Ashley Faus

Director of Integrated Product Marketing, Atlassian

Marketer, writer, speaker by day... singer, actor, fitness fiend by night!

I've handled all aspects of marketing strategy and execution, from product marketing, to demand-gen, to content and events. I love telling the high-level stories that resonate with an audience and connecting the dots between types of assets and distribution channels.

My writing has been published on TIME, Forbes, The Journal of Brand Strategy, and The Muse, and I've spoken on various marketing topics for INBOUND, Harvard Business Review, and MarketingProfs.

Visit https://ashleyfaus.com/ for more information about my book, "Human-Centered Marketing: How to Connect with Audiences in the Age of AI".

(Views are my own)

Head of Lifecycle Marketing, Portfolio

Tim Herbig

Product Management Coach & Consultant | I help Product Teams connect the Dots of Strategy, OKRs, and Discovery through better practices.

As a Product Management Coach and Consultant, I enable product teams to make real progress by connecting the dots of Product Strategy, Product OKRs, and Product Discovery.

I have helped companies like StepStone, Chrono24, Deutsche Telekom, and Specsavers turn product theory into pragmatic and practical application. Before going independent, I held multiple Product roles at companies like Gruner+Jahr and XING and multiple b2c and b2b startups for 10 years.

I now help product teams develop and implement better practices that help them progress in their context instead of chasing artificially created "best practices." I achieve that through hands-on training, coaching offerings, in-depth courses, and intriguing content.

Product Management Coach & Consultant, Speaker

As a Product Management Coach, I work with product managers and teams around the globe to connect the dots through empathy and structure.

By introducting confidence and skills to their way of working, I ensure that they can and want to own their Product Strategy, Goals, and Product Discovery.

Thereby, they don’t pick techniques and techniques for the sake of it, but to actually make progress. And they don‘t just progress for the sake of it, but in order to achieve meaningful goals through self-determined learning.

When it comes to Strategy, what separates the Company, Product, and Feature levels? 

Company Strategy provides non-Feature guidance for where the company wants to go, based on the Company Vision and high-level goals. It outlines details around fundamental shifts like entering new markets or abandoning business models.

Product Strategy synthesizes the evidence around executing specific Strategy Choices from the Company level. And since Products are bundles of features that create value for a core use case they aim to address, it provides s overarching guidance for more tactical feature prioritization and execution. In early stage (typically, pre-PMF) companies, Company Strategy IS the Product Strategy.

Feature Strategy expresses priority through proven feature usage and value insights. Features are discrete pieces of value that solve individual problems but contribute to the product's core use case.

