The Experimentation Edge

Summary
What if you could A/B test on customers who don't exist before spending a single live impression? Erika Dunn, assistant director of data science at Principal Financial Group, joins host Ashley Stirrup, CMO at GrowthBook, to share how she built synthetic digital audiences entirely in house: profiles shaped by real data that rank content by likelihood of engagement, whose first live A/B test selection just beat the control. They also dig into the looping metric her team built in SQL to find where customers get stuck without heat-mapping tools, why testing gets watered down into "let's try something" at large companies, and how a center of excellence that shares wins and losses defeats the "we tried that years ago" reflex. This episode is for product managers, data scientists, marketers, and experimentation leaders, especially those working inside large, risk-averse organizations.

Chapters
00:00 Cold open and welcome
01:40 Erika's path from quantitative psychology to experimentation
03:35 When testing gets watered down
06:50 Experimentation in Principal's marketing space
09:55 The looping metric that finds stuck users
12:45 Building synthetic digital audiences
15:15 The first synthetic audience A/B test wins
22:30 The PDF lesson and meeting customers on mobile
25:15 North star metrics and the right contact cadence
28:05 Where experimentation goes next with AI agents

Takeaways
- Synthetic digital audiences let teams rank 20 content options by predicted engagement before spending a single live impression, and Principal's first synthetic selection beat the control in a real A/B test.
- A looping metric built from web behavior data can find where customers get stuck without heat-mapping tools: watch how often users cycle back to the same page within tight time windows.
- Testing gets watered down when "let's try something" replaces a control group; a little pre-planning gets far more out of every experiment.
- A center of excellence that shares wins and losses turns tribal knowledge into shared knowledge and stops "we tried that years ago" from killing valuable retests.
- An experimentation mindset requires that people can't get punished for mistakes; give teams guardrails and a safe playground and they'll stop running the same test forever.

Connect with the Guest
Erika Dunn LinkedIn: https://www.linkedin.com/in/erikadunn/
About the guest: https://www.growthbook.io/podcast/guests/erika-dunn
Episode page: https://www.growthbook.io/podcast/episode/1-32
Company Website: https://www.principal.com

Sponsor
GrowthBook is the warehouse-native platform for experimentation, feature flags, and product analytics trusted by AI-native product teams at 3,000+ companies worldwide.

Go to http://growthbook.io

Summary
How does a major bank scale experimentation when even one percent of customers missing an experience is unacceptable? Vijay Lal, Lead Product Manager for Experimentation at US Bank, joins host Ashley Stirrup, CMO at GrowthBook, to share how his team made their experimentation platform self serve for non technical marketers, how a login widget experiment led to a two second fallback that accounted for every customer, and why metrics should be driven by hypotheses instead of handed down by leadership. They also dig into where AI genuinely saves time in experiment analysis, why a human in the loop is non negotiable, and what real time personalization means for the future of testing. This episode is for product managers, data scientists, and experimentation leaders, especially those working in regulated industries.

Chapters
00:00 Cold open and welcome
00:40 Vijay's path from Comcast to financial services
03:16 Making the experimentation platform self serve
05:08 The login widget experiment and the two second fallback
08:59 Documenting learnings from every experiment
10:38 AI in experimentation and the human in the loop
12:39 Advice for new product managers
15:24 Hypothesis driven metrics
18:25 Real time personalization and agentic AI
20:11 Democratizing experimentation with responsibility

Takeaways
- Self serve experimentation lets a small central team support a huge testing volume, but it only works with continuous training and guardrail metrics attached.
- In a regulated industry, every customer must be accounted for. Even one to two percent of users missing an experience is unacceptable.
- A simple fallback, like a two second load rule, can save an ambitious experiment without sacrificing coverage or security.
- Metrics should be driven by the experiment's hypothesis, not chosen by leadership in a silo. Pair a primary KPI with secondary KPIs for return behavior.
- AI saves real time in experiment analysis, but a human in the loop must validate anything AI produces before it goes live.

Connect with the Guest
Vijay Lal LinkedIn: https://www.linkedin.com/in/vijay-lal/
About the guest: https://www.growthbook.io/podcast/guests/vijay-lal
Episode page: https://www.growthbook.io/podcast/episode/1-31
Company Website: https://www.usbank.com

Sponsor
GrowthBook is the warehouse-native platform for experimentation, feature flags, and product analytics trusted by AI-native product teams at 3,000+ companies worldwide.

