Data Disclaimer

Last Modified: March 15th, 2024

FISCOWL, LLC (and AnalyticOwl.com) Advertising metrics, including, but not limited to, aggregated data for use in the NumericOwl (Industry Insights) product, derived from matching time stamped website data against time stamped advertising data are designed to be used for estimating purposes only, and accuracy is not guaranteed. NumericOwl (Industry Insights) insights and reports are pure aggregations of advertising analytics. Session and New User information is reported directly from AnalyticOwl advertising analytics. In the case of new user only advertisers, Session data includes only new user advertising analytics in order to provide the most relevant information. FISCOWL, LLC does not guarantee the accuracy of any information available on this site, and is not responsible for any errors, omissions, or misrepresentations. The user acknowledges that FISCOWL, LLC is providing the means to 'match' time stamped information and not the resulting data, as FISCOWL, LLC has no control over the availability or accuracy of data that is uploaded or imported to create Advertising metrics. The user agrees there is no monetary or non-monetary remediation owed by FISCOWL, LLC to the user at any time, directly or indirectly, for any reason, as a result of errors or omissions experienced while using AnalyticOwl's advertising analytics dashboard. Advertising metrics do not have the ability to quantify the value of any advertising for any business, product or service and should be used with the knowledge that there is always a degree of error to any advertising metrics. To make an informed decision about changing advertising budgets or schedules may require additional information such as market, audience or ratings data which are not gathered by FISCOWL, LLC.

Attribution is primarily based on a window of time and geography as indicated, however, certain adjustments are uniformly made to ensure relevance and percent over attribution and over reporting. Raw attribution must be considered in the aggregate, which indicates comparative performance, and not actual performance.

As of July 1st, 2023, Google Analytics replaced UA (Universal Analytics) properties with GA4 properties. As part of this change the available filters and data are changed to reflect both minute-level web data (typically for single market advertisers) and hourly web data (typically for multi-market advertisers) where minute-level web data is not available via Google's API. Due to changes in Google's web data collection methodology there may be differences between old UA metrics and new GA4 metrics.

Google Analytics data is used to calculate the number of page views, new users and user sessions, known as visits, in each minute throughout each day (where available in the data feeds). When using by-minute data for broadcast, the advertising analytics (or attributed visits), are the number of visits in each minute within the 8-minute tracking window that begins when each ad spot airs based on the specific attribution methodology selected during reporting (page views, user sessions, or new users). Estimated Daily Visits is aggregated by data partners and includes all visits, not just unique or new visits. For Broadcast commercials, visits are randomly assigned to one of the commercials in the overlapping range. For by-hour broadcast data, the visits within each hour are compared to the number of commercials within that same hour. For impression-based advertising, like streaming, OTT or CTV, an 8-minute tracking window is used if impressions are received by the minute, or an hourly tracking window is used if impressions are received by the hour. For Streaming commercials/impressions, correlated visits are split evenly across all overlapping commercials. All impressions are geofenced by location and impressions are measured against geofenced visits. Geo-fenced data is defined as website visits in the associated data feeds that match as closely as possible to the area where commercials were delivered (subject to availability in the data feed). Lift (or attributed/broadcast visits percentage and sometimes referred to as traffic contribution) is the comparison of the attributed visits against the total visits in any given date range of advertising days, or contiguous days, if selected. In instances of high traffic and/or spot volume, additional filters or multipliers may be applied to maintain insights, statistical significance and validity. Some advertiser data is limited to new user baseline only due to high volume of either advertising schedules or web traffic. We look at the days when an advertiser is off air as a baseline to compare to the days when the advertiser is airing commercials. The baseline of off-air web traffic is constantly changing based on different time periods measured and the total number of days included in the measurement where an advertiser was airing commercials (on air) or not airing commercials (off air). For additional data granularity, especially when an advertiser is on air every day and a baseline is not possible, we use geo-fencing web traffic to only the demographic market where advertisements aired, in addition to a tight time measurement window and the ability to filter by the source or destination of web traffic. We can also filter response to new or new/returning users (where available in the data feed) to increase the accuracy of directional insights. In instances of very high commercial volume, a sampling of the available data is used to preserve data accuracy and usability.

When comparing traffic reported on the Google Analytics dashboard to the traffic reported in the Analytics platform, there will commonly be differences. This is because the data fed through the Google Analytics API to AnalyticOwl is unfiltered, while the data reported on the Google Analytics dashboard generally has filters applied.

For more information from Google: https://developers.google.com/google-ads/api/docs/reporting/uireports#common_differences .

It is possible to make adjustments to how data is reported on the Google Analytics dashboard so that it matches what is fed through the Google Analytics API.

For more information from Google: https://developers.google.com/google-ads/api/docs/reporting/uireports .

Goal (or event) names are edited and maintained in Google Analytics for each Google Analytics view. Goal (or event) names are not historical. Google Analytics does not maintain a history of goal names. If and when you update a goal name in a view, that goal name is used in all reporting, both current and historical. Goal data is subject to availability in each data feed.

Foot Traffic metrics are derived from our foot traffic data provider, Precisely PlaceIQ, which tracks approximately 100m+ million mobile devices in the U.S. and any Foot Traffic metrics, including foot traffic visits, should be considered an estimation of Foot Traffic only. Foot Traffic visits are calculated by evaluating multiple attributes* such as GPS, WiFi or cell tower source, latitude and longitude of location, geofencing, movement detection, speed of travel, direction of travel, device orientation, business density in surrounding area, popularity of place, hours of operation, time of day, day of week, frequency of visitation and plot polygons of locations. Machine learning is used to more accurately determine user location. *This greatly reduces 'App Spoofing' issues where user locations appear to be legitimate, but the user is physically in a different location. Data comes from multiple diverse sources from data partners, public sources, social signals, facts found online and hand curated data to ensure a holistic and unbiased representation of the physical world. Importantly, there is no reliance on external hardware or bluetooth signals and FiscOwl, LLC does not receive personal data, just an anonymized device ID from data partners to know if a device is new or returning to a location. It is widely regarded that most foot traffic measurement will show approximately 7% to 10% of foot traffic in a location. Extensive research was performed looking at known stadium attendance and measuring that attendance against measured foot traffic. Activity detection technology helps to give added value to a mobile device signal to determine what type of activity a user was doing (for example visiting a store versus driving versus walking) and how long the activity was performed. This data is used to determine 'Dwell Time' and helps to accurately attach a user's activity to a device and to a place in the real world. To increase accuracy it's possible to cross reference foot traffic to a 'Truth Set' of data to help validate trends, patterns and location data before any advertising decisions are made. A 'Truth Set' of data would be considered where the attributes are highly accurate, for example sales data, in-store beacons or CRM customer data.

Information provided is always subject to change at any time and without prior notice. Advertisers, Ad Agencies, Media Buyers, Media Companies understand that all information provided are estimates and should be used with the express understanding that any advertising decisions should be based on a multitude of factors that FISCOWL, LLC. does not capture. FISCOWL, LLC. does not guarantee or warrant any of the information obtained as a result of using the AnalyticOwl advertising analytics service or NumericOwl (Industry Insights) aggregated reports.

For more information, please visit:

For full details of Google's changes please visit: https://analytics.google.com

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https://marketingplatform.google.com/about/analytics/terms/us/

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