Types and Sources of Identity Graph Data

Primary Identity Graph Data Sources

• First-Party Data: This is data collected directly by businesses from their customers. It includes information from CRM systems, subscription lists, and customer feedback. Being the most reliable and accurate, first-party data forms the foundation of any identity graph.
• Device IDs: Unique identifiers associated with smartphones, tablets, and other devices. These IDs help in tracking user behavior across apps and websites on a particular device.
• Email Addresses: A significant identifier, especially when users log into multiple devices and platforms using a single email address.

Secondary Identity Graph Data Sources

• Third-Party Data: Data purchased from external providers that haven't had direct interaction with the users. This data helps in enriching the existing customer profiles.
• Social Media Platforms: With users logging into various apps and websites using their social media credentials, platforms like Facebook and Google become valuable sources for identity graph data.
• Public Records: Information from public databases and records can be used to verify and enhance the identity graph data.

Types of Identity Graph Data Available

• Deterministic Data: This data is definitive and is based on specific identifiers like email addresses or device IDs. It offers high accuracy but might be limited in scale.
• Probabilistic Data: Based on algorithms and patterns, this data predicts the likelihood of different identifiers belonging to the same user. It offers a broader scale but might not be as accurate as deterministic data.

Identity Graph Data Sub-Categories

• Consumer Identity Graphs: Focuses on individual consumers, tracking their behavior across devices and platforms.
• Business Identity Graphs: Concentrates on businesses and decision-makers within organizations, helping B2B marketers in their targeting efforts.

Common Identity Graph Data Attributes

• Personal Identifiers: Such as name, email address, and phone number.
• Device Identifiers: Including device ID, IP address, and browser type.
• Behavioral Data: Web browsing history, app usage patterns, and purchase behavior.
• Demographic Data: Age, gender, location, and other demographic details.

Understanding the sources, types, and attributes of Identity Graph Data is crucial for businesses. It ensures that they are leveraging accurate and comprehensive data to create a unified view of their customers, enhancing their marketing and advertising efforts.

Applications and Use-Cases of Identity Graph Data

Benefits of Implementing External Identity Graph Data in Your Business

• Unified Customer View: Identity graph data allows businesses to stitch together fragmented user profiles, providing a holistic view of the customer. This unified view enables personalized marketing, better customer service, and improved user experience.
• Cross-Device Tracking: With the proliferation of devices, tracking user behavior across multiple devices becomes crucial. Identity graph data enables businesses to understand the user journey across smartphones, tablets, desktops, and even IoT devices.
• Enhanced Personalization: By understanding the complete user journey, businesses can deliver highly personalized content, offers, and advertisements, leading to increased conversion rates.
• Improved Ad Targeting: Identity graph data ensures that ads are displayed to the right audience, reducing ad wastage and increasing ROI.
• Data Enrichment: Combining first-party data with external identity graph data can enrich customer profiles, leading to deeper insights and better decision-making.

Industry-Specific Applications

• E-commerce: Tailored product recommendations based on a user's browsing history across devices, leading to increased sales.
• Banking and Finance: Enhanced fraud detection by tracking unusual behavior across multiple devices.
• Healthcare: Providing personalized patient care by understanding their interactions across various digital touchpoints.
• Travel and Hospitality: Delivering personalized travel recommendations and offers based on a user's search and booking history across platforms.

Cross-Industry Applications

• Customer Retention: By understanding the complete customer journey, businesses can identify and address pain points, leading to increased customer loyalty.
• Market Research: Gaining deeper insights into customer behavior, preferences, and trends, aiding in product development and market positioning.
• Risk Management: In industries like finance and insurance, identity graph data can help in assessing risk based on a customer's behavior across platforms.

Who Uses Identity Graph Data?

• Marketers: For targeted advertising, personalization, and understanding the customer journey.
• Data Scientists: To build and refine algorithms, especially in areas like recommendation systems and fraud detection.
• Business Analysts: For gaining insights into customer behavior, market trends, and business performance.
• Customer Service Teams: To provide personalized support by understanding the customer's history and interactions across touchpoints.

Identity graph data is revolutionizing the way businesses interact with their customers. By providing a complete, unified view of the customer, it enables businesses to deliver unparalleled personalization, improve customer satisfaction, and drive business growth.

Case Study: Leveraging Identity Graph Data for Enhanced Customer Engagement

Introduction

In today's digital age, businesses are constantly seeking ways to understand their customers better and offer personalized experiences. One such business, a leading e-commerce platform, recognized the potential of identity graph data in enhancing its customer engagement strategy.

Challenge

The e-commerce platform had a vast user base, with customers accessing the platform through various devices, including desktops, smartphones, tablets, and even smart TVs. The challenge was to stitch together these fragmented user interactions and gain a holistic view of each customer's journey.

Solution

The company decided to integrate external identity graph data with its existing first-party data. This integration allowed the platform to:

1. Create Unified Customer Profiles: By combining data from various touchpoints, the platform could create a single, comprehensive profile for each user. This profile included data such as browsing history, purchase behavior, and device usage.
2. Enhance Personalization: With a unified view of each customer, the platform could offer highly personalized product recommendations. For instance, a user who searched for running shoes on their desktop and fitness trackers on their smartphone received recommendations for fitness apparel on their next visit.
3. Optimize Ad Campaigns: The platform used identity graph data to refine its ad targeting strategy. Ads were displayed based on a user's complete browsing and purchase history, ensuring relevancy and reducing ad wastage.
4. Improve Customer Support: Customer service representatives had access to a user's complete interaction history, allowing them to provide tailored support.

Results

• Increased Sales: Personalized product recommendations led to a 25% increase in sales.
• Higher Ad ROI: Optimized ad campaigns resulted in a 30% increase in click-through rates and a 20% increase in conversion rates.
• Enhanced Customer Satisfaction: The improved customer support strategy led to a 15% increase in customer satisfaction scores.

Conclusion

This case study highlights the transformative potential of identity graph data. By providing a 360-degree view of the customer, businesses can drive sales, optimize marketing efforts, and enhance overall customer satisfaction. The e-commerce platform's success story serves as a testament to the power of identity graph data in today's digital landscape.