2.1 Coverage Overview

One of the most common questions we get is about historical length, granularity, and update frequency. Below is a summary comparison of our key datasets:

Data Type	Historical Length	Granularity	Update Frequency
Google Search	2004-present	Weekly	Weekly
Amazon Search	2+ years	Monthly	Monthly
YouTube Search	2008-present	Weekly	Weekly
YouTube Followers	Since YouTube channel inception	Daily	Daily
Instagram Followers	Since Instagram account inception	Daily	Daily
Web Traffic (Main Domain)	2+ years	Daily	Weekly
Website Traffic (Domain Path)	2+ years	Daily	Weekly
Mobile App Usage (USA & Global WAUs - iOS & Android)	2+ years	Weekly	Monthly
Mobile App Store Rankings (Top Free Charts)	2+ years	Daily	Weekly
Android App Review	2+ years	Daily	Daily
TikTok Hashtag View	3 years	Monthly (Daily Available)	Weekly
TikTok Followers	Since TikTok account inception	Daily	Daily
Reddit Subscribers	Since Subreddit account inception	Daily	Daily
Reddit Post	Since Subreddit account inception	Daily	Daily
Wikipedia (Page Views)	Since Wikipedia page inception	Daily	Daily
Financial Data (Consensus, Estimates, News Sentiment)	Varies (via external feeds)	Quarterly, Monthly, Daily	Varies (via external feeds)

Data Integrity and Normalization We preserve raw data as closely as possible to the original source to ensure accuracy and transparency. Unlike many providers that pre-normalize datasets, our approach keeps the underlying signals intact. Normalization is applied only within our KPI forecasting models, where it is necessary to align disparate signals into a unified framework for predicting company performance. This way, users always have access to both the raw, unadjusted data for direct analysis and the normalized outputs used in KPI forecasts.