You have traction.
After a hard start, you have users. And you have a record of nearly everything they did, bought and said. It is scattered across systems that don't talk to each other.
What they did
Behavior
- Meta Ads Manager
- TikTok
- Google Analytics
- Your CRM and sales pipeline
What they bought
Outcomes
- How many people bought
- How much they spent
- Whether they came back
What they said
Language
- Support tickets
- Testimonials
- Reviews
- Social media posts and their comments
- Community conversations on Discord or Telegram
Somewhere in that pile are stories nobody has noticed yet.
What the engine learns from outcomes.
As patterns emerge across behavior, language and results, Arkwright can:
Connect what people do and say to what they end up doing.
Surface the messaging frames that move people.
Highlight the behaviors that predict positive outcomes.
Math that sees what the human eye misses.
Arkwright runs specialized machine learning algorithms: data-crunching formulas built to find structure in records too large and too tangled to read by hand. Two examples.
Logistic regression
Estimates the chance of an outcome from a set of clues. Will this customer renew? Will this trial convert?
Random forest
Combines many decision trees. Each tree asks a series of questions about past examples. Together, they uncover combinations that a single broad trend would hide.
A setup delay, for instance, might matter far more to first-time buyers than to people who already know the product.
It looks backward at what is known, then forward at what is likely.
- Which customers are likely to leave, while there is still time to do something about it.
- Which ads ended up creating the most loyal customers.
A pattern is a lead. A test is the answer.
When the evidence points to a promising change, the team tests it.
Randomized experiment
Assigns customers by chance to different versions of an offer or experience, then compares the results. Chance assignment is what turns a correlation into a cause.
Bayesian analysis
Updates the team's understanding as each new piece of evidence arrives, instead of waiting for one verdict at the end. Atma's free ebook explains how →
The question that matters
Is this change likely enough to produce a net profit?
A higher click count alone can't settle that. Arkwright helps answer it.
Below the clicks.
Those are the simpler possibilities. At a deeper level, Arkwright helps us understand the emotional context and the other-than-conscious factors that drive user behavior.
And it doesn't work alone. As an integral part of AtmaOS, Arkwright feeds two other Growth Bastards engines, telling them what to test, on whom, and where.