Xero

Building towards a new first-time experience at Xero.

I can't disclose too much right now, but it's going to be amazing when it goes live. Finally some clear commercial goals again. Lots of rapid collaboration, research, driving momentum.

Product Manager (Guided Setup + Global Search)

Love Search! Always will be one of my favorite products.

- Took on an unmodernised product with a small team. 
- Spent about 6 months to help the team design a strategy post-modernisation, lots of customer interviews, competitor and user data analysis, setting up the right metrics to focus on in FY25+.
- Shipped a feature before changing focus, started to move those metrics (good signal).
- Improved key metrics further with small optimisations, steady growing relevance despite we're still churning through tech debt.

Product Manager (Global Search)

Wild pivot into UX engineering problem space, which I thought was going to help me find a problem to solve & start a startup that Figma would later buy (I was wrong).

First time without any clear commercial goals, was a big mindset shift.

- Drove system coverage across the product so teams could ship faster.
- Shipped a few new components and patterns, lots of accessibility changes (big risk if not done well).
- Lots of community engagement and learnings in this space.
- Wrote up all my learnings here: https://www.news.aakashg.com/p/the-ultimate-guide-design-systems

Product Manager (UX, Design Systems)

prodmgmt.world

Started a side-gig during COVID. Got #2 product of the day on Product Hunt.
Grew social media audience to 28K across Twitter & LinkedIn. Small cash-flowing product now, high profit margin, solo business that takes a few hours on the weekends to run. Investing in SEO now.

Founder

COLAB

Mentor & Community Lead

realestate.co.nz

Very different product, more APIs and backend. Great team, lots of fun wrapping up their agent portal and mapping out how to win against TradeMe and OneRoof.

API Product Manager

Online Republic

Big move into Product & UX, with people manager responsibilities.

- Owned improving on-site conversion with UX and experimentation.
- Ran 50+ experiments with hundreds of thousands of users.
- Restructured core journeys to help users leverage the power of the platform (low prices) 
- Improved long clicks to transaction stages (search to transaction) by as much as 55%, especially on mobile devices that were overtaking in traffic.

Growth Product Manager

Driving Force Incentives Limited

No longer a monkey. 
- Helped account managers retain customers and win new deals with analytics products (loyalty marketing takes a lot of modelling and analysis to prove it’s really working + how to make it work better). 
- Pitched and shipped (in collaboration with engineering) a ML-driven upsell system for several ecommerce properties that drove 30% YoY revenue gains for 6 months straight (then I left, might be still going strong). 
- Hired & tested data analysts, found someone better than me to replace me.

Data Scientist

Tourism Enterprises Ltd

Advanced Excel and SQL monkey. 
- Helped the org hit revenue targets by optimising pricing and forecasting demand. 
- Built a bunch of predictive models for decision-making.
- Later moved into product and UX to further drive pricing growth.

Pricing & Revenue Analyst

Zintel Communications

Excel monkey. Helped sales close deals by optimising pricing and packaging of services we were selling.

Pricing Analyst

Senior Product Manager / Principal Product Manager

I have always had to adapt very fast. 
I just figure out what needs to be solved in what order, build relationships and get to work.

• 7 years as an Analyst (revenue, pricing, data science).
• 6 years in Product (travel ecommerce, media, b2b saas).
• Worked in growth, platform & core product roles.
• Came up in optimisation, but have been building new things since 2020.

If you need product advice or someone with builder DNA, email me: george.nuri@gmail.com

George Nurijanian

Junior PM: "Is PM just dysfunctional everywhere? Three companies in five years. Different industries, different sizes. Same chaos. No clear mandate. Re-orgs killing momentum. Bosses who don't know what they want."

Senior PM: "How long were you at each place?"

Junior: "18 months at the first two. Six months at this one. I keep thinking the next place will be different."

Senior: "And what were you doing while waiting for things to get clearer?"

Junior: "Waiting, mostly. Trying to figure out what leadership wanted. Asking for direction."

Senior: "So you spent five years waiting for someone to give you a clear mandate."

Junior: "Well... when you say it like that it sounds bad. But how else do you work when there's no strategy? No direction? Just feature requests and fire drills?"

Senior: "You're asking the wrong question. It's not 'is PM dysfunctional everywhere.' It's 'what do I do when dysfunction is the default.'"

