real-time data processing is facilitated through Delta Lake, which ensures reliable, real-time identity resolution.
The choice of technology for Identity Resolution depends on your organization’s specific needs and existing technology stack. The decision should be guided by your data integration needs, scalability requirements, machine learning capabilities, real-time processing demands, ecosystem compatibility, and cost considerations.
- Snowflake is ideal for high scalability and performance in a cloud-native environment, suitable for large volumes of structured and semi-structured data.
- Oracle excels in integrating diverse enterprise systems and offers robust machine learning and real-time processing capabilities, making it a strong choice for complex enterprise environments.
- Salesforce Data Cloud provides seamless integration and real-time insights within the Salesforce ecosystem, perfect for organizations heavily invested in Salesforce CRM.
- Microsoft’s Azure and Dynamics 365 offer excellent scalability, real-time processing, and integration with other Microsoft products, making it suitable for hybrid cloud environments.
- Databricks stands out for big data processing and collaborative data science, leveraging Apache Spark for advanced machine learning and real-time analytics.
Identity Resolution applicability for Data Clean Rooms
Consolidating vast amounts of data requires robust security, met by data clean rooms, secure environments where both internal and external teams can merge data for joint analysis while complying with privacy laws like GDPR and CCPA. Here, critical data, including personally identifiable information (PII), is anonymized, processed, and stored in accordance with regulations.
Identity resolution in data clean rooms facilitates integrating data from various sources, forming unified and precise individual profiles without compromising privacy. Despite the waning influence of third-party cookies, brands and partners can utilize first-party data in these environments to extract audience insights, develop segmentation strategies, tailor personalized experiences and offers, craft media plans, and conduct measurement and attribution analysis.
The Two Approaches for Identity Resolution
Once the Identity Resolution system grabs data – from the real world, online, and even from different devices – it can make a match, either by making a good guess or by being sure, depending on the tech and data it’s using. There are two primary approaches based on these perspectives.
- Deterministic Approach: This method relies on exact matches using statical analysis of information such as name, email, birthdate, etc. It compares records based on specific customer identifiers to ascertain if they belong to the same individual. This approach offers high accuracy but a narrower reach.
- Probabilistic Approach: It utilizes statistical algorithms to predict and connect identifiers based on customer behavior. It considers a wider array of data, including IP addresses, geographic location, and behavioral data like purchases and website interactions. While it extends identification reach, it does so with less accuracy.
Deterministic Approach | Probabilistic Approach |
|---|---|
Utilizes exact matches of static information | Uses algorithms to predict matches among similar data records |
High accuracy (70-80%) | Enables building user profiles without relying solely on identifiable information |
Enhances database quality and personalization | Increases database size, marketing reach and enterprise level performance |
Customizable matching rules | Predicts future customer behavior |
Types of Data Matching in Identity Resolution
Deterministic and Probabilistic approaches each have three data matching types that help you select the appropriate approach:
Deterministic Matching
- Single-field matching: Uses a single variable to determine if records connect to the same individual.
- Composite field matching: Assesses multiple customer identifiers, such as name and email.
- Cascading deterministic heuristic matching: Uses a cascading process with if/then conditions to recognize matches despite variable inconsistencies.
Probabilistic Matching
- Fuzzy string matching: Identifies similarities by expanding acceptance for discrepancies.
- Cascading mixed heuristic matching: Uses a range of deterministic and probabilistic algorithms sequentially to establish matches.
- Advanced machine learning matching: Analyzes correlations and uses neural matching techniques to evaluate associations between search queries and data.
A Strategic Path Towards Enhanced Marketing and Enterprise level Experiences
As we dive deeper into Identity Resolution, we understand its role in CDP using tools like Snowflake, Salesforce Data Cloud, Databricks, Oracle and Microsoft tool. By leveraging Deterministic and Probabilistic approaches, we can balance accuracy and outreach. Understanding the nuances of matching types allows businesses to tailor their strategies effectively. It is inherent that Integrating with Identity Resolution is essential for fostering lasting customer relationships.
At V2Solutions we craft Identity Resolutions algorithms that leverage both the Deterministic and Probabilistic approaches. The solutions are also designed for enterprise data management to help master customer records, product records, and suppliers. Our expertise in tools like Snowflake, Salesforce Data Cloud, Databricks, Oracle and Microsoft ensure to provide our clients with the best solution to deliver exceptionally seamless customer experiences.
Ready to set up Identity Resolution and amplify your marketing and enterprise level ? Let’s Connect.