Purchase Behaviors Of U.S. Sports Fans - Sample

Sports Innovation Lab/Purchase Behaviors Of U.S. Sports Fans - Sample is a Card & Transactions data product listed on Snowflake Marketplace and indexed by The Alternative Data Encyclopedia.

How Fans Of Different U.S. Sports Leagues and Teams Spend With Brands

The category commercialized in the mid-2010s when firms such as Second Measure and Earnest Research began selling merchant-level spend estimates to hedge funds. It entered mainstream practice after earnings cycles in which panel users visibly out-forecast company guidance. Coverage has since broadened to commercial-card and SKU-level feeds, while delivery latency compressed toward a single day.

The signal

Aggregated, anonymized panels of card payments that measure consumer spending by merchant, brand and sector in near real time. The signal approximates company revenue between reporting dates, replacing modeled guesswork with observed transactions.

Card spend leads reported revenue by days to weeks, so divergence between panel growth and consensus anticipates earnings surprises and estimate revisions. Funds use it to front-run retail comparable-store sales, validate guidance on restaurant and e-commerce names, and track private-company traction during diligence. Because a single panel observes all competitors simultaneously, it also prices relative share shifts that no individual disclosure reveals. Raw records contain transaction amount, timestamp, merchant descriptor or category, geography, channel (in-store versus online) and payment method; some providers extend to UPC/SKU line items and commercial-card spend. Records are pseudonymized at the source and aggregated to merchant level before delivery. Derived layers add growth rates, market-share series, average ticket and cohort retention mapped to tickers.

Data characteristics and access

Last updated: 2024-09-20.

Panels are assembled through agreements with issuers, processors and fintech apps, mirroring the authorization, clearing and settlement lifecycle of each purchase. Vendors clean merchant descriptors, resolve them to corporate parents and tickers, and weight or scale aggregates against benchmarks such as reported revenues to correct demographic skew. Delivery is typically daily with roughly a one-day lag, shipped as point-in-time tables rather than revised snapshots. Established providers describe tens of institutional sources and active cards measured in the hundreds of millions.

Caveats and compliance

Panels skew toward the demographics of banking-app users and underrepresent cash-heavy verticals and small merchants. Panel composition drifts as sources join or leave, shifting aggregate levels without notice. Scaling models are proprietary and differ across vendors, so two panels can disagree on the same retailer until reported actuals recalibrate both.

This is among the most regulated alt-data categories: GDPR, CCPA and GLBA govern the upstream personal-data flows, and card-network rules bind processors. Buyers should verify consent chains, aggregation thresholds, re-identification testing and data-broker registrations during due diligence.

Who uses this signal

Long/short consumer equity funds and retail-sector analysts use panels as a pre-earnings revenue telescope; macro teams read them as high-frequency consumption nowcasts. Corporate strategy and investor-relations teams buy the same data for competitive benchmarking.

Complementary signals

This kind of signal pairs naturally with adjacent categories of the encyclopedia:

Further reading