Friction Audits, But Testable
Proof, not claims—classic persuasion triggers, re-tested live with digital twins.
Dooley describes a trigger; we re-tested it with digital twins.
Does a clunky checkout really cost you conversions?
Roger Dooley builds a central argument around this question in his book Friction (2019): every extra click, every additional required field, every extra step between cart and confirmation lowers the odds that a purchase gets completed at all. His concrete recommendation for checkout: offer guest checkout and avoid forced account creation wherever you can.
This recommendation is close to consensus in e-commerce circles—but how strong is the effect really, once you measure it on a concrete example? We wanted to know whether Dooley’s rule of thumb reproduces as a concrete number with digital twins. So we ran the same €89 cart through two different checkout flows: Flow A with 5 steps—create an account with email confirmation, address, payment method, newsletter opt-in, confirmation, 12 required fields in total—and Flow B, a guest checkout with just 2 steps: address and payment on one page (5 fields), then confirmation.
How did we test this?
In the first run, twins made a forced choice (“Which checkout would you rather complete?”) and additionally estimated an abandonment probability from 1 to 10 for each flow. Because only 9 of 20 responses gave a number at all, we asked differently in a second run: we had each twin picture 10 purchases and estimate, out of 10, how many they’d abandon per flow.
The Method: n = 10 digital twins (DACH consumer panel, ages 25–60) made a forced choice between two text-described checkout flows for the same €89 cart, across two runs with reversed order (20 twin responses). All 20 responses contained a clear A/B choice in prose, 0 parse errors; the additionally requested abandonment probability (1–10 per flow), however, produced an explicit number in only 9 of 20 responses—where it did, the mean was 8.3 for Flow A versus 4.3 for Flow B, a directionally consistent but n=9 side finding, still well under the series’ 15-usable bar for treating a rating delta as publishable, so it stays a side note rather than the headline. The panel responded in German; quotes are translated. Re-tested 2026-07-05 with the exact English wording shown here, on the same panel of 10 German-consumer twins (questions asked in German, stimuli in English), using neutral placeholders in the pick/rating answer-format example instead of the original run’s real letter and direction-hinting numbers—a methodology improvement over the original German run, which is one reason magnitudes differ between the two editions. The German edition of this article reports the original German-stimulus run. The forced-choice pick share itself (0% Flow A / 100% Flow B) came out identical in both language runs.
Because this first rating question returned too few numbers, we replaced it with a follow-up allocation measurement: the same 10 twins, across two more oppositely ordered runs, estimated out of 10 purchases how many they’d abandon per flow (again 20 twin responses, all 20 usable). No meaningful position effect shows up here: the means differ by just 0.4 points for Flow A and 0.1 points for Flow B between the two orders. Flow B’s abandonment also came out notably lower than in the German-stimulus run (0.55 vs. 1.4 of 10) under this anchor-neutral version of the question, while Flow A held roughly steady (7.0 vs. 7.05 of 10)—plausibly because removing the direction-hinting example numbers let twins gravitate toward near-zero for the option they already found clearly better, rather than a language effect per se.
Beate Hofmann, 58
Project manager twin* · Stuttgart · University degree
“I’m Beate Hofmann, a project manager from Stuttgart. Since my divorce I’ve found new stability with a new partner, and even though I’m living with chronic back pain and an active cancer diagnosis, I stay active with daily exercise and feel deeply satisfied with my life.”
What makes this twin distinct: I hold strong private religious beliefs without attending church, I’m deeply skeptical of politics and the economic situation, and I guard my data so carefully that I’ll pass up a discount rather than share it.
* Digital twin: an AI simulation based on a real person’s profile — 68+ survey items, a full psychographic profile (values, demographics, behavior). Not a real person.
Sabine Wagner, 56
Nurse twin* · Leipzig · Upper secondary education
“I’m Sabine Wagner, a nurse at a hospital in Leipzig. I’m married and live with my husband, but between 40-hour shift work and running the household, I have almost no time left for myself.”