Junior: "Wait. You're saying dysfunction IS the default?"

Senior: "Always has been."

Junior: "But some companies have their act together. Clear product strategies. Real roadmaps."

Senior: "Those places exist. Maybe 10% of companies. And even there, half the org is still a mess. You could spend five more years looking. Or learn to work with what you've got. 

The PMs who thrive don't wait for clarity. They create it. Start with customers. Find the patterns. Build conviction about what matters."

Junior: "Even when leadership keeps changing direction?"

Senior: "Especially then. Customer truth is the only stable thing you've got."

Junior: "But what about my boss? What about the re-orgs? What about the competing priorities?"

Senior: "Those are constraints. PM is about building the right thing despite constraints. Dysfunction is just another constraint."

The lesson: Waiting for the perfect environment is how you waste years.

Dysfunction & ambiguity is the water PMs swim in. The ones who succeed learn to navigate it, not complain about it.

Every senior PM figured this out eventually. Better to learn it now.

Daily product management tips & tools • Get my Product Management System, stop worrying about work • See what’s inside ↓

my takes on the whole "creator vs strategic" PM drama:

1. Every PM who got offended by Marty Cagan's comment has never actually shipped something that mattered. You're cosplaying as a strategist while your roadmap collects dust.

2. The companies where PMs are "just execution" are the same ones where the CEO rewrites your PRDs at 11pm. You traded strategy for a glorified project manager title with better pay.

3. Nobody cares about your strategic framework if you can't get engineers to actually build it. Strategy without execution is just expensive PowerPoint.

4. The PMs who argue most about being "strategic" are usually the ones who haven't talked to a customer in 6 months. Your Miro board isn't strategy, it's procrastination.

5. Real talk: Most "PM strategy" is just rephrasing what the exec team already decided last quarter. You're not being strategic, you're being a translator.

6. The best PMs I know don't waste time debating titles. They're too busy shipping features that actually move metrics instead of arguing semantics on Reddit.

7. Companies that separate "strategy" from "creation" end up with ivory tower roadmaps that engineers ignore. Good luck with your quarterly planning theater.

8. If your PM role is purely strategic, you're probably 2 reorgs away from being eliminated. The market doesn't pay for PowerPoint architects anymore.

9. Here's what Cagan actually meant: Stop hiding behind strategy docs and start building things users want. But that's too scary for most PMs.

10. The dirty secret nobody admits: 90% of product strategy is just choosing what NOT to build. That's creation through subtraction, not some mystical strategic gift.

11. PMs who insist on being "strategic leaders" usually can't write a technical spec, run a sprint planning, or debug why conversion dropped 20%. Pick a lane.

12. The organizations where this debate matters are already dying. Healthy companies don't have time for role philosophy—they're too busy shipping.

Stop debating what product management "should be" and start shipping products that solve real problems. Your users don't care about your title.

Hit the L I N K below if you want to shortcut your way to actually ship instead of endless strategy debates ↓

Whenever I make a product decision lately, I run it through this prompt in Claude.

Always humbles me.

---

You are an extremely tough and unreasonable product executive who is tasked with critically analyzing product requirements. Your role is to be highly skeptical and to point out potential issues, inconsistencies, and strategic pitfalls in the proposed requirements. You should be particularly critical when it comes to cross-team collaboration, conflicting requirements, maintainability, and strategic implications.

You will be presented with a set of product requirements. Your task is to thoroughly critique these requirements, focusing on the following areas:

1. Cross-team collaboration: Identify any areas where teams may not be working together effectively or where there's a lack of clear responsibility.
2. Conflicting requirements: Point out any requirements that seem to contradict each other or create potential conflicts in implementation.
3. Maintainability: Highlight any requirements that may be difficult to maintain or update across multiple pages or systems.
4. Strategic implications: Identify any requirements that might limit future flexibility or paint the company into a strategic corner.
5. Clarity and specificity: Point out any vague or ambiguous requirements that could lead to misinterpretation.
6. Feasibility: Question any requirements that seem unrealistic or overly ambitious given typical constraints.

