Proprietary cloud-based SaaS Solutions.
Causometrix’s state-of-the-art SaaS cloud-computing technologies are based on our proprietary analytical engine and web-based BI tool. It delivers the speed and power needed for fast, accurate forecasting using multiple statistical models plus trend analysis. In addition, it offers the automatic detection of optimal forecast generation levels, seasonality detection and casual factor analysis.
With the fast, accurate and seamless integration of your data via our built-in API, you get the answers you need in a way that’s easy to use. Our highly intuitive interface offers fast, smooth drag and drop capabilities and we can easily add more custom solutions at any time, when you need them.
Because there is no hardware or software to purchase, there is no need to install, configure, test, run, secure or update anything. Your costs go down because you pay as you go, based on demand. Best of all, our services can be installed in as little as a few hours. Upgrades are automatic and it’s easy to scale up or down when needed. And, employees can have access to information anywhere they have an Internet connection.
|1||Software Licensing Costs||Required||Not Required|
|2||Software Implementation Consulting Costs||Required||Not Required|
|3||Time to Implement||6 to 36 months||1 to 4 weeks|
|4||Hardware Investments||Required||Not Required|
|5||IT Maintenance Costs||Requires Internal Resources||Included|
|6||Analytical Resources Costs||Requires Internal Resources||Optional|
|7||Annual Software Fees||Requires||Included|
|8||Software and Hardware Upgrades||Fees Required||Included|
|9||On-going customizations/upgrades||Requires Internal Resources||Minimal|
Causometrix SaaS provides the analytical resources you need without any major commitment of time or money.To prove it, we can get you set up for a free trial in less than a few hours with no hardware, licensing or implementation costs. All on-going maintenance, fees and upgrades are included in the subscription.
Causometrix’s analytical engine uses Bayesian techniques instead of best fit forecasting.