What makes this twin distinct: My faith isn’t just tradition — it’s an active source of strength for a demanding job, I place strong trust in the police and the justice system, and despite my packed hospital schedule I still volunteer for charitable causes.
* Digital twin: an AI simulation based on a real person’s profile — 68+ survey items, a full psychographic profile (values, demographics, behavior). Not a real person.
Kathrin Baumann, 32
Teacher twin* · Munich · Postgraduate degree
“I’m Kathrin Baumann, a primary school teacher from Munich. I’m married with two young children, and life right now is turbulent between school and a young family — exercise has taken a back seat.”
What makes this twin distinct: I trust people deeply and tend to look for the good in them, I lean politically left and feel close to the Greens, and I consistently boycott products for sustainability reasons even though politics otherwise takes a back seat in my daily life.
* Digital twin: an AI simulation based on a real person’s profile — 68+ survey items, a full psychographic profile (values, demographics, behavior). Not a real person.
Melanie Schubert, 33
Bank clerk twin* · near Frankfurt · Advanced vocational education
“I’m Melanie Schubert, a bank clerk at a large company near Frankfurt. I’m married and live with my husband, though occasional back and neck issues slow me down a bit in daily life.”
What makes this twin distinct: I’m considerably more risk-averse than most people around me, I avoid leadership roles and deliberately limit my own time online even though I’m perfectly capable with technology — order and reliability matter more to me than trying new things.
* Digital twin: an AI simulation based on a real person’s profile — 68+ survey items, a full psychographic profile (values, demographics, behavior). Not a real person.
Lukas Sander, 33
Retail twin* · Dortmund · Postgraduate degree
“I’m Lukas Sander, a retail employee with team-lead responsibility in Dortmund. I’m married with three children aged two, four, and seven — between a 40-hour work week and a full family life, I feel very satisfied and firmly in control.”
What makes this twin distinct: Even though I’m security-oriented and risk-averse, I strongly support minority rights, including LGBTQ rights, and want a strong, socially active government — and my postgraduate degree gives me an unusual outside perspective on my retail job.
* Digital twin: an AI simulation based on a real person’s profile — 68+ survey items, a full psychographic profile (values, demographics, behavior). Not a real person.
Anke Schumann, 48
HR twin* · Hamburg · University degree
“I’m Anke Schumann, an HR officer at a mid-size company in Hamburg. I’m married, have two sons, and feel deeply fulfilled and settled in my life.”
What makes this twin distinct: I place strong trust in parliament and the justice system even though the economic situation leaves me dissatisfied, I champion income equality and minority rights, and yet I also see obedience and respect for authority as core parenting values — a contradiction I notice in myself.
* Digital twin: an AI simulation based on a real person’s profile — 68+ survey items, a full psychographic profile (values, demographics, behavior). Not a real person.
Sören Lindner, 30
IT twin* · Cologne · Advanced vocational education
“I’m Sören Lindner, an IT administrator at a large company in Cologne. I’m not married and live with my partner — my childhood was shaped by financial hardship and family conflict, which made me more risk-tolerant and determined as an adult.”
What makes this twin distinct: I’m unusually risk-tolerant and drawn to leadership, I protest and donate for causes I believe in, I guard my data strictly despite my strong tech affinity, and I actively oppose workplace inequality for women.
* Digital twin: an AI simulation based on a real person’s profile — 68+ survey items, a full psychographic profile (values, demographics, behavior). Not a real person.
Tobias Hübner, 35
Mechatronics twin* · Essen (Ruhr area) · Upper secondary education
“I’m Tobias Hübner, a mechatronics technician at a mid-size electronics manufacturer in Essen, in the Ruhr area. I’m not married and live in a large six-person household with my parents and younger relatives — chaotic, but a strong source of security for me.”
