Table Trac

Table Trac R&D per Employee

The R&D Spending per Employee of Table Trac (TBTC) as of Oct 11, 2026 is 3,711.41 USD. In the previous year, R&D Spending per Employee was 1,458.94 USD — a change of 154.39% (higher).

R&D per Employee

3,711.41USD

YoY

154.39%

Last updated:

R&D Spending per Employee of Table Trac is 2018 3,711.41 USD. R&D Spending per Employee of Table Trac was 2017 1,458.94 USD. It decreases by 154.39% higher compared to the previous year.

The Table Trac R&D per Employee history

  • 3 Years

  • 10 Years

  • 25 Years

  • Max

R&D per Employee
Date
R&D per Employee
Jan 1, 2014
726.28 USD
Jan 1, 2015
689.28 USD
Jan 1, 2016
2,379.84 USD
Jan 1, 2017
1,458.94 USD
Jan 1, 2018
3,711.41 USD
The Table Trac R&D per Employee history
YEARR&D per EmployeeYoY
3,711.41USD+154.39%
1,458.94USD-38.70%
2,379.84USD+245.26%
689.28USD-5.09%
726.28USD—
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Table Trac Stock analysis

What does Table Trac do? Table Trac Inc is a leading technology company specializing in the development of gaming software solutions. The company was founded in Minnesota in 1995 and has since steadily grown. Table Trac is one of the most popular companies on Eulerpool.

Frequently Asked Questions about Table Trac stock

R&D Spending per Employee of Table Trac is 3,711.41 USD in 2018.

R&D Spending per Employee of Table Trac changed from 1,458.94 USD to 3,711.41 USD, representing a 154.39% change. The value is higher than the previous year.

On Eulerpool you can find the complete historical development of R&D Spending per Employee Table Trac since 2006 – with annual values, charts, and detailed analysis.

R&D per employee shows the research and development investment allocated per employee. Higher values indicate innovation-focused companies.

R&D Spending per Employee's USD is a key factor for investors. Changes in this metric can signal improving or deteriorating fundamentals, directly impacting the stock price. On Eulerpool, you can track R&D Spending per Employee's Table Trac historically and in real time.

Access this data via the Eulerpool API

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