Sorry, that 𝗁̶𝖺̶𝗅̶𝖿̶ ̶𝖻̶𝖺̶𝗄̶𝖾̶𝖽̶ brand new AI feature won’t save your product…
There I said it.
In fact, adding random features that none of your customers asked for really does more to alienate than attract.
When we started ARRow, we had a lot of assumptions about who our product would be for and how we would grow it.
For example, we thought we would mostly be working with smaller orgs in a growth spurt where the manual methods of calculating SaaS metrics were no longer cutting it.
But once ARRow actually went live, what ended up being the guiding hand for our product roadmap was the next most pressing challenges faced by our customers.
In manufacturing, they call this “removing the bottleneck”. And typically once you work on your main bottleneck, another one appears either upstream or downstream.
And it is exactly what we witnessed with our clients:
First, a lot more were using spreadsheets than we thought. And in much later stages of growth.
The initial challenge everyone had was:
✅ they had data,
✅ they had a CRM,
❌ but they needed a much faster and easier (and incredibly more reliable!) way of calculating and reporting on their revenue metrics.
💪 Great! Check and check. We can help with that!
ARRow gets implemented, and now there is trust that the metrics are being calculated accurately and consistently.
But all of a sudden… a new challenge appears!
⏸ Let’s pause for a second, to allow for a shameless brag: one of things I’m most proud of in regards to ARRow is how quickly and easily it can be up and running, and provide accurate data (a few hours).
And a recurring theme immediately post-implementation was ARRow identifying and correcting historical errors that stemmed from how the data was initially captured in (manual, sporadic, missing data points etc.).
We even have clients that initially deployed in a sandbox and were able to identify (and fix) issues in their prod data. Talk about time to value!
⏯ Back to our bottleneck discussion: the pain point had now shifted upstream to the quality of the data being used in the metrics and how to ensure that sales is using a standardized (and approved) pricing in their open opportunities to allow for clean flow of accurate data.
Like a CPQ tool.
If you’ve ever looked into some of the top CPQ solutions, you know the implementation process can be lengthy (talking 9 months to a year) and the cost can be prohibitive for most orgs.
So we set out to build a CPQ tool that follows the same idea of being native to Salesforce, works where our clients’ sales and customer success teams are, has the same access to data and security specs.
But a tool that is way more cost effective, provides the same results and implements so much faster (… think hours not months).
Because that’s what we heard from our clients. Not trendy, not cool, not viral.
Just practical and useful to our ICP.
Any one else got reminders for all of us planning out our 2025?