# Fabian Gmeindl

Fabian Gmeindl works on conversion optimization for online shops at DRIP. Gmeindl's work focuses on using A/B test results to help teams choose what to test, including through Apex by DRIP, a testing tool Gmeindl describes in posts.

- Profile: https://b2binfluencer.marketing/creators/fabian-gmeindl/
- LinkedIn: https://www.linkedin.com/in/fabiangmeindl
- YouTube: https://www.youtube.com/channel/UCT47ZTHddVV4QxcCVWiTzhA
- Topics: ecommerce, conversion-rate-optimization
- Based on 11 dated posts, latest 2026-10-09

## Summary

Fabian Gmeindl is most useful for B2B SaaS advice on test prioritization and conversion optimization.

- Best for: A B2B SaaS company building experimentation or conversion analytics features.
- Not for: Consumer brands buying sponsored Instagram posts for reach.
- Check first: Ask for audience data and sponsorship terms before assessing fit.

### Takeaways

- Apex's Likelihood to Win ranks ideas; it is not a literal probability.
- BLACKROLL delivery-time message results differed by placement above or beneath call to action.
- KICKZ mobile slider indicators change lifted conversion rate by 2.28%.

## Main ideas

- **Use what other shops have tested to decide what to build next**: Gmeindl argues that the expensive part of experimentation is not the test itself but the research, design, build and checking that come before it—and that teams routinely spend that effort on ideas that only sound persuasive. Apex by DRIP ranks candidate tests against results from comparable tests at other shops before that work starts. Gmeindl describes the tool as drawing on a database of 4.3 million tests from 151,000 shops collected since 2011. Each test idea receives a Likelihood to Win score with supporting results attached, helping teams order their backlog. The goal is narrow: identify what to test next and what supports that choice.
- **A prediction orders your backlog; it does not replace the experiment**: Gmeindl spells out the limits of the Likelihood to Win score: it is not a literal probability that a particular test will win, but a way to rank ideas within one shop. The experiment still has to run, with real customers deciding the outcome. They also distinguish evaluating an idea before it is built from changing how a live test runs after launch. A whitepaper promoted by Gmeindl explains what a prediction cannot tell you and how to reject weak hypotheses before development. They argue that new ideas without a track record should still have room in the backlog rather than being filtered out by the model.
- **Where an element appears can matter more than whether it appears**: Gmeindl makes a concrete case for testing placement. Rather than asking whether reassurance such as delivery-time information belongs on a product page, they test where it sits relative to the call to action and other reassurance points. Their takeaway: test where reassurance appears, not just whether it appears. They also limit their interpretation to what the test measured: the comparison was between placements and did not directly measure trust or hesitation.
- **Small mobile details are a real revenue lever on high-traffic shops**: Gmeindl argues that small mobile navigation details are worth testing because the changes are cheap and their measured effects can justify the work at a high-traffic shop. They link the reasoning to cognitive ease, suggesting that easier image navigation lowers cognitive load so shoppers process visuals faster and decide sooner. The test measured purchasing outcomes; the explanation is a hypothesis. Gmeindl also reports device-level outcomes separately, a recurring practice rather than a one-off.
- **Idea generation and prioritization are two separate steps**: Gmeindl turns testing knowledge into a productized asset, then draws a line between generating and prioritizing ideas. They describe a library of more than 45 A/B test ideas in Notion, each with a hypothesis, the psychological mechanism behind it, key takeaways and available win-rate predictions. The ideas span product pages, navigation and checkout. Finding an idea is the first step; deciding which deserves the team's time comes next. The library feeds hypotheses, while the prediction tool helps prioritize them.

## Work to read

- [Why the same test works in one shop and fails in another (Apex by DRIP)](https://www.linkedin.com/posts/fabiangmeindl_more-revenue-from-the-traffic-you-already-activity-7513966500953784320-16Ft)
- [KICKZ: mobile slider indicators, +2.28% conversion rate](https://www.linkedin.com/posts/fabiangmeindl_a-small-mobile-change-lifted-conversion-rate-activity-7514272422343262208-hgi-)
- [BLACKROLL: where delivery-time reassurance sits matters](https://www.linkedin.com/posts/fabiangmeindl_adding-delivery-time-information-lifted-conversion-activity-7513903038613450752-m9y-)
- [A library of 45+ test ideas with hypotheses and predicted win rates](https://www.linkedin.com/posts/fabiangmeindl_45-ab-test-ideas-with-hypotheses-psychological-activity-7514220007971606528-JL9M)
- [A whitepaper on Prediction-Based Experimentation, including its limits](https://www.linkedin.com/posts/fabiangmeindl_make-more-revenue-from-the-traffic-you-already-activity-7513844561278988288-TyRK)

## Brand relationships

- **DRIP** (disclosed_partnership)
- **KICKZ** (mention)
- **BLACKROLL** (mention)

## Partnerships

The five reviewed LinkedIn posts document no sponsorship packages, brand-partnership formats, rates, exclusivity terms or partnership inquiry route. They do document DRIP’s commercial offers: a whitepaper on prediction-based experimentation and a testing library of 45+ A/B test ideas, each with hypotheses, psychological mechanisms and predicted win rates, both available by direct message; and Apex by DRIP, a testing tool the posts describe as telling you which test wins before you build it. Apex targets shops doing at least six figures a month in revenue, and the posts direct prospects to request a demo, not to contact DRIP about partnerships. DRIP also offers managed execution: the team builds, checks, launches and evaluates tests for shops that want help running them. For a partnership plan, ask Gmeindl directly about sponsorship availability, lead times, deliverables, approval steps and pricing.

## Alternatives

- Harry Molyneux: A better choice when a platform's users are DTC and Shopify brands that need a working CRO program, especially smaller stores that cannot reach A/B test sample size. Molyneux writes about testing landing pages directly on Meta instead of running an underpowered A/B test, shifting buyers to subscriptions without hurting overall conversion, keeping quiz funnels short, and placing trust signals where checkout doubt peaks. There is also a concrete Black Friday sequence: acquire email subscribers at normal CPMs before Q4, lock the offer before design, build one landing page per offer and ad angle, and kill losing variants fast. For a SaaS product selling into consumer commerce and seeking advice on what its customers do week to week, this is closer to the work than Gmeindl's platform-side material.
- Hayan Khan: A better choice when the engagement needs design-side conversion work rather than experimentation methodology. Khan's posts cover page and navigation design: leading with the product benefit, answering purchase barriers such as convenience, taste, and cost up front, adding reasons to believe, and organizing a Shopify menu around customer problems such as itching, joint health, or anxiety rather than product names. The redesign post reports conversion moving from 1.8% to 5.8% and revenue from $18,000 to $58,000. This is the closer match for a platform whose customers struggle with page-level conversion, or a design-led redesign project that needs CRO judgment applied to layout and copy.
- Tom Conzelman, PMP, CCM, LEED AP: A better choice only when the need is construction and real-estate project advisory, not conversion optimization. Conzelman's posts cover a manufacturing facility build in Franklin County, construction risk, design defects, change orders, and building codes. A B2B SaaS company assessing a conversion-optimization advisor would not find CRO or experimentation evidence here.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/