#1 most frequent
United States of America
41.7%5 installations of 12
What is Causal?
Causal, from the company of the same name in London, presents a way to perform calculations, visualise data, and communicate with numbers. It helps build models faster, connect them directly to data, and share them with interactive dashboards and visuals. Suggested use cases are financial planning, planning CPC campaigns, track KPIs, or determine employee compensation.
Categories & Use Cases
Videos
Top Performing Features
Customizable dashboards
Customizable dashboards are dashboards providing the builder some degree of control over the look and feel and display options.
Category average: 8.7
Report Formatting Templates
Category average: 8.4
Drill-down analysis
Drill down analysis is the ability to get to a further level of detail by going deeper into the hierarchy.
Category average: 8.7
Areas for Improvement
Predictive Analytics
Predictive Analytics is the ability to build forecasting models based on existing data sets.
Category average: 7.5
Pattern Recognition and Data Mining
Pattern recognition and data mining mean the ability to recognize hidden patterns in large quantities of data.
Category average: 6.2
Delivery to Remote Servers
Ability to deliver reports to remote servers
Category average: 8.5
Who Buys & Uses Causal
#2 most frequent
United Kingdom of Great Britain and Northern Ireland
33.3%4 installations of 12
#3 most frequent
Canada
8.3%1 installation of 12
Super budget friendly software, would recommend to all startups!
Use Cases and Deployment Scope
Financial modelling made easy with Causal.
We had a very challenging use case which involved multiple products with multiple GTM strategies and Causal helped us to paint a picture of what this would look like whilst being able to dial up and dial down resources for what was required for each product to succeed.
The beauty of Causal is the ability to repeat model with ease.
We had a very challenging use case which involved multiple products with multiple GTM strategies and Causal helped us to paint a picture of what this would look like whilst being able to dial up and dial down resources for what was required for each product to succeed.
The beauty of Causal is the ability to repeat model with ease.
Pros
- Time Series Analysis
- Working with numerous variables in ranges
- Sensitivity Analysis
Cons
- Lags and volatility in range calculations
- AI driven error checking through a model
- Some small bugs which were always flagged and ironed out
Return on Investment
- Understanding key performance drivers
- Understanding the cost of HR to drive top line revenue
- Bringing to light ramp up time for new hires