What does "tickerized" data mean?

Consumer-behavior signals, foot traffic, transactions, ACR exposure, web intent, pre-mapped to public equity tickers and reporting calendars, so quant and fundamental analysts can join the data directly into a backtest or earnings-preview model without doing the company-to-signal mapping themselves.

What signals are tickerized today?

Foot-traffic share by ticker (retail, restaurants, lodging, healthcare), transaction-share via the CPG feed, web-intent via the clickstream feed, and CTV ad exposure via the ACR feed. Brand-to-ticker mapping is maintained against the SEC EDGAR universe and refreshed quarterly with corporate-action handling.

What's the refresh cadence?

Daily for the underlying signals; quarterly for the brand-to-ticker mapping and corporate-action handling. Earnings-window deliveries can be accelerated on request, standard cadence works for most discretionary and quant managers.

Is the data backtested?

Backtest history is available depending on the signal: foot-traffic typically goes back 5+ years, transactions and ACR shorter. A backtest panel is shared during evaluation so buyers can validate signal-to-revenue correlation before licensing.

Who licenses tickerized data?

Hedge funds (long/short, market-neutral, event-driven), fundamental research desks at long-only managers, sell-side research, and corporate-development teams running competitive intelligence. Data is sold as a research-only license; redistribution requires a separate agreement.

Tickerized Alternative Data Feed (Public Equities)

Since 2018

Privacy-safe enterprise data

VOSB & SDVOSB

U.S. Veteran-Owned, Service-Disabled

SCC Transfers

EU/UK/CH per privacy policy

GDPR · CCPA · CPRA

Consent-first data sourcing

Overview

A curated data layer that maps web engagement, CTV exposure, foot traffic, and purchase behavior to public company tickers through brand-to-parent hierarchies. Built for hedge funds, asset managers, and financial analysts looking for alternative data signals for revenue proxies and brand momentum analysis.

Want the buying guide?

If you’re evaluating vendors or writing an RFP, this guide lays out what to ask for (coverage, refresh, governance) and links to the best references.

Alternative data buying guide

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For technical evaluation, we can provide a sample payload, schema notes, and delivery options (S3/SFTP/Snowflake) aligned to your workflow.

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Data Types

Web traffic by ticker
CTV exposure by brand
Foot traffic by location
Purchase signals

Delivery Formats

CSVJSONParquetSQL

Key Features

Brand-to-parent-company mapping
Daily signal updates
Historical data for backtesting
Ticker coverage list shared during evaluation

Sample fields

A representative slice of the Tickerized Data schema. The data dictionary shared during evaluation lists every field with its type, nullability, join keys, and refresh cadence.

Field	What it contains
ticker	Public-equity ticker associated with the observed brand or parent company.
brand_id	Normalized brand identifier used in the brand-to-parent hierarchy.
signal_date	Observation date for the mapped alternative-data signal.
signal_type	Signal family such as web engagement, CTV exposure, foot traffic, or purchase activity.
signal_value	Normalized value for the ticker-mapped signal.

How Tickerized Data Is Constructed

GSDSI's tickerized data pipeline starts with raw signal ingestion from four distinct data channels: device-level GPS location data capturing foot traffic to branded retail locations, ACR-derived CTV viewership measuring household ad exposure by brand, clickstream data tracking web engagement and digital share-of-voice, and transaction-level CPG purchase data capturing brand-level consumer spend. Each signal source is normalized, quality-scored, and then mapped to parent companies through a proprietary brand-to-ticker hierarchy that links subsidiary brands, franchise locations, and DBA names to their publicly traded parent entity. The final dataset maps signals to publicly traded equities with daily signal updates and historical data available for quantitative backtesting; the ticker list and history depth for each signal are shared during evaluation.

Common Applications for Tickerized Signals

Quantitative hedge funds integrate GSDSI's tickerized data into systematic trading models, using foot traffic trends as revenue proxies and web engagement velocity as a leading indicator of brand momentum. Fundamental analysts at long-only asset managers use the data to validate or challenge consensus estimates ahead of earnings announcements, looking for companies where observed consumer behavior diverges from Wall Street expectations. Sell-side equity research teams incorporate alternative data signals to differentiate their coverage and give clients non-consensus insights. Risk managers monitor tickerized signals for sudden behavioral shifts, like a sharp decline in foot traffic or web engagement, that may come before negative earnings surprises or operational disruptions at portfolio companies.

Industries Using This Product

Financial Services

Alternative data signals for alpha generation

How finance buyers diligence tickerized signals

Alternative-data teams evaluate tickerized feeds through historical backtests, brand-to-parent mapping review, revision policy, and leakage controls. The core procurement question is whether signal construction is stable enough for repeatable research.

Review the brand-to-ticker hierarchy, subsidiary rollups, and corporate-action handling.
Backtest signal stability across known earnings periods and market events.
Validate point-in-time files, restatement rules, and historical availability before production.
Confirm permitted use for investment research and restrictions on redistribution.

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Tickerized Data