Senior Product Manager · Oslo, Norway

Glenn
Svendsen

I turn complex problems into simple products people trust. Discovery-led, data-backed, and technical enough to prototype the first version myself.

See my work Get in touch
5+
years in product management
B2B · B2C · B2G
products for businesses, consumers and government
20+
countries I've shipped to, across Europe, ANZ & the US
01About
Portrait of Glenn Svendsen

Hi, I'm Glenn.

I'm a Senior Product Manager at Arrive (formerly EasyPark Group), where I own parking policies and pricing across Europe, ANZ and the US.

My work sits where complex systems meet real people: operators configuring prices for thousands of zones, where one wrong field means a driver pays when parking should have been free. I like taking that kind of complexity and making it safe, simple and fast.

I lean on continuous discovery, hard data and hands-on prototyping. I'm comfortable in the technical details, whether that's digging into the data, reading an API response or tracing why a price came out wrong. It makes me quicker to work with engineers, not a replacement for them. When it helps a decision, I'll build the first version myself, often with AI, sometimes during the meeting.

5+ years in product B2B · B2C · B2G Based in Oslo
02What I do

How I work.

Four things I bring to a product team, each backed by a case study below.

01

Discovery that changes the roadmap

Jobs to be Done, opportunity solution trees and a steady rhythm of user interviews, so we build the missing piece instead of the loudest request.

66→87%CSAT lift
02

Making complex platforms safe

Pricing engines, configuration tools and platform migrations, where mistakes cost real money. I help design guardrails that catch errors without slowing people down, and I'm trained in incident management for when things still go wrong.

−36%misconfigurations
03

Decisions backed by data

Hands-on analysis with data from Snowflake and Mixpanel: sizing opportunities, segmenting users and finding root causes, so priorities rest on evidence rather than opinion.

71%coverage at MVP
04

Close to engineering

I understand the systems well enough to investigate problems with engineers and to build working prototypes in hours, so we can test an idea with stakeholders before committing to build it properly.

<1 hridea to validated prototype
Toolkit

Discovery

  • Continuous discovery
  • User interviews
  • Jobs to be Done
  • Opportunity solution trees
  • Dovetail

Data

  • Snowflake
  • Mixpanel
  • Segmentation & scoring
  • Root-cause analysis

Leadership

  • Stakeholder management
  • OKRs
  • Incident management

Build

  • AI prototyping
  • JavaScript · React Native
03Selected work

Case studies.

Three in depth, three more below. Each one covers the problem, my role, and what changed.

Arrive (former EasyPark Group) 2024 Case 01 / 06

Background
Validation

A silent safety net that catches pricing mistakes before drivers ever see them.

My role
Product Manager. I framed the problem, led the root-cause analysis, and worked with configuration experts and engineering to turn the patterns into validation rules and a non-blocking experience.
36%
Fewer misconfigurations year-on-year
33
Rules checked on every save
0
Blocking pop-ups. Checks never interrupt the workflow
Tariff admin — Weekend tariff Interactive
Time based tariffsWeekend · 4 periods
Weekend tariff: price per hour for each period. Edit a price and press Save to run the validator.
PeriodTimePrice / hour
108:00–12:00
212:00–17:00
317:00–20:00
420:00–23:00
✓ Saved · not blocked
Tariff validation 1 error

Errors (1)

  • Price is far above the median for this tariff.

    Period 3 · €250.00 is 91× the median

    PRICE_SPIKE_MEDIAN
Try it: change a price (try 0 or 99) and save. Issues show up quietly on the shield. Click it to review or acknowledge them.

Problem

Tariff configurations are detailed and complex, and small mistakes are easy to miss by eye. The goal was to catch them where they start, before they could ever reach drivers, without slowing operators down.

What I did

I led a root-cause analysis across past configuration issues. Most fell into a few predictable patterns:

  • Price spikes against the median
  • Zero prices on active periods
  • Caps set to zero, inverted date ranges
  • Expired or overlapping tables

Together with configuration experts and engineering, I turned those patterns into 33 validation rules and shaped how they show up: checks run on every save and flag issues quietly, without ever blocking the save.

Outcome

After launch, misconfigurations fell 36% year-on-year. Issues are caught where they start, operators stay in their flow, and acknowledged warnings leave a clear audit trail.