Or, as Roger Martin has put it:
𝘙𝘶𝘴𝘴𝘪𝘢𝘯 𝘔𝘢𝘵𝘳𝘺𝘰𝘴𝘩𝘬𝘢 𝘥𝘰𝘭𝘭𝘴 — 𝘦𝘢𝘤𝘩 𝘰𝘯𝘦 𝘯𝘦𝘴𝘵𝘦𝘥 𝘸𝘪𝘵𝘩𝘪𝘯 𝘵𝘩𝘦 𝘯𝘦𝘹𝘵 𝘭𝘢𝘳𝘨𝘦𝘳 𝘰𝘯𝘦. 𝘛𝘩𝘦 𝘰𝘶𝘵𝘦𝘳 𝘥𝘰𝘭𝘭 𝘪𝘴 𝘭𝘪𝘬𝘦 𝘵𝘩𝘦 𝘩𝘪𝘨𝘩𝘦𝘴𝘵-𝘭𝘦𝘷𝘦𝘭 𝘤𝘰𝘳𝘱𝘰𝘳𝘢𝘵𝘦 𝘴𝘵𝘳𝘢𝘵𝘦𝘨𝘺 𝘤𝘩𝘰𝘪𝘤𝘦. 𝘐𝘵𝘴 𝘪𝘮𝘱𝘰𝘳𝘵𝘢𝘯𝘤𝘦 𝘪𝘴 𝘤𝘰𝘯𝘴𝘪𝘴𝘵𝘦𝘯𝘵 𝘸𝘪𝘵𝘩 𝘵𝘩𝘦 𝘥𝘰𝘭𝘭’𝘴 𝘴𝘪𝘻𝘦 𝘢𝘯𝘥 𝘱𝘰𝘴𝘪𝘵𝘪𝘰𝘯. 𝘐𝘵 𝘪𝘴 𝘵𝘩𝘦 𝘮𝘰𝘴𝘵 𝘪𝘮𝘱𝘰𝘳𝘵𝘢𝘯𝘵 𝘤𝘩𝘰𝘪𝘤𝘦 — 𝘢𝘯𝘥 𝘪𝘵 𝘴𝘦𝘵𝘴 𝘵𝘩𝘦 𝘤𝘰𝘯𝘵𝘦𝘹𝘵 𝘧𝘰𝘳 𝘢𝘭𝘭 𝘵𝘩𝘦 𝘤𝘩𝘰𝘪𝘤𝘦𝘴 𝘣𝘦𝘭𝘰𝘸. 𝘛𝘩𝘦 𝘯𝘦𝘹𝘵 𝘥𝘰𝘭𝘭 𝘤𝘢𝘯’𝘵 𝘣𝘦 𝘣𝘪𝘨𝘨𝘦𝘳 𝘵𝘩𝘢𝘯 𝘵𝘩𝘦 𝘧𝘪𝘳𝘴𝘵, 𝘰𝘳 𝘢 𝘥𝘪𝘧𝘧𝘦𝘳𝘦𝘯𝘵 𝘴𝘩𝘢𝘱𝘦. 𝘛𝘩𝘦 𝘴𝘦𝘤𝘰𝘯𝘥 𝘥𝘰𝘭𝘭, 𝘢𝘴 𝘸𝘪𝘵𝘩 𝘵𝘩𝘦 𝘯𝘦𝘹𝘵 𝘭𝘦𝘷𝘦𝘭 𝘰𝘧 𝘴𝘵𝘳𝘢𝘵𝘦𝘨𝘺 𝘤𝘩𝘰𝘪𝘤𝘦𝘴 (𝘭𝘦𝘵’𝘴 𝘴𝘢𝘺 𝘉𝘜 𝘴𝘵𝘳𝘢𝘵𝘦𝘨𝘺) 𝘮𝘶𝘴𝘵 𝘧𝘪𝘵 𝘸𝘪𝘵𝘩 (𝘢𝘯𝘥 𝘳𝘦𝘪𝘯𝘧𝘰𝘳𝘤𝘦) 𝘵𝘩𝘦 𝘧𝘪𝘳𝘴𝘵 𝘥𝘰𝘭𝘭/𝘤𝘰𝘳𝘱𝘰𝘳𝘢𝘵𝘦 𝘤𝘩𝘰𝘪𝘤𝘦.

For a large company, this strategic thought exercise is probably nested across many  departments and teams. Focusing on the fundamental cornerstones of Product Strategy choices helps. 

The difference for consistent Strategy across the levels of Company, Product, and Feature lies in ensuring that the proper Patterns are connected to the other stacked Strategies.

#productstrategy #outcomes #prodmgmt #productvision #productmanagement

Product Management Coach, Author, and Speaker | I help Product Teams connect the Dots of Strategy, OKRs, and Discovery.

Metrics frameworks don't matter. What matters is knowing how the metrics relate to one another.

I'm a fan of going back to the basics: ​Any metrics framework​ should help you measure value delivered to or changes created for whomever your audience is. And guess what? ​Even internal product teams have an audience: The fellow product, marketing, or sales teams their products serve.

Once that fundamental truth has sunk in, teams can also more easily understand that concepts like ​North Star Metrics​ or ​Lagging and Leading Indicators​ are contextual, not definitive. The Input Metric for one product's NSM could also be the (smaller-scale) NSM for another team. ​What's a leading indicator for one team can also be a lagging indicator for another team.

When I coach teams, we don't start with "let's set OKRs" or "what's our North Star Metric?"
 
