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

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.

George Nurijanian

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

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 👇)

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:

No alternative text description for this image

Most PMs collect frameworks like Pokemon cards.

RICE, ICE, KANO, MoSCoW, AARRR...

They memorize them. Apply them once. Get a generic output. Move on.

Then wonder why their prioritization still feels like guessing.

Frameworks fail PMs because of how they're used.

The typical pattern: PM has a decision to make. Opens a framework. Plugs in numbers. Gets an output. Presents it as rigorous analysis.

It's framework theater. The output looks legitimate but the thinking is shallow.

Elite PMs use frameworks as interrogation protocols.

A framework's job is to surface what you don't know. To generate better questions about your situation. To attack your own assumptions before stakeholders do.

The output of first application should be discomfort, not confidence.

The two-pass method:

First pass: Apply the framework.
Output: A list of questions you can't answer. Assumptions you haven't validated. Edge cases you hadn't considered.

Second pass: Answer those questions. Then reapply.
Output: Now you actually understand your problem.

When your framework output is generic, your input was vague.

Shallow: "We should prioritize Feature X because it scored highest on RICE."

Deep: "We should prioritize Feature X because when we examined our reach assumptions, we discovered 73% of our target segment doesn't have the prerequisite workflow in place. That changes everything about timing."

Same framework. Different depth of interrogation.

The interview question that separates senior from staff:

"Tell me about a time you chose NOT to use a popular framework. What did you use instead and why?"

Juniors memorize frameworks.
Seniors apply them.
Staff PMs know when frameworks will mislead them.

👁️ hit the comment below for something special ↓

ChatGPT after dealing with my problems all day

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

taplio

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