Arrive (former EasyPark Group) 2024 Case 02 / 06

Mass Price
Validator

From stakeholder call to validated prototype in under an hour, built live with AI.

My role
Product Manager. I framed the problem with the configuration experts, built the first working prototype myself with AI during the call, and handed a validated concept to engineering the same day.
<1 hr
From problem to validated prototype, handed to engineering the same day
0
Holiday pricing incidents since release
1,000+
US zones checked in a single run
From problem to agreed solution, inside one call Built live with AI, stakeholders on the call
  1. Problem raisedOne missed holiday setting across thousands of US zones means drivers get charged.
  2. Build, show, adjust, liveBack and forth with stakeholders, making small improvements on the fly.
  3. Agreed before the call endedValidated as the right solution by the people who'd use it.
  4. Handed to engineering the same dayRebuilt properly and securely for production.
Instead of
  1. Write spec
  2. Design
  3. Review
  4. Build
  5. First feedback
Price testing tool — Validate pricing at scale Interactive
Holidays to test
Price call from 12:00 for
Not run yet
–Total calls
–Free ($0)
–Paid (price > 0)
Results
ZoneDateWindowPriceResult

Run the test to see results.

Try it: pick holidays, change the duration and run the test. Dummy data. Zones that charge on a free holiday get flagged.

Problem

Configuration experts managing parking across major US cities had a scale problem. A single nationwide holiday touches thousands of zones, and one missed configuration means drivers get charged when parking should be free.

What I did

Instead of writing a spec, I built a working prototype with AI during the stakeholder call itself. Knowing how pricing is configured and calculated let me shape it around how the experts actually work. We tested it live and adjusted it on the fly until it did what they needed. Engineering then built the production version properly and securely.

  • Pick multiple holiday dates
  • Set time windows and durations
  • Run price calls across every zone
  • Flag any zone that returns a price above zero

Outcome

Since release there have been zero reported holiday pricing incidents. The tool turned out to be flexible enough that configuration experts now use it for general pricing QA across markets, not just holidays.

Arrive (former EasyPark Group) 2024 Case 03 / 06

Self-Service
Discovery

Is self-service pricing viable? I answered with data before a line of code was written.

My role
Product Manager. I owned the discovery end to end: the data work in Snowflake, the complexity model, and the MVP recommendation.
5,640+
EU operators scored and segmented
52%
Ready for self-service today
71%
Covered by the MVP (feature tier 8)
One feature tier takes coverage from 16% to 71% EU · 5,641 operators · cumulative share covered
F1 · 2%
F2 · 2%
F3 · 4%
F4 · 4%
F5 · 8%
F6 · 8%
F7 · 16%
F8 · MVP · 71%
F9 · 79%
F10 · 80%
F11 · 86%
F12 · 92%

Tier 8 became the MVP scope. Everything after it adds far less.

Half of all operators are simple enough to self-serve today

  • 10.3%Simple
  • 41.7%Moderate
  • 23.1%Complex
  • 25.0%Very complex

Problem

Operators couldn't update their own prices. Every rate change, however routine, needed a configuration expert. Was a self-service product viable, and what would it have to cover to be useful across the whole operator base?

What I did

I pulled tariff data for 5,640+ EU operators from Snowflake and built a 0–25 complexity score across 39 configuration dimensions, splitting operators into four tiers. I checked all 40 tariff fields for whether operators could safely edit them (24 could), then modelled how much of the operator base each feature tier would cover.

Outcome

The analysis gave the team a data-backed MVP scope with measured coverage at every milestone: 52% of operators viable right away, 71% covered by tier 8. Price changes, the most requested operator action, came out as the clear place to start.

Arrive (former EasyPark Group) 2023 — Ongoing Case 04 / 06

Global Pricing Engine

Owning the domain behind 2.8B+ price requests a year, and making it easier to configure correctly.

My role
Product Manager for the pricing domain since 2023: discovery, prioritisation, a regular rhythm of user interviews, and close work with engineering on root causes.
2.8B+
Price requests served in 2025
6,000+
Parking operators globally
87%
Operator CSAT, up from 66%
Operator CSAT up 21 points Before vs. after
Before
66%
After
87%
percentage points+21

Problem

Pricing across 20+ countries, each with its own regulations, currencies and operator models, runs on one shared engine. Every new market adds edge cases, which makes getting configuration right harder as the platform grows.