We start by mapping how value flows through their product. In something as simple as a metrics tree. Everything else falls out from there. Which makes this a universal practice whether you work in b2b, b2c, or serve external or internal customers.
 
𝗪𝗮𝗻𝘁 𝘁𝗼 𝘀𝗲𝗲 𝗵𝗼𝘄 𝘁𝗵𝗶𝘀 𝘄𝗼𝗿𝗸𝘀 𝗶𝗻 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲?
 
I'm running a free 60-minute metrics masterclass on March 4th: "𝗨𝘀𝗲 𝗥𝗲𝗮𝗹-𝗟𝗶𝗳𝗲 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 𝗧𝗿𝗲𝗲𝘀 𝘁𝗼 𝗗𝗲𝗳𝗶𝗻𝗲 𝗡𝗦𝗠𝘀, 𝗢𝗞𝗥𝘀, 𝗮𝗻𝗱 𝗞𝗣𝗜𝘀"
 
You'll learn: 
→ How to define metrics trees that reveal actionable metrics
→ How to choose which parts become KPIs or North Star Metrics
→ How to connect your metrics tree to product strategy and discovery
 
𝗦𝗲𝗰𝘂𝗿𝗲 𝘆𝗼𝘂𝗿 𝘀𝗽𝗼𝘁:
https://herbig.link/eHNipN

I asked 352 Product Managers what their biggest Product Strategy challenges were.
 
42.6% can't get started. 27.6% can't connect company and product strategy. 19.6% can't execute or measure it. And only 10.2% worry about picking the right template.
 
I'm somewhat glad the frameworks came in last. It tells me most of you already sense what I keep saying in my book: the format isn't the point.
 
Real Progress starts when your strategy helps you confidently say yes or no—not when you've filled in a canvas correctly.
 
If that resonates, I'm running a free, hands-on live webinar tomorrow (Feb 11) where I'll walk through how to approach each of these struggles in practice.

Reserve your seat here (the session won't be recorded):
https://lnkd.in/dCBTVqKH

Top 4 Product Strategy Struggles

"𝘏𝘰𝘸 𝘮𝘶𝘤𝘩 𝘮𝘰𝘯𝘦𝘺 𝘸𝘪𝘭𝘭 𝘵𝘩𝘪𝘴 𝘧𝘦𝘢𝘵𝘶𝘳𝘦 𝘮𝘢𝘬𝘦?" Raise your hand if you've heard this question before.

I for sure have. And, I get it. Money is what keeps the lights on, and while we enjoy attaching fancy labels to obvious activities, we often lose sight of the point of their work. In essence, we try to solve people's problems to drive business goals. 

Business goals may not always be directly about money, but even improvements in internal efficiency or retention are expected to lead to increased revenue over time.

𝗪𝗵𝘆 𝗱𝗼 𝗜 𝘀𝘁𝗶𝗹𝗹 𝘁𝗵𝗶𝗻𝗸 𝗶𝘁'𝘀 𝘁𝗵𝗲 𝘄𝗿𝗼𝗻𝗴 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻? 𝗕𝗲𝗰𝗮𝘂𝘀𝗲 𝘄𝗲 𝗻𝗲𝗲𝗱 𝘁𝗼 𝘀𝗲𝗽𝗮𝗿𝗮𝘁𝗲 𝘄𝗵𝗮𝘁 𝗼𝘂𝗿 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 𝗰𝗮𝗻 𝗶𝗻𝗳𝗹𝘂𝗲𝗻𝗰𝗲 𝗱𝗶𝗿𝗲𝗰𝘁𝗹𝘆 𝗳𝗿𝗼𝗺 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝗼𝗻𝗹𝘆 𝗰𝗼𝗻𝘁𝗿𝗶𝗯𝘂𝘁𝗲 𝘁𝗼.

When I worked in a B2C Monetization team, we focused on imminent success metrics. Even as I was optimizing the design of a freemium feature aimed at luring in non-paying customers, we didn't use "money" to measure its success.

We designed for, and measured how many more people this brought into the funnel. Yes, we observed how the variant cohorts performed mid- to long-term for upsell page and checkout conversion rates.