What makes this twin distinct: I put several hours a week into caring for relatives and neighbors rather than outward-facing social activities, I consistently reject tracking cookies, and I still vote regularly even though I feel my vote carries little real weight.
* Digital twin: an AI simulation based on a real person’s profile — 68+ survey items, a full psychographic profile (values, demographics, behavior). Not a real person.
Dennis Altmann, 41
Sales twin* · Düsseldorf · University degree
“I’m Dennis Altmann, a sales rep at a mid-size wholesale company in Düsseldorf, and I travel frequently for work. I’m married with three children — my own childhood was marked by financial strain and conflict, which is why I want a more stable, harmonious home for my own kids.”
What makes this twin distinct: Unlike Düsseldorf’s generally liberal environment, I place high value on clear rules, order, and traditional parenting values like obedience and respect for authority, I meet strangers with healthy skepticism; my father originally came from Turkey.
* Digital twin: an AI simulation based on a real person’s profile — 68+ survey items, a full psychographic profile (values, demographics, behavior). Not a real person.
Jürgen Krause, 59
Accountant twin* · Berlin · Upper secondary education
“I’m Jürgen Krause, an accountant nearing retirement in Berlin. I’ve never married and live with two older relatives I care for about 15 hours a week, while dealing with back and joint pain and occasional severe headaches.”
What makes this twin distinct: I’m socially and culturally conservative, value tradition and respect for authority, and feel little connection to the European idea despite living in a cosmopolitan city — yet I still vote SPD because social and income justice matter to me.
* Digital twin: an AI simulation based on a real person’s profile — 68+ survey items, a full psychographic profile (values, demographics, behavior). Not a real person.
* Digital twins are AI simulations based on real person profiles — not real people. Click a twin to see what it is based on.
What we tested
Flow A · 5 steps, forced account · 7.0 of 10 abandonments
Create an account with email confirmation, address, payment method, newsletter opt-in, confirmation. 12 required fields.
Flow B · 2 steps, guest checkout Winner · 0.55 of 10 abandonments
Address and payment on one page (5 fields), then confirmation.
Why do all the twins choose the guest checkout?
Roger Dooley names guest checkout in Friction (2019) as one of the most effective moves against cart abandonment. Digital twins put a number on it in our test: on average, they abandon 7.0 of 10 purchases at the 5-step checkout with forced account creation, versus only 0.55 of 10 at the 2-step guest checkout (range 4–8 vs. 0–2)—a friction score of 6.45 points, with fully usable answers from all 20 twin responses. The direct choice confirms the same picture: 20 of 20 responses—across both counterbalanced runs, regardless of order—additionally picked Flow B, the 2-step guest checkout, in the forced choice. Not one twin chose Flow A, the 5-step checkout with forced account creation.
The reasoning clusters around two themes: time and data minimalism. “Flow A, with its 12 required fields and email confirmation, would probably annoy me enough to abandon,” says Twin “Lukas Sander” (digital twin, DACH consumer panel). For him, the checkout is simply too long to see through to the end.
For Beate Hofmann, the concern runs deeper than plain impatience: “The hand-holding in Flow A, with account creation and email confirmation, feels patronizing and like a pure waste of time,” says Twin “Beate Hofmann” (digital twin, DACH consumer panel). Here, forced accounts register as more than lost time—they read as being talked down to, a tone that comes through even more sharply in the allocation follow-up than in the original choice.
Sören Lindner, surveyed in both measurements, puts it most bluntly: “FLOW A is way too much effort for me, and the 12 required fields are an absolute deal-breaker. I’d almost certainly abandon there,” says Twin “Sören Lindner” in the original choice. In the allocation follow-up, he gets more specific: “A cumbersome process like Flow A, which asks for unnecessary data and forces an account, would be a clear signal for me to spend my money elsewhere—especially on an €89 cart,” says Twin “Sören Lindner” (digital twin, DACH consumer panel).
How big is the difference, really?
