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Customer Story
How Marley Spoon levelled up their revenue modelling with Causal
Results
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We evaluated a range of financial modelling products, but Causal was the only platform that provided the modelling flexibility that we needed, without requiring external consultants to manage it. Causal has freed up my team’s time to focus on value-additive work with confidence in our numbers, and the Causal team are a pleasure to work with.”
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
Accurate revenue forecasting is crucial for Marley Spoon's business. As the company expanded, the business analysis team started reaching the bottleneck of spreadsheets.
Difficult to manage
With a complex cohort revenue model across multiple dimensions, the Excel file used to take 5 minutes to run, with no good way to keep track of different versions and run scenarios
Disconnected from data
Marley Spoon needed to pull actuals from a data warehouse, and push the outputs of the model into Looker
Difficult to share
The Excel file was too complex to directly share, so the team had to maintain separate Google Sheets displaying simple outputs for stakeholders.
Difficult to visualise data
The model’s complexity made it extremely cumbersome to create compelling visuals, and the disconnect of the output Sheets from the core model meant that visuals had to be manually updated as the model evolved.
Dina, Marley Spoon’s business analysis lead, was looking for a solution that would be flexible enough to support a bespoke and complex revenue model (including cohorts) connected to their data warehouse, while easily supporting multiple versions and scenarios, and being able to present directly to stakeholders.
Dina, Marley Spoon’s business analysis lead, was looking for a solution that would be flexible enough to support a bespoke and complex revenue model (including cohorts) connected to their data warehouse, while easily supporting multiple versions and scenarios, and being able to present directly to stakeholders.
The Solution
6.5 million formulas in Excel reduced 10,000x to a few hundred in Causal with version control.
Detailed Cohort Modelling
Our customer success team worked with Marley Spoon to build out their full revenue model in Causal. The model is at a weekly granularity, tracking each weekly cohort separately, spanning 6 years.
The model also spans 8 countries and 2 brands, letting Dina and her team drill down into any of the 12 combinations of 'country' and 'brand', as well as easily see aggregated views across the categories.
The total number of formulas required in the model was reduced from 6.5 million in Excel to a few hundred in Causal (a 10,000x reduction). Dina’s team are tracking multiple versions of the model separately using Causal’s Version Control functionality.
The model also spans 8 countries and 2 brands, letting Dina and her team drill down into any of the 12 combinations of 'country' and 'brand', as well as easily see aggregated views across the categories.
The total number of formulas required in the model was reduced from 6.5 million in Excel to a few hundred in Causal (a 10,000x reduction). Dina’s team are tracking multiple versions of the model separately using Causal’s Version Control functionality.
Data integrations
Marley Spoon’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 Marley Spoon’s revenue forecast.
Causal automatically refreshes the actuals on a weekly basis, and automatically compares them against different saved versions of Marley Spoon’s revenue forecast.
The Outcome
Causal has freed up the team's time to focus on value-additive work with confidence in their numbers.
More time for business partnering
Causal’s modelling functionality and data automation save Dina’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.
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