But that is what Rich Mironov calls "Money Stories."​

In this excellent talk at Product at Heart 2024, he acknowledged the importance of "SWAG predictions" - 𝗦tupid 𝗪ild 𝗔ss 𝗚uesses. We should be explicit about the (internal or external) customer behavior we want to drive, but also acknowledge the uncertainty in the exact ambition.

Even more, we should define the directional contribution we want to make, so that alignment with overarching strategic goals can serve as a guardrail.

Yes, an improvement in usability can lead to a mid-term increase in customer satisfaction, and, thereby, an increase in revenue from retention. BUT, if our goal is to increase revenue from NEW b2b customers this year, this isn't a priority for us at the moment, even if the influenceable customer impact is admirable.

This should lead to what I call directional narrative statements in the context of what you will work on, what you aim to influence, and what you want to contribute to:

• 𝘞𝘦 𝘸𝘪𝘭𝘭 𝘪𝘯𝘤𝘳𝘦𝘢𝘴𝘦 𝘵𝘩𝘦 𝘷𝘪𝘴𝘪𝘣𝘪𝘭𝘪𝘵𝘺 𝘰𝘧 𝘵𝘩𝘦 𝘱𝘳𝘦𝘮𝘪𝘶𝘮 𝘷𝘢𝘭𝘶𝘦 𝘰𝘯 𝘧𝘦𝘢𝘵𝘶𝘳𝘦 𝘟,
• 𝘣𝘦𝘤𝘢𝘶𝘴𝘦 𝘸𝘦 𝘸𝘢𝘯𝘵 𝘵𝘰 𝘮𝘢𝘬𝘦 𝘮𝘰𝘳𝘦 𝘧𝘳𝘦𝘦 𝘤𝘶𝘴𝘵𝘰𝘮𝘦𝘳𝘴 𝘤𝘶𝘳𝘪𝘰𝘶𝘴 𝘢𝘯𝘥 𝘵𝘩𝘦𝘳𝘦𝘣𝘺 𝘤𝘰𝘯𝘷𝘪𝘯𝘤𝘦 𝘵𝘩𝘦𝘮 𝘵𝘰 𝘦𝘯𝘵𝘦𝘳 𝘵𝘩𝘦 𝘴𝘶𝘣𝘴𝘤𝘳𝘪𝘱𝘵𝘪𝘰𝘯 𝘧𝘶𝘯𝘯𝘦𝘭,
• 𝘴𝘰 𝘸𝘦 𝘤𝘢𝘯 𝘤𝘰𝘯𝘵𝘳𝘪𝘣𝘶𝘵𝘦 𝘵𝘰 𝘰𝘶𝘳 𝘰𝘷𝘦𝘳𝘢𝘳𝘤𝘩𝘪𝘯𝘨 𝘨𝘰𝘢𝘭 𝘰𝘧 𝘪𝘯𝘤𝘳𝘦𝘢𝘴𝘪𝘯𝘨 𝘵𝘩𝘦 𝘯𝘶𝘮𝘣𝘦𝘳 𝘰𝘧 𝘱𝘢𝘺𝘪𝘯𝘨 𝘮𝘦𝘮𝘣𝘦𝘳𝘴.

So, the next time you're asked, "𝘏𝘰𝘸 𝘮𝘶𝘤𝘩 𝘔𝘰𝘯𝘦𝘺 𝘸𝘪𝘭𝘭 𝘵𝘩𝘪𝘴 𝘍𝘦𝘢𝘵𝘶𝘳𝘦 𝘮𝘢𝘬𝘦?" share a directional narrative instead of wasting hours on busywork calculating the direct revenue impact of the new dashboard.

A metrics tree isn't an organizational tool.
It's a decision map.
 
That's why just listing metrics in dashboards, strategy jogs, or your OKRs doesn't help with decision-making. They ignore the relationship between them. 
 
A metrics tree fixes that. Not because it gives you more metrics, but because it shows you how they relate to one another.
 
