Who Bandit Won't Work For

Most software pages tell you their product is for everyone. Ours isn't, and pretending otherwise would waste your trial and our time.

So here's the honest list. If you see yourself in the first half, close the tab. If you see yourself in the second half, keep reading.

Not a fit: sites with too little traffic

Bandit learns from conversions. Every signup, add-to-cart or purchase is a vote for the version that earned it, and Thompson sampling shifts traffic toward whichever version is collecting votes fastest. No votes, no learning.

The problem with a handful of conversions is noise. A useful rule of thumb: the random wobble in a conversion count is roughly the square root of that count. So the relative noise is about 1 ÷ √(conversions).

• 25 conversions on a version: √25 = 5, so the count can swing about ±20% by luck alone.
• 100 conversions: about ±10%.
• 400 conversions: about ±5%.

Most real improvements are single digits, +5%, +6%, +7%. To see a 5% difference through the noise, each version needs a few hundred conversions. With two or three versions, that puts the rule of thumb at a few hundred conversions a month on the goal you care about, at minimum. This is back-of-the-envelope statistics, not a product limit. Below that, the algorithm will still pick something, but it's mostly picking on luck, and it'll take months to stop.

If you get ten sales a week, you don't have an optimization problem. You have a traffic problem, or a positioning problem, and both are better solved by talking to customers than by splitting your homepage.

Not a fit: teams that already run a real experimentation program

If you have a dedicated experimentation, growth or data team running Optimizely, Statsig, Eppo or a homegrown platform, you've probably made deliberate choices: your own stats engine, your own guardrail metrics, pre-registered hypotheses, a review process. That's a good setup. It's also not what Bandit is for.

Bandit makes the statistical decisions for you. It moves traffic on its own, it decides when a winner is clear, and it keeps adapting. A team that wants to own every one of those choices will find that frustrating, and they'd be right to. You don't need us to babysit tests. You already have people who do it well.

Not a fit: no clear conversion event

A bandit needs to know what "better" means. If your site is a brand page, a content property where success is "people seemed to like it," or a sales process where the only conversion happens on a phone call three weeks later, there's nothing for it to learn from. Pick one measurable action first. Then come back.

Not a fit: you need a fixed split or a fixed-horizon readout

Some flows, like regulated disclosures, pricing tests with legal review, or anything that has to show exactly 50/50 for compliance, need splits that don't move. And some stakeholders want a classic report: "we ran it four weeks, here's the p-value." Adaptive allocation is the opposite of both by design. Bandit gives you plain-language results and credible intervals, but if your board needs a pre-registered fixed-horizon test, use a tool built for that.

Who it is for

The sweet spot is narrow and very real: enough traffic to learn, but no experimentation team to run it.

Marketing agencies running tests across many client sites. You don't have a statistician per account, and you can't babysit twenty dashboards. You need every client site improving in the background and a result you can paste into a client report in one sentence.

Mid-size Shopify stores. Thousands of sessions a week and real revenue on every step, from product page to cart to checkout, but no growth team. The founder or one marketer owns "conversion" along with ten other things.

Lean SaaS growth teams without a stats person. Signup, activation and upgrade pages all matter, and nobody on the team wants to argue about statistical power on a Tuesday.

Why those teams buy

Not for one big win. For a lot of small ones that stack.

Funnels multiply. A +5% lift at signup, +6% at activation and +7% at checkout is 1.05 × 1.06 × 1.07 ≈ 1.19, or about +19% in end conversion. Those numbers are illustrative math, not customer data. No single step moved much, and the result is double digits.

The catch is that each of those small lifts is slow to prove with a fixed 50/50 test, so teams without dedicated people usually call them flat and move on. Bandit doesn't wait for a verdict before acting. It leans traffic toward what's earning while it learns, draws new variants with AI when a step plateaus, and keeps going after you've stopped looking.

That's the whole pitch: if you have the traffic and not the team, Bandit captures the single-digit lifts you'd otherwise leave on the table, step after step, without anyone babysitting a test.

If that's you, start a 30-day trial. After that, Bandit is $299 a month per site. If it's not, we hope this saved you an afternoon.