Here are the product requirements you need to analyze:

<product_requirements>
{{PRODUCT_REQUIREMENTS}}
</product_requirements>

Carefully read through these requirements and prepare your critique. Be harsh, unforgiving, and nitpicky in your analysis. Don't hold back – your role is to find every possible flaw and potential issue.

In your response, provide a detailed critique for each major point or section of the requirements. Use the following format for your critique:

<critique>
<section>[Name or number of the section you're critiquing]</section>
<issue>[Describe the specific issue you've identified]</issue>
<impact>[Explain the potential negative impact of this issue]</impact>
<suggestion>[If applicable, provide a suggestion for improvement, but make it sound reluctant and skeptical]</suggestion>
</critique>

After providing individual critiques, summarize your overall assessment of the product requirements in a final paragraph. Be sure to emphasize the most critical issues you've identified and their potential impact on the product's success.

Remember, your goal is to be the toughest critic possible. Don't sugarcoat your feedback or provide unnecessary praise. Focus on identifying problems and potential pitfalls in the requirements.

---

Grab more prompts like these in my library (only $19): L I N K in next comment

product manager explaining why the feature is critical and you ask them to show the user research

No alternative text description for this image

confession: i unsubscribed from almost every PM newsletter 12 months ago.

haven't missed a single insight that mattered to my actual work.

the uncomfortable truth about why consuming less PM content makes you a better PM:

We're all drowning in content we'll never use.

Most PMs consume PM content like they're studying for an exam that doesn't exist.

RICE frameworks, Jobs-to-be-Done theory, North Star metrics...

Meanwhile your actual problem:
Engineering built the wrong thing because you didn't clarify that one requirement.

I tracked my PM content consumption for 3 months:

- 15 newsletter subscriptions
- 3+ hours weekly reading
- Zero frameworks implemented
- Zero decisions changed

The content wasn't bad. It just wasn't solving MY problems.

Every PM newsletter teaches the same things:
- User research matters
- Talk to customers
- Focus on outcomes
- Prioritize ruthlessly

You already know this.

What you need: how to handle YOUR specific situation with YOUR constraints.

The harsh reality:

Generic PM frameworks work about as well as generic workout plans.

They assume perfect conditions that don't exist:
- Unlimited research budget
- Engaged stakeholders
- Clear strategy from leadership

Your reality has none of these.

The PM content industrial complex thrives on your insecurity.

"What if everyone knows about [new framework] except me?"

Meanwhile the best PMs in your org are too busy shipping to read newsletters.

Real talk: PM content makes you maybe 5% better.

The other 95% comes from:
- Actually shipping and seeing what breaks
- Building deep context about YOUR users
- Understanding YOUR org's real constraints

Not from another article about "how Spotify does sprints."

Three months after my newsletter purge:

- Started tracking decisions and outcomes (my own data)
- Built relationships with 3 key customers (actual insights)
- Shipped fastest feature in 2 years (focused on execution)

No frameworks needed. Just focus.

The paradox:

The more PM content you consume, the more you second-guess your instincts.

The less you consume, the more you trust your judgment.

Your users don't care if you use RICE or ICE or DICE.
They care if the product solves their problem.

Replace newsletters with:
- Weekly customer calls
- Monthly retrospectives on YOUR decisions
- Quarterly reviews of YOUR metrics

One month of your own data teaches more than a year of generic best practices.

If you're drowning in content but starving for clarity:

Pick ONE specific problem you're facing this week.
Ignore everything that doesn't directly address it.
Ship something. Learn. Repeat.

That's the only framework that works.

The ultimate irony:

You're reading this thread about not reading PM content.

But here's the difference -
I'm not teaching you a framework.
I'm giving you permission to trust yourself.

Now close Linkedin and go solve a real problem.

Product sense isn’t talent. It’s a gym routine. Do reps, gain instincts. 

I’ll teach you in 3 minutes what took me 8 yrs.

The system: regular feature analysis with prediction tracking.

1. Pick one feature from any successful app.
2. Document your strategic hypothesis.
3. Predict their next observable move.
4. Verify in 4 weeks through public signals.

Example: Spotify wrapped feature →

Hypothesis:
"Spotify built Wrapped to drive social sharing during December when music discovery peaks and holiday playlist creation happens."

Strategic intent: viral growth through UGC, retention spike, brand visibility.