Business Goals like net revenue sit at the top. They tell you the state of company-level success and health. It doesn't tell you what to do next.
 
One level down, you start to see the strategic choices. Which levers matter most? Checkout conversion? Return rates? Shipping time? These are the metrics that reflect whether your strategy is working — but they still move too slowly to steer your weekly work.
 
Go further down the tree, and you find the metrics your team can actually influence. 
Started checkouts on Mobile. Completed purchases for return customers. No. of carts with the wrong-size item. # of Electronics products compared before a purchase is made. 

These are the metrics worth putting in your OKR. Not because they sound impressive, but because you can move them, and you can see whether they're moving fast enough to matter.
 
The tree doesn't just organize your metrics. It tells you what each one is for. That's what I'm helping you do yourself every day this Wednesday in a free 60-minute live session.
 
𝗨𝘀𝗲 𝗥𝗲𝗮𝗹-𝗟𝗶𝗳𝗲 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 𝗧𝗿𝗲𝗲𝘀 𝘁𝗼 𝗗𝗲𝗳𝗶𝗻𝗲 𝗡𝗦𝗠𝘀, 𝗢𝗞𝗥𝘀, 𝗮𝗻𝗱 𝗞𝗣𝗜𝘀 March 4 | 5:00 PM CET | 11:00 AM EST | Live only, no recording.

Register here to join: https://lnkd.in/dCBTVqKH

Here's what participants say about their AI interview experience:

"𝘐𝘵 𝘸𝘢𝘴 𝘭𝘪𝘬𝘦 𝘨𝘦𝘵𝘵𝘪𝘯𝘨 𝘪𝘯𝘵𝘦𝘳𝘷𝘪𝘦𝘸𝘦𝘥 𝘣𝘺 𝘴𝘰𝘮𝘦𝘰𝘯𝘦 𝘸𝘩𝘰 𝘪𝘴 𝘥𝘰𝘪𝘯𝘨 𝘵𝘩𝘪𝘴 𝘰𝘯𝘦 𝘰𝘧 𝘵𝘩𝘦 𝘧𝘪𝘳𝘴𝘵 𝘵𝘪𝘮𝘦𝘴."

"𝘐 𝘧𝘦𝘭𝘵 𝘧𝘳𝘦𝘦 𝘵𝘰 𝘴𝘢𝘺 𝘸𝘩𝘢𝘵 𝘐 𝘸𝘢𝘯𝘵𝘦𝘥 𝘸𝘪𝘵𝘩𝘰𝘶𝘵 '𝘩𝘶𝘳𝘵𝘪𝘯𝘨' 𝘵𝘩𝘦 𝘪𝘯𝘵𝘦𝘳𝘷𝘪𝘦𝘸𝘦𝘳𝘴. 𝘐 𝘧𝘦𝘭𝘵 𝘭𝘪𝘴𝘵𝘦𝘯𝘦𝘥 (𝘢𝘴 𝘵𝘩𝘦 𝘈𝘐 𝘸𝘢𝘴 𝘳𝘦𝘱𝘦𝘢𝘵𝘪𝘯𝘨 𝘮𝘺 𝘱𝘰𝘪𝘯𝘵𝘴). 𝘉𝘶𝘵 𝘴𝘰𝘮𝘦𝘵𝘪𝘮𝘦𝘴 𝘵𝘩𝘦 𝘤𝘰𝘯𝘷𝘦𝘳𝘴𝘢𝘵𝘪𝘰𝘯 𝘸𝘢𝘴 𝘤𝘶𝘵 𝘰𝘧𝘧 𝘸𝘦𝘪𝘳𝘥𝘭𝘺."

