2.2 Data Types

TickerTrends collects and organizes multiple categories of financial alternative data, each designed to give investors a unique perspective on consumer demand and brand momentum. These datasets capture how people search, shop, engage, and talk about companies online, which makes them leading indicators of future business performance.

Each data type has its own strengths. For example, Google Search highlights early demand signals, Amazon Search reflects real purchase intent, YouTube and TikTok show cultural momentum, and Web Traffic provides direct visibility into customer digital behavior. Social sources such as Reddit and Instagram capture grassroots attention, while Wikipedia page views can track shifts in public awareness.

In the pages that follow, each dataset is explained in detail, including:

• What is collected and general collection methodology

• Historical length and update frequency

• Example charts and tables

• Best use cases for investors (e.g., proxy for demand, virality detection, brand tracking)

We keep the raw data intact without normalization, so users see accurate reflections of the underlying source. Normalization is only applied within KPI forecasting models, where signals from multiple datasets are combined to predict company KPIs.