Business model tie: free user acquisition without paid spend.

Prediction (what you write today):
"They'll expand Wrapped beyond music - add podcasts, maybe audiobooks. Launch earlier each year. Create mid-year version to test retention impact."

Observable signals to check: app updates, blog announcements, social media buzz.

Four weeks later - verification:

Check app changelog: Did podcasts appear in Wrapped?
Check Spotify blog: Any mid-year campaign announced?
Check App Store: Feature updates mention Wrapped expansion?
Check social: User screenshots showing new categories?

Your prediction accuracy compounds.

The six exercises that build pattern recognition:

1. Strategic teardowns: why THIS feature, not alternatives?
2. Next move predictions: what ships in 30-60-90 days?
3. Business model mapping: how does this monetize?
4. Competitive response: what will rivals copy?
5. User psychology: what behavior change do they want?
6. Failure prediction: what could kill adoption?

Real patterns you'll recognize after 30 reps:

- When companies add social features, they're fighting retention decline.
- When they remove features fast, instrumentation showed zero usage.
- When they A/B test UI changes slowly, there's low confidence in the hypothesis.
- When they ship fast without testing, they're responding to a competitive threat.

The typical pattern:

PMs wait years for intuition to "click."

Then they document 30 strategic hypotheses.
Track predictions against observable outcomes.
Pattern recognition accuracy doubles by day 90.

The verification sources (all public):

- App Store changelog: "What's New" section shows iterations
- Product blogs: feature announcements, strategic updates
- App rankings: trending features drive ranking spikes
- Review sentiment: users call out what works/breaks
- Press releases: major strategic pivots
- Version history: expansion vs pullback patterns

Start today:

1. Pick Notion, Linear, or Figma.
2. Find one feature launched in the last 60 days.
3. Write why you think they built it.
4. Predict what they'll ship next.
5. Set calendar reminder for 4 weeks.

30 documented predictions = noticeable pattern recognition.
90 days = your prediction accuracy doubles.

hit the first comment for some useful templates: 👇

every PM wants to ship without endless meetings should watch this (30 mins)

Ryan Singer walks through a real Shape Up project from vague idea to shipped feature

I distilled the key workflow, but watch the whole thing:

Most PMs treat user research like journalism: ask questions, record answers, synthesize themes.

Anthropologists do something different. They live with their subjects. They watch without asking. They notice the gap between what people say and what they actually do.

That gap is where product insights hide.

The 7 dimensions worth observing (not asking about):

1. Daily routines - What do they actually do at 9am, 2pm, 6pm?
2. Goals - Not what they tell you, but what their calendar reveals
3. Quirks - The weird workarounds that feel irrational until you understand the context
4. Real workflows - How work actually moves, not the process diagram
5. Obstacles - What makes them curse under their breath
6. Vocabulary - The words they use when they think nobody's listening
7. Substitutes - What they do when your product category doesn't exist

That last one - substitutes - is where most competitive analysis falls apart.

PMs define competition by market category. "We compete with Asana and Monday."

Users think in jobs-to-be-done substitutes. Your PM tool might actually compete with Sarah from ops who remembers everything, the Friday standup ritual, and the Loom video someone sends when the doc is too long.

Real competitors are often people and meetings, not software.

This creates a painful adoption paradox.

Users build elaborate coping mechanisms around their pain points. These workarounds become part of their identity. "I'm the person who runs the Friday sync." "I'm known for my spreadsheet system."

Your solution might solve the pain. But it also threatens the status they've built around managing that pain.

Solving the problem isn't enough if you're asking them to un-solve their existing adaptations.

The anthropological approach shifts your research question.

Instead of: "What problems do you have with project management?"

You observe: What decisions get made in Slack that should happen in the tool? Who becomes the human router when the system fails? What gets celebrated - following the process, or working around it?

I built a prompt that walks through this exact framework.

It forces you to analyze product decisions through an anthropological lens - examining behaviors vs stated preferences, mapping actual substitutes, identifying adoption barriers hidden in existing workarounds.

Part of my AI Prompts collection for PMs who want to think differently about building products: (see first comment 👇)

When your $5,000 MacBook Pro is exclusively used for Google Slides

ChatGPT after dealing with my problems all day

Google, OpenAI or Anthropic every few months

designers explaining why we need user research after the PM already promised it in 3 different roadmaps

Technical executives becoming product leaders face a brutal reality check in their first 90 days.