"𝘛𝘩𝘦 𝘈𝘐 𝘰𝘧𝘵𝘦𝘯 𝘪𝘯𝘵𝘦𝘳𝘳𝘶𝘱𝘵𝘦𝘥 𝘮𝘦 𝘸𝘩𝘦𝘯 𝘐 𝘸𝘢𝘴 𝘵𝘩𝘪𝘯𝘬𝘪𝘯𝘨. 𝘐𝘯 𝘢𝘥𝘥𝘪𝘵𝘪𝘰𝘯, 𝘪𝘵 𝘰𝘧𝘵𝘦𝘯 𝘴𝘵𝘢𝘳𝘵𝘦𝘥 𝘵𝘰 𝘳𝘦𝘴𝘱𝘰𝘯𝘥, 𝘳𝘦𝘢𝘭𝘪𝘻𝘦𝘥 𝘐 𝘸𝘢𝘴𝘯'𝘵 𝘧𝘪𝘯𝘪𝘴𝘩𝘦𝘥, 𝘢𝘯𝘥 𝘵𝘩𝘦𝘯 𝘵𝘰𝘰𝘬 𝘢 𝘤𝘰𝘮𝘱𝘭𝘦𝘵𝘦𝘭𝘺 𝘥𝘪𝘧𝘧𝘦𝘳𝘦𝘯𝘵 𝘢𝘱𝘱𝘳𝘰𝘢𝘤𝘩, 𝘧𝘰𝘳𝘨𝘦𝘵𝘵𝘪𝘯𝘨 𝘸𝘩𝘢𝘵 𝘪𝘵 𝘩𝘢𝘥 𝘴𝘢𝘪𝘥 𝘣𝘦𝘧𝘰𝘳𝘦. 𝘈𝘧𝘵𝘦𝘳 𝘵𝘸𝘰 𝘰𝘳 𝘵𝘩𝘳𝘦𝘦 𝘪𝘯𝘵𝘦𝘳𝘳𝘶𝘱𝘵𝘪𝘰𝘯𝘴, 𝘐 𝘯𝘢𝘵𝘶𝘳𝘢𝘭𝘭𝘺 𝘭𝘰𝘴𝘵 𝘵𝘩𝘦 𝘥𝘦𝘴𝘪𝘳𝘦 𝘵𝘰 𝘴𝘱𝘦𝘢𝘬 𝘯𝘢𝘵𝘶𝘳𝘢𝘭𝘭𝘺 𝘢𝘯𝘥 𝘧𝘦𝘭𝘭 𝘪𝘯𝘵𝘰 𝘢 𝘵𝘦𝘭𝘦𝘱𝘩𝘰𝘯𝘦 𝘢𝘨𝘦𝘯𝘵'𝘴 𝘸𝘢𝘺 𝘰𝘧 𝘴𝘱𝘦𝘢𝘬𝘪𝘯𝘨."

With a grain of salt, here's what I'm observing:

1. The AI ignores basic principles, such as rotating the starting variant, which impacts the feedback you'll get. Not helpful (even borderline harmful for your insights).
2. It's seemingly easier for people to drop out of AI-led interviews because there's no friction. Closing a browser tab is less uncomfortable than leaving a room.
3. Getting summaries with specific playback markers from feedback shared brings insights alive - ​but you don't need AI-led interviews for that​.

This experiment led me to rethink my high-level view on where AI can be inserted into Product Discovery activities, inspired by the thinking of smart folks like Julia Bastian, Sachin Rekhi, and Ravi Mehta.

Three things your next strategy document should include, whether wrote it or your AI agent.

The most effective strategy document I've seen doesn't worry about the looks or format. Whether it's a scrappy Google Doc or a fancy Miro template, what matters is the quality and cohesiveness of the information it contains.

Make sure what you cover aligns with your company's expected standards to ensure stakeholder understanding and, consequently, buy-in. But make sure you don't skip these parts:

#𝟭 𝗧𝗵𝗲 𝗕𝗮𝘀𝗶𝗰𝘀
You know the drill. Your Product Strategy has a problem to solve.

And, to do that, you need to answer a few key questions. There are a gazillion versions of these out there, but all mostly center around the same core:

• How does this connect to Company Strategy, Product Vision, and Business Goals?
• For which audience (and their adjacent roles) are we trying to solve a problem? What is the problem? What else is solving that problem?
• What will we provide as a solution? What makes our solution more likely to get chosen by the audience? How will we put it into people's hands?
• 
The answers to these questions need to reinforce each other. TikTok ads won't help you reach people aged 70+. You're not just competing against similar software or services, but also against Excel and ChatGPT.