Go to http://growthbook.io

Summary
Luis Trindade, Principal Product Manager of Experimentation at Farfetch, joins host Ashley Stirrup to explain how one of the world's largest luxury marketplaces built its own experimentation platform and the culture around it. Luis covers the move from a hybrid setup with an external testing vendor to Fabs 2.0, the in house system where a feature toggle is the single entry point for every experiment, why Farfetch manages by learning rate instead of win rate, and the two year Inspire experiment that replaced the world's leading recommendation engine vendor. He also shares how a deliberately shrinking center of excellence supports hundreds of experiments a month through clinics, shared templates, and open learning sessions. This episode is for product managers, engineers, data scientists, and growth leaders building or scaling an experimentation program.

Chapters
00:00 Cold open and introduction
01:45 Inside Farfetch, the global marketplace for luxury fashion
08:00 From startup validation to an experimentation mindset
09:45 A center of excellence that enables instead of executes
12:45 Fabs, build versus buy, and dropping the external vendor
16:45 One feature toggle as the entry point for every experiment
20:45 Learning rate over win rate
23:15 The two year experiment that replaced the recommendation vendor
29:45 Onboarding new product managers into experimentation
33:15 AI, corporate knowledge, and what comes next for experimentation

Takeaways
- Manage by learning rate, not win rate. The only failed test is one that was badly designed, with wrong metrics or sampling biases. Every other test produces a learning.
- Route every experiment through a single entry point. Farfetch's feature toggling system connects segmentation, user systems, CMS, and messaging so every team tests in the same language.
- External JavaScript injection tools carry hidden costs: broken pages, inconsistent results, and rework to reclaim your own data for deep dives.
- Strategic bets deserve a longer clock than fail fast allows. Farfetch iterated on its Inspire engine for two years before it beat and replaced the market leader.
- A center of excellence should enable, not execute. Farfetch's central team shrank while experiment volume grew because its job is ceremonies, templates, and coaching.

Connect with the Guest
Luis Trindade LinkedIn: https://www.linkedin.com/in/ltrindade/
About the guest: https://www.growthbook.io/podcast/guests/luis-trindade
Episode page: https://www.growthbook.io/podcast/episode/1-30
Company Website: https://www.farfetch.com

Sponsor
GrowthBook is the warehouse-native platform for experimentation, feature flags, and product analytics trusted by AI-native product teams at 3,000+ companies worldwide.

Go to http://growthbook.io

Summary
In this episode of The Experimentation Edge, host Ashley Stirrup talks with Fabian Hans, founder and behavioral psychologist at Cogniteer, a consultancy that helps enterprises build in-house experimentation programs and raise both test velocity and win rate. Drawing on fifteen years in conversion rate optimization, Fabian explains why mass-producing the same A/B tests across clients quietly kills learning, why most ecommerce drop-offs are structural rather than your fault, and how matching the interface to how people actually buy, new versus returning, B2C versus B2B, can move conversion far more than another button. It is a practical, psychology-grounded conversation for product managers, engineers, data scientists, and growth leaders who want their experimentation programs to compound understanding, not just volume.

Chapters
00:00 Introduction
01:25 From agency mass production to in house deep dives
04:05 Why some products resist selling online
06:35 The drop offs every ecommerce shop shares
07:45 The 50% win rate test Cogniteer reused
09:25 Why alignment beats developer resources
12:45 Two teams, two goals, one broken checkout
17:05 Selling water dispensers without a product catalog
22:15 Designing every experiment to lose
26:15 Personalizing buyers and where AI takes experimentation

Takeaways
- Deep dives beat mass produced tests, because understanding one business's users uncovers bigger levers than reusing the same test across many clients.
- Many ecommerce drop offs are structural, since the basket and product page leak in roughly 80% of shops because it is ecommerce, not because of your product.
- Product to channel fit decides what sells online, so books and fashion judge well on a screen while perfume and washing machines need cues the interface cannot fully provide.
- The real bottleneck is alignment, not developer resources, so agree on the problem and its hierarchy before anyone builds a variation.
- Match the interface to how people actually buy, because new buyers need information, returning buyers want speed, and B2B buyers often want a solution and an offer instead of a product catalog.

Connect with the Guest
Fabian Hans LinkedIn: https://www.linkedin.com/in/fabianhans-cogniteer/
About the guest: https://www.growthbook.io/podcast/guests/fabian-hans
Episode page: https://www.growthbook.io/podcast/episode/1-29
Company Website: https://www.cogniteer.de/

Sponsor
GrowthBook is the warehouse-native platform for experimentation, feature flags, and product analytics trusted by AI-native product teams at 3,000+ companies worldwide.

