Improve Your Testing Success with This [Unique] Calculator Immediately 🚀

I recently teamed up with the brilliant Ishan Goel, Founder of Thinking Bell, to create a tool we wish every testing team had at their fingertips: a Reverse MDE Calculator.

Here are the problems: too many teams run experiments without knowing whether their setup could ever produce a reliable result. Maybe they don’t have enough traffic. Maybe they expect results faster than their data can support. Or maybe they just pick a test window and cross their fingers. The outcome is the same: wasted weeks, inconclusive data, and the sinking feeling that the effort didn’t actually teach you anything.

Most testing calculators out there don’t really help with this.

• Some ask you to enter an MDE up front — but that forces you to assume the size of the effect before you even know what your traffic can support.

• Others only give you sample size requirements, without telling you how long a test would actually need to run.

• And several leave out timelines entirely, which is the most important part for planning.

This calculator flips the process. Instead of guessing, you start with what you actually know: your traffic. From there, it works backwards to tell you two key things:

1. How many weeks you’d need to run a test at that traffic level

2. The smallest lift (MDE) you could reasonably expect to detect

For anyone who’s less familiar with testing lingo: MDE simply means the smallest change you can spot with confidence. If your traffic only allows you to see big swings (say, a 20%+ difference), you’ll know right away that chasing a smaller lift just isn’t worth your time. On the flip side, if your traffic volume is large, you can spot much smaller improvements and still trust the result.

For the more technical crowd: the calculator bakes in the fundamentals of statistical power and significance without forcing you to do the math by hand. It’s a quick way to sanity-check test feasibility and avoid underpowered experiments.

The big benefits: before you spend weeks designing, launching, and analyzing a test, you’ll know whether it has a shot at producing a meaningful, trustworthy result. That means fewer wasted cycles, fewer inconclusive readouts, and more confidence that the tests you do run will drive value.

Shopify Plus Users: A Guaranteed Way to Boost Checkout Subscriptions and LTV

I’ve just partnered with Dataships, and I don’t say that lightly. I only recommend tools I know are genuinely useful — ones that actually make my work (and yours) more effective. Right now, it’s only available for Shopify Plus stores (and yes, I keep bugging them to roll out a Webflow version so I can use it on Chirpy’s site too 😉).

Here’s what makes it different: Dataships automatically adjusts the opt-in experience based on each shopper’s region. That means every customer gets the right consent flow for their location — GDPR in the EU, CCPA in California, simple opt-ins elsewhere. The result? More subscribers captured, zero compliance headaches, and higher lifetime value.

And the best part: they don’t just claim it works. They’ll prove the results to you.

Here’s their process:

1. Cohort Analysis: They’ll analyze your Shopify data to compare the lifetime value of subscribed vs. non-subscribed customers.

2. A/B Test: They’ll run an A/B test with the Dataships app, comparing Shopifyʼs static consent collection to Datashipsʼ dynamic approach.

3. Incremental Revenue Tracking: They’ll track opt-in rates, unlocked contacts, and incremental revenue, providing a data-backed calculation of Datashipsʼ impact on your bottom line.

They price based on unlocked value.

I’m a big fan. Also, their team is a joy to work with in my experience.

Book Recommendation 📚

This is from my personal life. I think it could be applicable to business in some ways and help people with communication. I think every adult needs to read it.

👉 Get the book on Amazon.

That’s all for now.

Stay Chirpy,

Haley 🤘