#𝟮 𝗠𝗲𝗮𝘀𝘂𝗿𝗮𝗯𝗹𝗲 𝗗𝗲𝗳𝗶𝗻𝗶𝘁𝗶𝗼𝗻𝘀 𝗼𝗳 𝗦𝘂𝗰𝗰𝗲𝘀𝘀
Struggling to measure if the strategy is working?

Can you answer the simple question: "𝘞𝘩𝘪𝘤𝘩 𝘵𝘩𝘳𝘦𝘦 𝘮𝘦𝘵𝘳𝘪𝘤𝘴 𝘸𝘰𝘶𝘭𝘥 𝘴𝘩𝘰𝘸 𝘵𝘩𝘢𝘵 𝘵𝘩𝘪𝘴 𝘴𝘵𝘳𝘢𝘵𝘦𝘨𝘺 𝘤𝘩𝘰𝘪𝘤𝘦 𝘩𝘢𝘴 𝘸𝘰𝘳𝘬𝘦𝘥 𝘪𝘯 𝘵𝘩𝘦 𝘯𝘦𝘹𝘵 12 𝘮𝘰𝘯𝘵𝘩𝘴?" Make them specific. Not just "Revenue" but "Revenue from Industry xyz." Then, use these to create influenceable team goals.

#𝟯 "𝗦𝘁𝘂𝗽𝗶𝗱𝗶𝘁𝘆-𝗙𝗹𝗶𝗽𝗽𝗶𝗻𝗴" 𝗬𝗼𝘂𝗿 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝗖𝗵𝗼𝗶𝗰𝗲
A real strategy choice should feel slightly uncomfortable. If the opposite of your choice would be plain stupid, it might not be a real choice at all (thank you, R͟o͟g͟e͟r͟ ͟M͟a͟r͟t͟i͟n͟). That's the stupidity flip. A simple way to pressure-test whether your strategy is actually saying something.

𝗕𝗼𝗻𝘂𝘀: "𝗪𝗵𝗮𝘁'𝘀 𝗮𝘁 𝗦𝘁𝗮𝗸𝗲?"
To point out the relevance of your choices and urgency to act, outline the consequences of your product not adapting to a changing landscape. Use a simple formula: If we don't act → [core risk] → which means → [downstream consequence that hits something people already care about].

𝘐𝘧 𝘸𝘦 𝘥𝘰𝘯'𝘵 𝘦𝘢𝘳𝘯 𝘳𝘦𝘱𝘦𝘢𝘵 𝘱𝘶𝘳𝘤𝘩𝘢𝘴𝘦 𝘣𝘦𝘩𝘢𝘷𝘪𝘰𝘳 𝘦𝘢𝘳𝘭𝘺 → 𝘵𝘩𝘦 𝘶𝘯𝘪𝘵 𝘦𝘤𝘰𝘯𝘰𝘮𝘪𝘤𝘴 𝘰𝘧 𝘢𝘤𝘲𝘶𝘪𝘴𝘪𝘵𝘪𝘰𝘯 𝘯𝘦𝘷𝘦𝘳 𝘤𝘭𝘰𝘴𝘦 → 𝘸𝘩𝘪𝘤𝘩 𝘮𝘦𝘢𝘯𝘴 𝘸𝘦'𝘳𝘦 𝘱𝘦𝘳𝘮𝘢𝘯𝘦𝘯𝘵𝘭𝘺 𝘥𝘦𝘱𝘦𝘯𝘥𝘦𝘯𝘵 𝘰𝘯 𝘱𝘢𝘪𝘥 𝘤𝘩𝘢𝘯𝘯𝘦𝘭𝘴 𝘵𝘰 𝘥𝘳𝘪𝘷𝘦 𝘳𝘦𝘷𝘦𝘯𝘶𝘦, 𝘢𝘯𝘥 𝘵𝘩𝘦 𝘣𝘶𝘴𝘪𝘯𝘦𝘴𝘴 𝘮𝘰𝘥𝘦𝘭 𝘰𝘯𝘭𝘺 𝘸𝘰𝘳𝘬𝘴 𝘪𝘧 𝘸𝘦 𝘬𝘦𝘦𝘱 𝘴𝘱𝘦𝘯𝘥𝘪𝘯𝘨.

