Consumer Identity Data

What is consumer identity data?

Consumer identity data presents information about a device ID or model connected with a consumer profile. It is used to enrich customer data and customer profiles by monitoring their behavior, such as the channels and apps used (often with the use of third-party cookies). 

Where does the data come from?

Consumer identity data comes from service providers and companies tracking consumer data and behavior across touchpoints and devices, including the use of email, mobile phone number, and social media. Vendors collect data from a wide range of physical and digital sources to build consumer identity graphs. A consumer identity graph provides profiles for individuals, households, and mobile ad identifiers (MAIDs).

What types of attributes should I expect?

Consumer identity data provides personal data attributes of email, phone numbers, account usernames, IP addresses, online cookies, device IDs, household occupants, geographical location, and physical addresses.  

How should I test the quality of the data?

Consumer data is collected from diverse sources, and testing its accuracy is the prime concern. This data contains personal and personally identifiable information(PII), therefore consumer privacy must be considered, and the data must be GDPR and privacy-compliant. Data timeliness will drive the effectiveness of its use, and hence testing when the data was last updated is critical. Due to the nature of data collection, testing for consistency and completeness is also essential.

To test the quality of the data:

• Check the credibility of the sources and accuracy of data.
• Ensure that the vendor certifies compliance with the industry and region-specific data privacy regulations.
• Check if the data is updated in real-time, and if not, when the data was last updated, and if its update frequency matches your requirements.
• Verify the data consistency and completeness for the intended use.
• Test with a sample dataset to confirm integration with your systems.

Who uses the data?

Marketers use consumer identity data for digital marketing campaigns and to understand the behavior of the target audience and the best way to communicate with them. It also gets used to personalize communication throughout the customer journey and across devices.

A consumer identity graph helps marketers focus on the target audience with the right message and not waste resources on unfeasible leads.   

What are the common challenges when buying this type of data?

Accuracy is the most common challenge for consumer identity data due to the credibility of diverse sources. Customer profiling uses this data, which must be accurate to ensure correct audience targeting. The other equally important challenge is ensuring privacy compliance. Customer profiles keep changing as the customers and their households change jobs, addresses, and devices, and keep evolving to the next phases of their lives. Data timeliness will ensure accurate customer profiling to improve the effectiveness of the targeted communication. Data completeness and consistency also contribute to the accuracy of customer profiling, and they must be validated.

• Source credibility and data accuracy: Marketers use consumer identity data to profile and segment the audience for efficient communication. Precise profiling is possible only when the data is accurate, and the sources are authentic. Choose vendors who can assure data accuracy and the credibility of their sources.
• Privacy compliance: Consumer identity data includes personal information, personally identifiable information (PII), and sensitive information protected under various privacy regulations. Compliance with all the region or industry-specific data privacy regulations can be challenging. You can choose only the vendors who can deliver privacy-compliant data.  
• Data timeliness: Data accuracy is closely associated with how recently the data is updated and how authentically it presents the current consumer identity information. As customers move houses or change jobs, their profiles and requirements get revised. They may change mobile numbers or plan a major purchase. Data timeliness ensures that the most recent updates of customers are reflected in the data to drive precisely targeted marketing.  
• Data completeness and consistency: The data comes from several sources. Data across these sources can have overlaps, inconsistencies, and mismatches. Similarly, data with missing attributes or gaps can badly impact customer profiles and targeted marketing efforts. Completeness and consistency of consumer identity data will ensure more accurate profiling, leading to more precise segmentation.

What are similar data types?

Consumer identity data is similar to identity graph data, cross-device identity data,  first-party data, and other related categories commonly used for marketing and identity resolution.

You can find a variety of examples of consumer data in the Explorium Data Catalog.

Sign up for Explorium’s free trial to access the data available on the platform.        

What are the most common use cases?

The most common use cases are data-driven marketing, data onboarding, and identity resolution. This data also gets used in customer segmentation, behavioral targeting, and other methods of data-driven promotion planning.

• Data-driven Marketing: The use of data and technology has transformed marketing activities. Marketers can now target audiences more accurately and at scale. They use consumer identity data to leverage customer profiles, segment audiences, and access communication channels precisely. They develop targeted marketing communication to reach the right audience, convert more leads, and achieve higher ROI.  
• Data Onboarding: Technology accelerates and scales marketing reach, but needs complete data in the online environment. Data onboarding is the process of moving offline data to the online environment to enable its reconciliation. It identifies and matches offline customer records with the existing online audience profiles. Consumer identity graph connects the records and harmonizes the customer profiles for online audiences.  
• Identity Resolution: Customers use various channels to engage with companies, including websites, mobile apps, email, phone, chat, or a physical store. Often, these engagements result in separate tracking IDs, such as cookies, postal addresses, or phone numbers. Marketers attempting to communicate with customers across these IDs may send different messages or the same message multiple times. Both the cases result in wasted marketing resources and disconnected customer experiences. Data-driven identity resolution uses identity graph data to connect different IDs to a single profile. Harmonizing customer identity profiles helps drive personalized omnichannel communication for better experiences and higher ROI.

Which industries commonly use this type of data?

Most companies targeting consumers commonly use this data for marketing, advertising, and identity resolution. They include retail, eCommerce, CPG, entertainment, travel, hospitality, leisure,  financial service providers, insurance providers, and banking.   

How can you judge the quality of your vendors?

Consumer identity data comes from diverse sources, and its quality largely depends on the quality of vendors. You can judge vendor quality by leveraging the customer feedback and case studies available on their websites, testing with sample datasets, and finally interacting with their reps.

• Customer reviews and testimonials: Customers provide feedback on vendor quality as numerical ratings, detailed reviews, and testimonials. Reviews cover both strengths and weaknesses of the vendor, while testimonials mostly highlight the strong points. All the types of customer feedback help you assess the vendor quality and suitability to your projects.
• Case studies: Some vendors offer case studies of their successful projects. They illustrate how the vendor has contributed to solving the challenges faced by the customers. Case studies are good indicators of vendor engagement, knowledgeability, and the ability to deliver custom solutions. Case studies can help evaluate if your projects are similar and if the vendor can provide you with the required datasets.
• Demo: Vendors sometimes provide recorded demos on their websites or offer to arrange a live demo. A demo is valuable in evaluating the datasets and the ease of integration.  Watching vendor data in action can help you judge vendor capabilities. If sample datasets are available, you can test them in your projects to confirm their quality.
• Interacting with vendor reps: After shortlisting vendors using the information available on the websites, you can take the final step of interacting with the vendor reps. They can resolve your queries, explain their quality testing process, and explore the possibility of custom datasets. Interaction with vendor reps opens up the opportunity for a mutually beneficial long-term partnership.