The allocation number makes the effect tangible: at Flow A, twins abandon on average 7.0 of 10 purchases; at Flow B, only 0.55 of 10—a range of 4–8 versus 0–2, with a friction score of 6.45 points. All 20 twin responses delivered a usable number, and the order of presentation barely moves the picture (a 0.4-point difference for Flow A and a 0.1-point difference for Flow B between the two runs). This is exactly the point where a friction audit no longer has to be left to gut-feel consulting today—you can make it testable before you rebuild the checkout. Digital twins quantify a flow in minutes, not weeks.
Classic study
Dooley (2019): Every extra step and every required field in checkout costs conversions—guest checkout beats forced accounts.
Digital Twins (2026)
7.0 vs. 0.55 of 10 purchases are abandoned at the forced-account checkout, compared to the guest checkout.
Same pattern, measured fresh—in minutes instead of weeks of user testing.
What does this mean for your own checkout?
The practical takeaway from this one test: if your checkout includes a mandatory account step with email confirmation and a double-digit number of required fields, a guest-checkout comparison is the most obvious first test before you touch design, colors, or copy. Whether the exact magnitude of this effect transfers to every shop, every audience, and every cart value is something an individual test has to show—but here, the direction lines up completely with Dooley’s classic recommendation.
Want to know how much friction your own checkout is costing you? Book my talk “Why Consumers Buy Weird”—including a live demo of how digital twins test a checkout flow in minutes.
This test is part of The Trigger Lab series, in which we re-examine classics of consumer psychology with digital twins. You’ll find the full overview of all re-tests soon in the flagship article “Brainfluence Retested” (coming soon at /en/trigger-lab-brainfluence-retested/).
Further reading
- Roger Dooley vs. Jonathan Mall: Who’s Right About Brainfluence?
- Digital Twins in Market Research: The Complete Guide 2026
- Digital Twins vs. Focus Groups: Method Comparison 2026
Frequently asked questions
How do you measure friction in a checkout process?
In our test, digital twins abandon on average 7.0 of 10 purchases at a 5-step checkout with forced account creation (12 required fields), versus 0.55 of 10 at a 2-step guest checkout (5 fields)—20 of 20 usable answers across two oppositely ordered runs. The direct choice was just as clear: 100 percent of the 20 responses chose the guest checkout, without a single exception.
Does forced account creation at checkout really cost conversions?
Field data says yes: per the Baymard Institute, around 26% of checkout abandoners cite forced account creation as a reason (overall cart abandonment: ~70%). Our twins show the same direction as stated intent: on average 7.0 of 10 abandonments at the 5-step flow with forced accounts and 12 required fields, versus 0.55 of 10 at the guest checkout—an intent measurement, not conversion tracking.
What is a guest checkout, and is it worth it?
A guest checkout lets customers complete a purchase without mandatory account creation—in our test, 2 steps with just 5 fields instead of 5 steps with 12 required fields and email confirmation. On average, the abandonment rate at the guest checkout sits at 0.55 of 10 purchases, versus 7.0 of 10 at the forced-account flow. In our digital-twins test (20 responses, two counterbalanced runs), every single twin chose the guest checkout without exception—clear evidence that dropping forced accounts pays off, at least for this specific flow pair.
How reliable is a 100 percent result with digital twins?
The digital twins (DACH consumer panel, ages 25–60) ran the test twice with the two flows’ order swapped, for 20 responses total—and all 20 came out the same, with no position effect. A second measurement in allocation format (same twins, a different question form; another 20 responses, 20/20 usable) confirms the picture with a graded number: 7.0 of 10 abandonments at forced accounts, 0.55 of 10 at the guest checkout. How strong the effect is for other products, price points, or audiences is something every individual test has to show—the result is one test on one concrete checkout pair, not a universal law.