What I did

I took ownership of the domain and ran structured discovery to learn where operators needed it to work better. I prioritised the edge cases that mattered most to them, dug into root causes together with engineering, and set up regular user interviews to keep improvements grounded in real needs.

Outcome

Over that period, operator CSAT rose from 66% to 87%. The engine runs live in every active market with 100% uptime over the last 24 months, and engineering time has shifted from maintenance to growth.

Arrive (former EasyPark Group) 2024 Case 05 / 06

Config Copy

A small feature that removed the most repetitive job operators had.

My role
Product Manager. I ran the discovery with Jobs to be Done and an opportunity solution tree, identified copy as the core job, and prioritised it with engineering.
74%
Faster to create a configuration
−78 sec
Saved on every configuration
JTBD
Found with Jobs to be Done and an opportunity solution tree
74% less time per configuration Sped up 30×
From scratch105 sec
With copy27 sec

In the time it took to build one configuration from scratch, operators can now create almost four.

Problem

Operators often needed configurations very similar to ones they already had, and building each from scratch meant repeating the same manual steps.

What I did

I mapped operator workflows with Jobs to be Done and an opportunity solution tree. It showed that copying an existing configuration wasn't a nice extra. It was what operators were actually trying to do, so I prioritised it and worked with engineering to ship it.

Outcome

With copy in place, configuration time dropped from 105 to 27 seconds, 74% faster. Error rates and support load fell with it, and operators work with more confidence.

Arrive (former EasyPark Group) 2022 — 2024 Case 06 / 06

Platform Migration

Retiring a legacy pricing platform without breaking a single live market.

My role
Product Manager. I led the gap analysis, prioritised the gaps, and worked with engineering on the simulation tooling and automated migration.
35+
Gaps found and closed
18
Months end-to-end, discovery to completion
0
Pricing incidents in production
35+ gaps mapped, then closed By area
  • Calculation engine12 gaps
  • Configuration UI10 gaps
  • Migration jobs7 gaps
  • Tariff types4 new
  • Simulation tools4 new

How delivery played out

Month 1Month 18 · 0 incidents

Illustrative shape: fixes landed here and there, most new features shipped at the end.

Problem

A legacy parking platform was being retired. Before migrating, every configuration model, tariff type and pricing behaviour had to be mapped to the new platform, and every gap closed without disrupting live markets.

What I did

I led a systematic gap analysis across the calculation engine, configuration UI, tariff types and operator workflows, and prioritised the 35+ gaps it surfaced. With engineering, we built simulation tools to prove pricing parity between the old and new platforms, created a new tariff type for configurations that weren't supported before, and automated the bulk migration.

Outcome

All 35+ gaps were closed over 18 months. Pricing parity was confirmed in every migrated market, automated jobs removed manual rework, and the migration finished with zero pricing incidents in production.

04Side projects

I build to learn.

Small products where I get to be PM, designer and engineer at once. It keeps my instincts sharp and my prototyping fast.

01 EquityTime balance tracker for iOS iOS · React Native
02 MyBooksPersonal reading companion iOS · React Native
03 UtendørslivOutdoor exploration companion Web · JavaScript
04 The Greenland RunFast-paced satirical browser game Web · JavaScript
05 Coin TossThrow the coin into the parking meter Web · JavaScript
05Outside work

Off the clock.

When I'm not thinking about products, I'm usually outside somewhere. The rest of the time I'm building something small that probably didn't need a computer attached to it.

Fig. 01

Running

Slow enough to think, fast enough to call it training. My better ideas tend to turn up around kilometre five, with nothing to write them down on.

Fig. 02

Mountains

Hiking anywhere with a summit and a view that makes the climb look like it was a good idea all along. Being outdoors in general, really.

Fig. 03

Two wheels

A Triumph Bonneville: classic lines, a parallel twin, and an owner who spends almost as much time looking at it as riding it.

Fig. 04

Tinkering

Tiny apps, side projects and the odd gadget. I learn best by building things, and every now and then one of them even gets finished.

In short: if it's outdoors or has a power button, I'm probably interested.