Causal

Cryptic User Interface: Some users have found the user interface of Causal to be cryptic, especially for non-tech users who may struggle with the formulas. They mentioned that this has made it difficult for them to navigate and use the software effectively.

Steep Learning Curve: Several users felt that there is a learning curve when transitioning from Excel to Causal. Although they acknowledged that once they became familiar with the platform, they were able to rapidly iterate on their models, they still expressed that the initial learning curve was steep and required time and effort to overcome.

Price Point and Pricing Model: A number of users considered the price point of Causal to be steep, particularly in certain regions like South Africa where more affordable options are desired. They also mentioned that the pricing model needs improvement as some users feel odd being on the free plan despite relying on Causal for their business planning.

Users have made several recommendations based on their experiences with Causal. The most common recommendations are as follows:

1. Use Causal for complex calculations and easy-to-read results: Users find Causal to be a valuable tool for performing complex calculations and presenting the results in a readable format. This makes it a suitable alternative to Excel for FP&A tasks.

2. Explore and invest time in learning Causal: Many users suggest experimenting with Causal to fully understand its capabilities. They recommend investing time in learning how to use the platform effectively, as it can provide significant benefits in terms of data analysis and modeling.

3. Seek support from the Causal team: Users commend the responsiveness and helpfulness of the Causal team. They recommend reaching out to them for support, especially when it comes to model building queries and general assistance.

In addition to these common recommendations, users also suggest connecting Causal with other tools like Xero for expense tracking and trying it for SAAS-based platform calculations. Users emphasize the importance of planning and studying the product before starting, as well as utilizing tutorial videos for a better understanding of its functionalities. Overall, users find that starting with simple models and gradually adding complexity is an effective way to learn Causal.