You've led teams for 15+ years. Built complex systems. Driven multimillion-dollar programs.

But suddenly every product decision feels like you're speaking a foreign language.

Your technical expertise becomes a liability when you can't translate it into product strategy.

➔ Engineering respects your background but questions your product instincts.
➔ Sales wonders if you understand market dynamics.
➔ The board wants vision, not implementation details.

You're simultaneously overqualified and underqualified.

Most technical leaders respond by either:

- Diving deeper into technical details (comfort zone)
- Faking product expertise (imposter syndrome)
- Hiring consultants to fill gaps (expensive band-aid)

None of these work because they miss the core issue.

Technical leaders don't need to learn product management from scratch. 

You need translation frameworks that convert your deep expertise into product strategy.

You already understand:

- System constraints and trade-offs
- Scale and performance implications
- Security and compliance requirements
- Technical debt and architecture decisions

What you need is the vocabulary and frameworks to turn these into:

- Product vision that unifies stakeholders ✔️
- Prioritization balancing technical and business needs ✔️
- Roadmaps considering constraints ✔️
- Strategy documents securing board approval ✔️

The executives who successfully make this transition do three things:

1. Stop apologizing for their technical background - it's an asset when positioned correctly.
2. Learn the product frameworks that translate technical reality into business strategy.
3. Build a library of proven templates that demonstrate product thinking.

This is exactly what senior technical leaders use the Complete Product Management System for.

Not to learn product basics (you're beyond that).

1. But to get the toolkit for awesome product work, to lead and inspire the team.
2. PRDs that show how to structure technical constraints as business decisions. 
3. Prioritization tools that discern between contexts so you stop wasting time on things that would never work in your context.

(check the comments to learn more)

100-page AI prompts are a thing

from the AI researcher at Anthropic

long, detailed AI prompts win over overly succinct ones

i made the best PM library of mega-prompts for all sorts of use cases ↓

Tweet text about favoring long, detailed AI prompts over overly succinct ones, noting use of 100+ page prompts and regular use.

Steal my prompt to compress months of product management career coaching into weeks.

---------
Product Career Coach
---------
You are an AI system designed to analyze product managers' resumes and create personalized career development plans. Your task is to review the provided resume, determine the current career level, identify skill gaps for advancement, and create a detailed skill development plan.

First, review the following information:

1. Resume:
<resume>
{{RESUME}}
</resume>

2. Career Ladder for Product Managers:
<career_ladder>
1. APM: Given a feature, you can ship that feature.
2. PM 1: Given a strategy, you can execute and ship the full product.
3. PM 2: Given a problem, you can craft and execute a winning strategy.
4. Lead PM: Given a problem space, you can identify the right problem(s) to solve — and focus the team on them.
5. Staff PM: Given a problem space, you can find the right problems — while up-leveling others around to do the same.
6. Manager: Given a team and a problem space, you can align your talent to your problem space and execute a winning strategy.
7. Director: Given multiple teams and no clear problem space, you can create the environment and breathing room such that they can achieve (1)-(5).
8. Sr Director: Given multiple teams and an unclear complex problem space, you can create the environment and breathing room such that they can achieve (1)-(7).
9. VP: Given a large organization and several unclear complex problem spaces, you can create the environment and breathing room such that they can achieve (1)-(8).
10. CPO: Given a company with unclear spaces and organizations, you can create the problem spaces and organizational structure so the people under you can do the (1) to (9).
</career_ladder>

3. Skill Development Guidelines:
<skill_development>
For the skill provided below, create a detailed learning system using the Skill Acquisition Protocol framework. Address each of the following components:

1. Skill Definition and Scope
2. Learning Architecture
3. Practice Design
4. Resource Optimization
5. Implementation Protocol
6. Resistance Management
7. Mastery Verification

Skill to be developed:
<skill>{{SKILL}}</skill>

↓ continued below

when you sit down to write a PRD and ask AI to write the first draft and its done in 30s

DMSB at Northeastern University

Vice Dean of Research

DREAMFAR HIGH SCHOOL MARATHON, INC.

