| Audience | Data teams, marketers |
Match Booster takes your first-party identifiers across multiple data types and resolves them to people and devices in its identity graph, so ad platforms can match more of your records.
Overview
Match Booster supports four types of input data, each suited to different use cases. The type of data you provide determines which records can be matched and what enriched identifiers Match Booster can append.
For details on configuring these inputs, see Map match keys.
Consumer data
Consumer data matching enriches your first-party records with additional emails, phone numbers, device IDs, and cookie IDs. Use this method when you collect emails or phone numbers from consumers and want ad platforms to match more of those records.
This is the most common matching method and works well for targeting, suppression, and lookalike campaigns.
Work data (B2B)
Match Booster matches B2B employees to consumer profiles and devices. If you collect work emails from prospects or have purchased a list of business contacts, work data matching enriches those records with consumer emails, phone numbers, device IDs, and cookie IDs that ad platforms are more likely to match on.
This is useful when your first-party data is primarily work-context identifiers that have low match rates in consumer-oriented ad platforms.
Address data
Match Booster matches first-party name and address data to person-level identifiers in its identity graph, including hashed emails, phone numbers, and device IDs.
To use address matching, provide at minimum: first name, last name, first address line, and postal code. Including city and state improves accuracy. See Match keys reference for format requirements.
Anonymous web visitor data (IP address)
Match Booster matches anonymous visitor IP addresses to known consumers and devices using reverse IP lookup. When you provide a first-party IP address, Hightouch filters the data to verify it comes from a residential IP address. If it does, Hightouch's identity graph matches that IP address to the people and devices associated with that household and sends those identifiers to ad platforms.
This method is designed for web retargeting scenarios where you have visitor session data with IP addresses but no known user identifiers.