Customer Story

How Humaans uses Causal as the single destination for all numbers and data

B2B SaaS
Series A


Nobody in the Finance/Data function wants to spend their time pulling various threads of data together, they want to be able to spend that time asking what the data means for their business — Causal allows me to do that. I wouldn't have the time to do my day job without Causal.
Robbie Osborne
Head of Finance
Greater confidence in accuracy
100x fewer formulas and built-in scenarios reduce errors.
30 hours saved every month
Time saved via live data integrations and modelling automation.
More time for business partnering
Time freed up to focus on strategic, value-add work.

The Problem

Robbie wears a lot of hats, but was having to spend an outsized amount of time managing the Excel model and keeping it up-to-date.
Difficult to use in meetings
The Excel model was difficult to tweak on-the-fly during live planning discussions, and comparing multiple scenarios was difficult.
Time-consuming data import
Bringing in actuals each month across multiple entities and the data warehouse was a nightmare. Robbie spent more time bringing the data in vs analysing the results.

On top of that, there was no way to 'drill down' and see the individual transactions behind each number.
Difficult to manage
As an early-stage company, the Excel model was constantly evolving. But pivoting the model by a new dimension (e.g. Segment instead of Plan) required almost a complete re-build each time.

Doing roll-ups in the right way (e.g. daily, weekly, monthly, quarterly) was cumbersome, and trying to build in scenarios blew up the complexity.

The Solution

Multi-dimensional models with live data feeds from QuickBooks, Xero, BigQuery, and Humaans.
Live data integrations
Robbie has data coming in automatically at different frequencies from BigQuery (daily)
Data integrations
Humaans’s model is using Causal’s live data integrations to pull 2 years of weekly cohort data for each country from their data warehouse.

Causal automatically refreshes the actuals on a weekly basis, and automatically compares them against different saved versions of Humaans’s revenue forecast.

The Outcome

Spending less time on number crunching and more time on data-driven decisions and analysis to drive the business forward.
More time for business partnering
Causal’s modelling functionality and data automation save Robbie’s team 30 hours per month. This has freed up their time to focus on the strategic work that adds value to the business — analysing performance and working with business partners to make better decisions.
Greater confidence in accuracy
With drastically fewer formulas and dedicated functionality for scenarios and versions, there’s much less complexity to manage in Causal, making it much less likely to make hidden errors. Causal’s plain-english formulas also make every model auditable by anyone, significantly reducing the chance of formula errors and reducing the time required to double-check work.