Go to http://growthbook.io

Summary
On this episode of The Experimentation Edge, host Ashley Stirrup talks with Pedro Tabacof, Principal Machine Learning Scientist at Fin (formerly Intercom), about how one of the most advanced AI customer support agents in the world is built on relentless experimentation. Pedro explains why unit tests don't work on non-deterministic AI, how Fin runs up to two dozen concurrent A/B tests pulling millions of samples in days, and shares two counterintuitive experiments: one where slowing the agent down improved every metric, and one where adding more context made Fin more helpful and more prone to fake promises until a targeted prompt fix kept the upside without the hallucinations. It's a candid look for product managers, engineers, and data scientists at how a $100M ARR AI product actually ships improvements.

Chapters
00:00 Welcome and what Fin actually does
02:00 How Fin became Anthropic's first line of support
02:30 Why Fin sells resolutions not deflections
06:00 Owning the stack with custom models
10:40 Pedro's path from fuzzy logic to AI
12:55 Why A/B testing is the only gold standard for AI
15:50 Do no harm testing on every change
18:00 The latency experiment that shocked the team
27:30 When more context made Fin hallucinate
30:15 Win rates and the future of AI driven experimentation

Takeaways
- Faster is not always better. Fin increased latency artificially and positive feedback went up, likely because a small delay makes an AI feel like it is doing real work.
- You cannot unit test a non-deterministic AI. A/B testing at scale, millions of samples in days, is the only reliable way to know a change actually helped.
- Adding more conversation history made Fin more helpful and more prone to fake promises, until a targeted prompt fix removed the hallucinations and kept most of the gain.
- A losing experiment is often a winner with one broken part. Diagnose which element hurts the experience, fix only that, and rerun.
- Fin A/B tests everything, even one-character prompt changes and many bug fixes, and treats a 20 to 30 percent win rate as a healthy sign of a real experimentation program.

Connect with the Guest
Pedro Tabacof LinkedIn: https://www.linkedin.com/in/tabacof/
About the guest: https://www.growthbook.io/podcast/guests/pedro-tabacof
Episode page: https://www.growthbook.io/podcast/episode/1-28
Company Website: https://fin.ai

Sponsor
GrowthBook is the warehouse-native platform for experimentation, feature flags, and product analytics trusted by AI-native product teams at 3,000+ companies worldwide.

Go to http://growthbook.io

Summary
In this episode of The Experimentation Edge, host Ashley Stirrup, CMO of GrowthBook, sits down with James Falzone, Director of Product Management at Kargo, to unpack how a high scale ad tech marketplace turns failure into its biggest advantage. James explains how Kargo connects advertisers to publishers through real time auctions that resolve in milliseconds across up to 10 billion ad requests a day, why experimentation is embedded in the company's culture rather than siloed in a team, and what happened when a winning click optimization model failed completely after being copied to a new customer type. The conversation is built for product managers, data scientists, engineers, and growth leaders who want a practical, honest view of running experiments at scale, learning from losses, and keeping AI grounded in solid infrastructure.

Chapters
00:00 Welcome and introducing James Falzone
01:45 What Kargo does and how real time ad auctions work
04:45 Why experimentation is embedded in Kargo's culture
07:45 The three things every marketplace has to deliver
10:15 The experiment that failed: click optimization on third party demand
12:15 A bad result versus a bad experiment
13:45 Why different customer types need different signals
15:30 Putting "where did you fail?" on every retro
18:45 How experimentation evolves with AI
21:15 Better not bigger: the closing takeaway

Takeaways
- A bad result is not a bad experiment. If you're not failing, you're probably not trying anything new.
- The same metrics and signals don't apply to every customer type. Bad results often come from a lack of context, not bad tech.
- Metrics and signals you test against should always be business driven, not ported from the last thing that worked.
- Put failure on the agenda. A biweekly "where did you fail?" retro turns one person's dead end into the whole team's shortcut.
- AI's biggest unlock is access. More people can run experiments, but it has to be built on solid ML and infrastructure. Better, not bigger.

Connect with the Guest
James Falzone LinkedIn: https://www.linkedin.com/in/jamesafalzone/
About the guest: https://www.growthbook.io/podcast/guests/james-falzone
Episode page: https://www.growthbook.io/podcast/episode/1-27
Company Website: https://kargo.com

Sponsor
GrowthBook is the warehouse-native platform for experimentation, feature flags, and product analytics trusted by AI-native product teams at 3,000+ companies worldwide.