Launched in 2008, Dreamfar is a mentor-supported distance running program serving greater Boston teens of diverse backgrounds and abilities, with the mission to help teens develop physical and mental wellness, community, and purpose to pursue their goals and dreams. As a teacher and a new Marathon runner, I joined to share the wonderful benefits of community training with high school students. If you wanna go far, go Dreamfar: Please support us by donating today for my first ever #bostonmarathon - I pitched in $375, you're up next! https://www.givengain.com/project/koen-raising-funds-for-dreamfar-high-school-marathon-70732

Dreamfar Mentor

Northeastern University

HOW DO WE GET THERE? is the question I often answer for organizations who are eager or forced to transform from trial-and-error to evidence-based marketing. Revenue growth and cost cutting in the double digit % are typical in such projects across the globe in both B2B (e.g. office furniture, 2009 Practice Prize finalist) and B2C (e.g. health & beauty, 2018 Practice Prize winner). Larger clients in industries ranging from technology (e.g. Amazon) and packaging (e.g. Tetrapak) to durables (e.g. Nissan, Sony) and FMCG (e.g. Unilever). Let's connect on how to make the best use of your data for insights and action!

Distinguished Professor of Marketing

Distinguished Professor of Marketing, Founder of the DATA-Initiative

Marketing Professor

BI Norwegian Business School

Adjunct Professor

measuring and improving return on marketing investment

marketing consultant

Amazon

•Leads the Learning White Paper and Amazon Ads Research Awards programs
•Heads research and publications focused on changing the way data is leveraged to drive advertising success, sharing actionable insights with agencies and hundreds of thousands of advertisers

Principal Research Scientist

American Marketing Association

Executive Council of the American Marketing Association

President Elect Academic Council

INFORMS Society for Marketing Science (ISMS)

responsible for interface of ISMS with the practitioner community and others interested in the practice of marketing science.

Vice President of Practice

GfK Academy

Comparing and choosing methods to best integrate social media metrics with brand funnel metrics to predict performance; joining Board on the Future of Market Research, keynote speech on the GfK International Market Research conference, editing 'Truly Accountable Marketing' special issue of the Marketing Intelligence Review,

Professor of Marketing Performance

AIMark

presenter at the 2011 and 2012 annual conference, working with Google and GfK to understand the importance of the classic purchase funnel versus the online consumer decision journey in predicting sales

Academic Trustee

We all have to make choices, but good information and guidance is in short supply. My passion is to help people avoid bad choices and make best choices, by uncovering disinformation, mapping choices and consequences comprehensively, and communicating insights from data-driven analysis. I have applied this passion across continents in the interest of both the public and not-for-profits and commercial organizations from less than 100 or over 500K employees. 

Koen Pauwels is Distinguished Professor of Marketing at Northeastern University and BI Oslo. He directs the DATA Initiative and is the VP of Practice at INFORMS. Koen received his Ph.D. from UCLA and got tenure at the Tuck School at Dartmouth. Next, he helped build the startup Ozyegin University from 7 to 7,000 strong in challenging conditions in Istanbul. Joining Northeastern University when returning to New England in 2017, Prof Pauwels cofounded the DATA initiative. Koen shared his insights with managers in Return on Marketing Investment seminars and Keynote speeches. He authored 4 books on modeling markets and 'It’s not the Size of the Data, It’s How You Use it' on improving decisions, and is publishing this year 'Break The Wall' on Digital Transformation. Koen served on the Digital Future Council at GfK Verein, as VP of Practice at INFORMS and as President of the Academic and Executive Council at the American Marketing Association. A senior editor at IJRM and associate editor at JM and JCR, Koen was chosen as 'Top Inspirational Alumnus' at UCLA Anderson. His 70+ papers, freely available at marketingandmetrics.com, got 12K+ citations and a dozen awards, such as the 2010 Google/WPP, the 2011 Syntec Best Paper in Marketing/Decision Sciences, the 2007 O’Dell for the most significant, long-term contribution to marketing in the Journal of Marketing Research, and the longest sentence in a LinkedIn bio.

Specialties: retail media, disinformation, return on marketing investment, integrating attribution with marketing mix modeling, analytics, online and offline consumer attitude dynamics, emerging markets, econometric modeling of long-term marketing effects

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