Ecommerce Financial Model, built in Causal
  • Ecommerce Financial Model, built in Causal
  • Ecommerce Financial Model, built in Causal
  • Ecommerce Financial Model, built in Causal
  • Ecommerce Financial Model, built in Causal
  • Ecommerce Financial Model, built in Causal
  • Ecommerce Financial Model, built in Causal
  • Ecommerce Financial Model, built in Causal
Originally published: 15/06/2020 18:44
Publication number: ELQ-89476-1
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Ecommerce Financial Model, built in Causal

Model an ecommerce or transaction business using key order metrics to create probabilistic forecasts.

Description
Build a model for your SaaS or subscription business in a web app built for modeling, using key business assumptions as inputs (growth rates, repeat rates, acquisition costs, average order value, etc.), and builds out a full set of SaaS metrics (MRR, LTV, etc.) forecasted into the future.

Model Highlights
- The model is built in Causal, a web-based platform for building dynamic financial models.
- The model uses range-based assumptions for growth rates, churn rates, average revenue, and more to create probability-based distributions of revenues, LTV, and other performance metrics.
- By default the model is built for an average order value for all growth channels (Google, Facebook, organic, search, etc.) across all distribution channels (direct to consumer, retail, Amazon, etc.). The model can be edited to forecast specific SKUs, multiple growth channels, or specific distribution channels.
- The model's timescale is dynamic, and can be adjusted to be monthly, quarterly, or annual, and the length can also be edited to extend for any time period.
- Optionally, link to Stripe, Google Sheets or Google Ads to automatically pull in continuously-updated historical data and let the model use past data to forecast the future.
- Model results can be exported to Excel or Google Sheets for any additional analysis and presentation, and can be linked to Google Sheets for updating and use with other models
- Embed your model in Notion, Medium, Quora, and basically anywhere else that allows embedding.

How to use
- Access the model for free by downloading the .txt file and copy and pasting the link into your web-browser.
- Click on a variable in the Inputs section and change its value to see the rest of the model update.
- Many Inputs are ranges, allowing you to input a range of potential values for your assumption, and the model will show a distribution of the outputs based on the ranges in your inputs.
- To modify, edit, or save a version of the model to your own account, click Clone Model (top-right) to create a fully-customizable copy that you can use for your own business.

Edit Anything
- Edit anything; all formulas are unlocked for editing and transparency, add on or edit for your specific situation.
- The model can be edited simply by using Causal's natural-language based formulas.

Personal Support
- Live chat or email support anytime.
- Built by a professional financial modeler with 20 years of experience as an investor and running forecasting inside startups. Responsive, personal support — chat, call, or email — and available for custom services.

This Best Practice includes
1 customizable model in Causal

Taylor Davidson offers you this Best Practice for free!

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Further information

Create a model for an ecommerce business, using range-based inputs to create a probabilistic forecast of key performance metrics. Model is built in Causal, a web-based platform for financial modeling.

Modeling an ecommerce business based on average order value across all distribution channels

Modeling multiple SKUs or multiple distribution channels will require edits

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