Crossing Over on

This week was my “2007 for solar”, I crossed over.

Note: this was originally published at

Until around 2007, the total energy that a solar panel could produce during its usable life was less than the energy it took to manufacture and deliver that same solar panel. 2007 was the crossover year because it meant that all the investment was now proving its value to the world environmentally and economically. Naturally it’s been all in the right direction since then because solar is good: it’s clean and efficient. That initial period of inefficiency has been well-surpassed and earned back many times over since then - the investment paid off for manufacturers, resellers and installers alike who joined the bandwagon early.

Such is the case with almost any new technology that is undergoing development and adoption concurrently.

As many people know, I am huge fanboy of Causal. I’ve loved the product since my friend showed it to me over a year ago, and I’ve spent a bunch of time with the team as they have continued to develop and refine it.

This week was my “2007 for solar,” I crossed over. The time I have spent in the product paid off and my model building efficiency in Causal surpassed that of Excel. As a former investment banking analyst and self-proclaimed Excel junkie, I’d like to think this means something. The good news: I only had to build a handful of models for this to happen.

I have been building SaaS models (a lot of them) regularly since 2014 and the link below is a set of videos showing the live construction of a SaaS model, including ratios, metrics and outputs that is primed for customization and live data integration for almost any SaaS company; all built in Causal.

Take a look and let me know what you think. Check out the product and play with a few models. Causal is canvas for numbers that allows you to gain intuition around numbers that was previously reserved for analysts and excel junkies. This product isn’t just built for those people, it’s built for everyone.

The model in the videos is here: (Click “use this template” to play with the backend)

In my work I build a lot of unit economic models that develop into detailed operating plans, and Causal has become my first step and takes models all the way from business model ideation to full-blown FP&A models.

Just like how 13 years later, solar is an obvious choice for efficient energy, in a short period of time you may see the same for Causal and modeling.

Some killer features to look out for:

Input Ranges - instead of locking an assumption at 10%, you can simply write “9% to 11%” in the same field and all outputs and charts will be displayed as a range as well.

Visual Dependencies - Rather than toggling F2 or hitting ctrl+[ in Excel, Causal automatically shows the dependencies - both forward and backward (hit alt or option to toggle back and forth).

Live Data Linking - You can automatically link data from a Google sheet or a variety of other sources into variables in the model. As new data comes through (just like it did when everyone closed out October on Friday), the entire Causal model will auto-advance the assumptions to the next time step and replace the previous calculation with historical data.

Aggregation Functions - When creating an ARR build or any other waterfall, you have four types of aggregators over time: initial, final, sum and average. Being able to say “Beginning ARR” should be aggregated as initial values whereas “New ARR” should be aggregated as a sum saves a lot of inefficient formulas and formatting in Excel when you want to go from Months->Quarters->Years.

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