Track the number of job listings each day to better understand the hiring plans of an individual company. This data set can be filtered by location, position, and industry. Certain positions and locations can be indicators of internal activity such as who the company is hiring as well as how quickly a company is expanding into a new market. Users will be able to see the overall number of job listings over time for a company giving insight into growth. Job listings is one of the drivers of turnover, since it takes more resources to hire than fire employees.

Here's

Thinknum’s “Stores” dataset allows users to overlay and calculate proximity and density of competitor overlap of store locations between thousands of public and private companies. Our demographic mapping tool enables profiling of store locations based on US Census and BLS demographic metrics. Store locations are indexed from the company’s own website. This dataset is comprised of whitespace, a function that will enable users to see new innovative opportunities.

Nearby Competitor Function

Utilize the nearby competitor function to analyze the number of competitors that exist within a user defined radius of a particular store.

Let's take Lululemon stores as

Thinknum’s “Traction” metrics are comprised of real time, publicly available data, indexed from a company’s own website. Examples would include, HTTP Hits Per Second, viewers and channel count.

Let's take Twitch.tv as an example for Amazon's Traction. Users will be able to view the hourly Channel Count and Concurrent Viewership by category, for the live streaming video platform.

Below is a query comparing the Concurrent Viewer Count for Apex Legends and Fortnite.

Here is the link for the documentation on the columns for this dataset:Columns Definition

The Fulltext search function scans Thinknum’s universe and returns companies that are and that are not associated with a user defined term.

Thinknum's screener populates a list of public and private tickers by searching homepages as well as subpages of one depth level. For example, the below output represents companies that are associated with the term "Blockchain".

The Change Over Time function simply generates a time series for the difference between a data point today and a user defined frequency.

For example, the below output represents the DoD% change in LinkedIn head count for Lululemon.