Go to http://growthbook.io

Summary
In this episode of The Experimentation Edge, host Ashley Stirrup talks with Danielle Olean, Director of E-commerce at Box, about what it really takes to build a culture of experimentation inside a B2B company. Drawing on 15 years across B2C and B2B at Wayfair, Drizly, Zoom, and now Box, Danielle explains why experimentation belongs to every product team and not just e-commerce, walks through a pricing page saga of one win and two losses that exposed the limits of simplification, and shares the "wine effect" test that won for a reason no one predicted. It's a practical, story rich conversation for product managers, growth leaders, and anyone trying to make better decisions with data.

Chapters
00:45 Meet Danielle Olean and Box's reinvention
02:45 Owning the entire customer life cycle
04:45 Why experimentation matters even without a checkout
07:45 The feature that's used but hidden
11:45 Proving ROI with a scrappy manual test
12:45 Building a culture that shares wins and losses
16:45 The pyramid strategy for prioritizing tests
18:45 The simplification tightrope on the pricing page
24:45 When a test wins for the wrong reason
27:45 Where experimentation at Box goes next

Takeaways
- Experimentation isn't only for e-commerce. Any product with a funnel, even an AI chatbot, can be measured and improved through testing.
- Simplification has a limit. Removing too much can strip away the cues and context buyers actually need to decide.
- Share losses as openly as wins. Wins build credibility, and losses build the psychological safety a testing culture runs on.
- Prioritize like a pyramid. Fix the widest-impact experiences first, then optimize down into smaller cohorts.
- Surprising results are the point. A test can win for a reason you never hypothesized, like the "wine effect," and that's where the real learning lives.

Connect with the Guest
Danielle Olean LinkedIn: https://www.linkedin.com/in/dolean1/
About the guest: https://www.growthbook.io/podcast/guests/danielle-olean
Episode page: https://www.growthbook.io/podcast/episode/1-26
Company Website: https://www.box.com

Sponsor
GrowthBook is the warehouse-native platform for experimentation, feature flags, and product analytics trusted by AI-native product teams at 3,000+ companies worldwide.

Go to http://growthbook.io

Summary
Dan Layfield, Director of Product Management at Diligent, joins host Ashley Stirrup on The Experimentation Edge to trace what fifteen years of A/B testing across Codecademy, Uber Eats, and the Fortune 1000 boardroom actually taught him. He breaks down the Codecademy trial-model rebuild that took four months and several rounds to deliver a 35% conversion lift, why moving on from a losing experiment too early is one of a PM's costliest mistakes, how to escape the B2B feature factory with metrics that genuinely ladder up, why retention should ride a product's natural use case instead of fighting it, and where AI is already replacing weeks of research and analysis. It's a practitioner's guide for product managers, growth leaders, data scientists, and engineers bringing experimentation rigor to both B2C and B2B.

Chapters
00:45 Meet Dan Layfield and Diligent
01:45 Two worlds of experimentation, Codecademy and Uber
03:45 The trial model that lifted conversion 35%
06:20 What to do with a losing experiment
08:50 Two flavors of experimentation
09:45 Reading forty metrics at Uber Eats
13:10 Escaping the B2B feature factory
16:45 Anchoring the North Star to real usage
19:15 Where AI fits in research and analysis

Takeaways
- A losing experiment is often inconclusive, not negative; treat it as a map of the funnel rather than a verdict, and know when a big problem is worth another round.
- Persistence paid off at Codecademy: four months and three to four rounds of trial-model testing produced a 35% conversion increase.
- Separate your two experimentation modes; high-volume CRO chases many small wins, while big, uncertain bets are worth taking multiple shots to de-risk.
- Most B2B product teams are feature factories; the fix is a top-down OKR system, and planning usually breaks in the connections between layers, not inside them.
- Anchor retention and engagement to the product's natural use case, and use AI to synthesize research and simple A/B analysis in hours instead of weeks.

Connect with the Guest
Dan Layfield LinkedIn: https://www.linkedin.com/in/layfield/
About the guest: https://www.growthbook.io/podcast/guests/daniel-layfield
Episode page: https://www.growthbook.io/podcast/episode/1-25
Company Website: https://www.diligent.com

Sponsor
GrowthBook is the warehouse-native platform for experimentation, feature flags, and product analytics trusted by AI-native product teams at 3,000+ companies worldwide.

