Fluid Fan Propensity Scores & Audiences

Overview

Sports Innovation Lab offers the largest and strongest data assets for sports-focused transaction data with transaction data for over 240 million individuals (Total US, 18+ years old). Applying our proprietary data model, we use our transactional database to identify and track their sports fandom and brand affinity over time.

Use cases

  • Targeting and Activation: Identify individuals to target who are fans of a specific sport, team, or league, or specific retailer, category, or community to reach an expanded, highly targeted audience with Sports Innovation Lab propensity audiences to complement Sports Innovation Lab's deterministic audiences.

  • Customer Acquisition & Prospecting: Identify individuals and households with a high potential for becoming fans, customers, and buyers within specific sports, categories, or product groups.

Product details Sports Innovation Lab uses a modeling algorithm that grades US consumers as individuals. This model is built on deterministic purchase signals using advanced statistical and machine learning tools. A score indicates propensity of an individual to be a buyer over a predefined period, usually 12 months. The scores for all merchants can be used by customers to add purchase signals in their modeling process for creating audiences. The raw scores provide individual level propensity of shopping at a merchant or in a category. Propensity scores can be generated for custom Sports Innovation Lab-coded groups (teams, leagues, categories, etc.) or individual merchants, sample size permitting.

Data Assets:

  • Individual and Household Attributes:

    • 140M+ Individuals’ passively collected transactional data

    • 240M+ Additional individual's targeting attributed (name-based or pseudonymous)

    • 8.5B+ Online behaviors

    • 700M+ Individuals’ emails

    • 7000+ Sports & Entertainment proprietary IP identified Venues, Merchants, Brands

    • 80+ Custom Sports & Entertainment communities

Data is for US consumers only.


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