Back in What it’s Worth, I introduced Percentage of Annual Revenues (%AR) as a tool for identifying expansion opportunities within a cohort of customers. 

The theory is: 

• Within a given industry, all companies have a similar set of persistent problems (e.g. acquiring customers, compliance, customer service, etc).
• The problem grows with revenue size, so while a larger company pays more for their customer service solution, as a percentage of their annual revenues it’s within range of what smaller competitors spend. 
• Outliers on the high- or low-end expose expansion opportunities. Whether that be potential price increases, revised packaging/tiers, or redefining the ideal customer profile (I know a dev shop using %AR as part of their client qualification process).
• We care about the total annual contract value here, not the specifics of how it was built (e.g. usage vs seat vs flat fee).

But I wanted some way to put it into practice, to quickly calculate and visualize %AR for my own business. So, earlier this week I built a small tool just for that, currently live at: acv-outlier.fly.dev/

ACV Outlier takes your customers’ company name and their contract value as inputs (either upload a CSV or enter manually), fetches an estimate of the company’s annual revenues from Gemini, and populates a scatter plot of %ARs across customer cohort. 

Below you can see my past 7 engagements, median %AR of 0.11% with some outliers at .40 (the yellow dots). 

This cohort includes late stage PE-backed and early stage VC-backed B2B SaaS firms, so given the breadth of company size and how few datapoints there are, the spread is suprisingly narrow – especially sans outliers. 

Even this initial visualization helps me tighten up my ICP and set a target for %AR moving forward (e.g. hmmm I’ve got one client at .2, how do I move the median there?)

If you’ll allow me to briefly brag about the technical architecture, as I think it’s very fun mix of modern internet technologies. 

• The ACV information is held in your browser’s private local storage, and is never transmitted to the database or Gemini. 
• All the CSV processing, calculations, and charting is handled by the browser as well.
• The annual revenue estimates are fetched by Gemini and then stored in a database for reuse and further refinement.

As subscribers and readers of For Starters, I invite you to help me beta test this, find your ACV Outliers and let me know what it says to you.

I also welcome feedback on your experience. Admittedly it’s a very new tool and relies on Gemini being available, responsive, and intelligent. 

So, if it’s not cooperating, email me with your dataset and I’ll run the analysis for you.