Go to http://growthbook.io

Summary
In this episode of The Experimentation Edge, GrowthBook CMO Ashley Stirrup sits down with Nick Beyler, data science manager at Stitch Fix, where he leads the decision and insights team and owns the company's internal experimentation platform. Nick shares why the metric he most wants is the one he can't measure yet, a North Star that predicts a client's long-term value from their earliest behaviors, and why the most impactful experiment learnings tend to come from adoption friction rather than product bugs. He makes the case that if you're only testing winners you're not taking enough risks, explains how guardrails make that risk safe, and looks ahead to a new in-house platform and the promise of agentic AI. It's a practical, statistician's-eye view of experimentation for product managers, data scientists, and engineers building serious testing programs.

Chapters
00:00 Cold open and welcome to the show
01:45 What Stitch Fix actually does
04:15 Balancing AI with the human stylist
05:15 From public policy to the A/B testing adrenaline rush
07:15 Inside the weekly experimentation review group
08:45 The AI style assistant and listening to qualitative feedback
10:45 Why adoption friction beats product bugs
13:45 Testing for losers and building guardrails
15:45 Keep rate, successful fixes, and the holy grail metric
18:15 The new platform and the promise of agentic AI

Takeaways
- The most impactful experiment learnings usually come from adoption friction, not product bugs. By the time a big feature reaches A/B testing, it's often already a winner, so the open question is how and where to introduce it.
- A losing test is a finding, not a failure. If every experiment wins, you're not taking enough risk to learn anything new.
- Guardrails and stopping criteria are what make risk-taking safe, especially when the experience is as personal as shopping.
- The most valuable North Star metric is the one you can't measure yet, long-term client value, and causal-inference modeling helps predict it from short-term behavior.
- Quantitative results are only half the story. Direct, qualitative client feedback inside an experiment often reshapes the rollout more than the numbers do.

Connect with the Guest
Nick Beyler LinkedIn: https://www.linkedin.com/in/nick-beyler-381864119/
About the guest: https://www.growthbook.io/podcast/guests/nick-beyler
Episode page: https://www.growthbook.io/podcast/episode/1-24
Company Website: https://www.stitchfix.com

Sponsor
GrowthBook is the warehouse-native platform for experimentation, feature flags, and product analytics trusted by AI-native product teams at 3,000+ companies worldwide.

Go to http://growthbook.io

Summary
Amir Moghaddam, Director of Software Engineering at Expedia Group, joins host Ashley Stirrup on The Experimentation Edge to make the case that measurement is not a reporting step but a gate: what you cannot measure, you cannot ship. Drawing on nearly four years at DoorDash and his current work leading Expedia's air booking platform, Amir explains why he refuses to label experiments winners or losers, how a "failed" pricing test pushed his team toward full personalization, and why a three sided marketplace forces hard trade-offs between competing metrics. The conversation closes on how the same experimentation discipline now applies to shipping and measuring AI. Built for product managers, engineers, data scientists, and growth leaders who care about rigor over opinion.

Chapters
00:00 Cold open
00:50 Meet Amir and the air booking platform at Expedia
03:10 DoorDash, growth, and a 70 experiment year
04:20 Three kinds of experimentation at Expedia
06:30 AI velocity and the new frontier model pace
08:30 What you cannot measure, you cannot ship
10:45 The DoorDash carousel and the price experiment
12:45 The three sided marketplace and competing metrics
16:55 There are no losing experiments
20:45 Predictability, LLMs, and Expedia's road ahead

Takeaways
- "What you cannot measure, you cannot ship" — if you can't measure an outcome, you can't decide whether it's better, so you're just debating opinions.
- Measurement spans three live dimensions: spend (more with less), speed (sprints instead of quarters), and quality, with guardrail "do no harm" metrics on top.
- There are no losing experiments. A flat result is a signal to either refine the hypothesis or step back and look from a completely different angle.
- DoorDash's price experiment proved price by itself doesn't predict orders. Different customers want different things at different times, which pushed the team toward personalization.
- A three sided marketplace (buyers, merchants, Dashers) makes metrics compete. Running the test is easy; deciding what to optimize when goals conflict is the real work.

Connect with the Guest
Amir Moghaddam LinkedIn: https://www.linkedin.com/in/amirmoghaddam
About the guest: https://www.growthbook.io/podcast/guests/amir-moghaddam
Episode page: https://www.growthbook.io/podcast/episode/1-23
Company Website: https://www.expediagroup.com

Sponsor
GrowthBook is the warehouse-native platform for experimentation, feature flags, and product analytics trusted by AI-native product teams at 3,000+ companies worldwide.

Go to growthbook.io