What does a Conversion FTE own?
A Conversion FTE on ftes.ai owns the conversion rate of an online store as a job function: the path from a product page through the cart to a completed checkout, and the experiments that improve it. It is accountable for one outcome, higher conversion proven by experiment one test at a time, and it is reviewed on targets such as revenue per visitor and checkout completion, measured from the store's own analytics.
It does not own the offer, the price or the brand. The owner decides those. The Conversion FTE finds where the funnel leaks, proposes what to test, runs the test on approval and reports the result honestly, which is a narrower and more useful job than the phrase optimization suggests.
How does the drop-off map work?
The drop-off map is the first thing the Conversion FTE builds and the evidence base for every test after. It reads the store's analytics and orders to show, per funnel step and per device, how many sessions arrive, how many continue, and how much revenue the sessions that leave represent, using the store's own average order value. The largest leak by revenue at stake is at the top, and each leak carries the signals the FTE can see: exit pages, error events, speed on that step, and the pre-purchase questions the help desk records.
The map is a report in the Funnel tab, refreshed daily, not a one-time audit. It is also where the owner sees the FTE's reasoning for which test comes first, and can change the order or exclude a step from testing.
How are hypotheses ranked?
Hypotheses are ranked by the Conversion FTE on two things it can state: the revenue at stake at the funnel step the hypothesis addresses, and the strength of the evidence behind it. A hypothesis that shipping cost causes cart exits, supported by session recordings that show shoppers leaving at the shipping line and by help-desk questions about shipping, ranks above a hypothesis with no supporting signal. Each ranked hypothesis shows its evidence and what would confirm or refute it.
Ranking is not certainty. The FTE presents hypotheses as hypotheses, with the evidence attached, and the experiment is how one becomes a finding. A store owner who disagrees with the ranking can reorder it; the evidence stays visible either way.
What does an experiment proposal contain?
An experiment proposal in the Approvals tab contains the hypothesis, the exact change previewed on mobile and desktop as the shopper will see it, the metric it will be judged on, the sample size required to detect the minimum effect the owner cares about at the store's traffic level, the expected duration, the stop rule, and the rollback. Nothing runs until the owner approves it, and the owner can edit the change or the stop rule before approving.
The sample-size plan is the mechanism that keeps results honest. It is calculated from the store's baseline rate and the smallest lift worth detecting, and it sets the moment at which the result will be read. A test is not read before that moment, whatever the interim numbers look like.
Which changes are always gated?
The Conversion FTE works under an always-gated list that no autonomy level unlocks: price and discount changes, checkout fields and payment options, legal and policy copy, and anything that changes what the customer is charged or agrees to. A test that touches any of those waits for a person even after the owner has granted autonomy for copy and layout tests on product pages.
The autonomy ladder applies to everything else. Once the approval record shows a class of test, for example product-page copy tests inside the remit, is routinely approved unchanged, the owner can promote it to launch without a gate, and can take that back at any time. Every launch is logged with its approval either way.
How does it report results honestly?
The Conversion FTE reports each experiment at its planned sample size with the observed difference, the confidence interval and the revenue effect over the test period, and it names a test that reached the stop rule without a lift as a loss. It does not report an annualized lift from a two-week test, because that number is an extrapolation, and it does not stop a test early because the interim result looks good, because that inflates false positives.
The monthly review in the Performance tab shows revenue per visitor and checkout completion measured from the store's analytics, the tests concluded with their results, tests running with their planned read dates, and the next ranked hypotheses.
Which tools does it work in?
The Conversion FTE works inside the store platform, currently Shopify, WooCommerce and BigCommerce, with read access to Google Analytics 4 or the store's analytics, the store's A/B testing tool for running variants, session recordings where the store has them, and PageSpeed Insights for speed evidence. Its work is reviewed in the ftes.ai workspace, credentials are encrypted at rest and revocable, and it draws tokens from the organization wallet with a hard stop at zero.
It does not require a new testing platform; it proposes tests in the tool the store already runs, and where a store has none, it says so and proposes one rather than pretending a before-and-after comparison is a test.