What can you learn from AI-led Concept Tests - and which insights shouldn't impact your conviction?

There are a few AI-based interviewer tools out there. You might have read takes from a few very knowledgeable people on the topic.

In my coaching practice and workshops, I often discuss the pros and cons of leveraging existing discovery workflows with more efficient and sometimes even more effective AI tools. 

But putting that aside for a moment, I think how Reforge marketed their capability points to an important distinction that is still valid even for AI-scaled practices: 𝗪𝗵𝗲𝗻 𝘁𝗼 𝘂𝘀𝗲 𝗮 𝗺𝗲𝘁𝗵𝗼𝗱 𝗼𝗿 𝘁𝗼𝗼𝗹 𝘁𝗼 𝗮𝗻𝘀𝘄𝗲𝗿 𝘄𝗵𝗶𝗰𝗵 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗳𝗼𝗿 𝘄𝗵𝗶𝗰𝗵 𝗽𝗮𝗿𝘁 𝗼𝗳 𝘆𝗼𝘂𝗿 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗢𝗥 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝘃𝗲 𝗗𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝘆 𝗲𝗳𝗳𝗼𝗿𝘁𝘀.

Here's how I view Reforge's positioning of concept testing:

1. 𝗖𝗵𝗼𝗼𝘀𝗶𝗻𝗴 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗱𝗶𝗿𝗲𝗰𝘁𝗶𝗼𝗻𝘀: Not sure I would agree with the term 𝘴𝘵𝘳𝘢𝘵𝘦𝘨𝘪𝘤 here. I believe you can choose between 𝘥𝘦𝘴𝘪𝘨𝘯 directions. I'm a believer that you can't A/B test your way to product-market fit. And maybe you don't want to bet all your chips on that design or value-prop communication based on (scaled) interview feedback. But you WILL learn which one confuses or delights people more, what they associate this with, or simply which design resonates for what reason.​
   
2. 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 𝗺𝗲𝘀𝘀𝗮𝗴𝗶𝗻𝗴 𝘃𝗮𝗿𝗶𝗮𝘁𝗶𝗼𝗻𝘀 𝗯𝗲𝗳𝗼𝗿𝗲 𝗱𝗲𝘀𝗶𝗴𝗻 𝗶𝗻𝘃𝗲𝘀𝘁𝗺𝗲𝗻𝘁: Absolutely. Looks like a slam dunk opportunity - as long as the tool keeps things like randomizing the order in which the concept variations are shown in mind (to avoid comparison bias) → t͟a͟k͟e͟ ͟t͟h͟e͟ ͟i͟n͟t͟e͟r͟v͟i͟e͟w͟ to tell me if the variant order was randomized)
   
3. 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗻𝗴 𝗽𝗿𝗼𝗯𝗹𝗲𝗺-𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗳𝗶𝘁 𝗯𝗲𝗳𝗼𝗿𝗲 𝗽𝗿𝗼𝘁𝗼𝘁𝘆𝗽𝗲𝘀: "Would users actually adopt it?" is a red flag in interview-style research, regardless of scale. Because humans suck at predicting future behavior - especially when it comes to trading time or money for a new solution. Concept testing gives you insights into the fundamental customer sentiments about a visual design or gaps in understanding. But they won't help you predict what will get adopted or bought. To get strong evidence about feature adoption or willingness-to-pay, you need to turn to behavioral methods, not attitudinal methods like interviews (no matter if a human or an AI does them).

To be clear, this is by no means a dunk on Reforge's work. I appreciate them pushing what's possible. I use this merely to explore and share which views and principles are still true and which need updating in the face of more AI-enabled workflows.

I'm excited about the questions, conversations, and possibilities these tools enable for product management practices across the board.

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