Glossary: The Trigger Lab vocabulary
Digital Twins: AI personas grounded in real survey profiles that respond to text stimuli with forced-choice decisions and ratings—a market-research panel that answers in minutes instead of weeks. → Learn more: Digital Twins in Market Research: The Complete Guide
The Trigger Lab: the article series in which classics of consumer psychology are re-tested live with digital twins from a DACH consumer panel. → See the experiment: Brainfluence Retested
Trust Words: fixed trust phrases placed under the buy button—money-back guarantees, customer reviews, or certification marks—that, per Dooley (Brainfluence, 2011), raise perceived trust and purchase intent. → See the experiment: Ten Words That Build Trust
First Impressions (50 Milliseconds): the finding that visitors form a design judgment about a website in roughly 50 milliseconds, with visual simplicity beating dense design (Lindgaard et al., 2006; Tuch et al., 2012). → See the experiment: The First 50 Milliseconds
Faces Effect (Eye Magnet): faces draw the eye (Dooley, 2011); the gaze direction of a pictured face further directs attention (Hutton & Nolte, 2011—not testable in our text format). → See the experiment: Faces, Eyes, Attention
Cognitive Fluency: the principle that easy-to-process design—clear type, short sentences, high contrast—makes tasks and offers feel more effortless and trustworthy than hard-to-process design (Song & Schwarz, 2008). → See the experiment: Does the Wrong Font Cost You Conversions?
Surprise Trigger (Expectation Gap): headlines that break an expectation or promise a surprise earn higher click intent, per Dooley (Brainfluence, 2011), than plain announcements or bare FREE/NEW signals. → See the experiment: Headline Triggers: FREE, NEW, and the Surprise Reflex
Decoy Effect: a deliberately unattractive, expensive third option in a pricing menu shifts buyer choice toward the middle, pricier option, without being chosen itself (Ariely, 2008). → See the experiment: Pricing Psychology 2.0: The Decoy Effect
Friction: every extra step, every extra required field, and every forced account creation in checkout lowers the odds of completing the purchase—guest checkout beats forced accounts (Dooley, Friction, 2019).
Banner Blindness (Dead Zone): users systematically overlook page areas that look like ads or sit in typical ad positions—the “corner of death” in the right sidebar and bottom corner (Benway & Lane, 1998; Nielsen, 2007; Dooley, 2011). → See the experiment: The Attention Dead Zone
Simple Slogans (Rhyme-as-Reason): short, concrete slogans are remembered better and land as more persuasive than complex or abstract phrasing; rhyme and wordplay amplify this effect further because they make plain statements feel truer (Dooley, 2011; McGlone & Tofighbakhsh, 2000). → See the experiment: Simple Slogans, Measured
Pick Share (Forced Choice): the share of twins who, in a forced-choice question with no “don’t know” option, choose a given variant, averaged across two oppositely ordered runs.
Allocation Measure: a question technique where twins state, for each variant, how many of 10 purchases or situations they would choose it in—yielding a realistic distribution instead of a single yes/no snapshot.
Sources & further reading
- Dooley, R. (2019). Friction. McGraw-Hill.
- Trigger Lab Experiment F5, 2026, n = 10 digital twins (neuroflash).
- Trigger Lab Experiment F5b (Allocation follow-up), 2026, n = 10 digital twins (neuroflash).
Get the same scientific power for your own marketing: Use the digital twins from this experiment yourself—via the neuroflash Digital Twins MCP directly inside Claude or Cursor, or in your browser at neuroflash.com. Your stimuli, the same panel principle, results in minutes.
Dr. Jonathan T. Mall
Cognitive neuropsychologist, AI entrepreneur, and Chief Innovation Officer at neuroflash. Jonathan combines 20+ years of experience in neuroscience and AI to predict how people decide. His signature talk “Why Consumers Buy Weird” explains why we buy irrationally—and how digital twins predict it. If you want to experience these insights live, you can book an AI keynote with live demos. LinkedIn · Keynote inquiry