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Mark Ryan

Co-Founder @ trypavlov.com

Mark Ryan is the founder of trypavlov.com and has a strong background in product management at companies like Shopify, Intercom, and Blooie.

He studied Bachelor of Science with a focus on Biology, Physics, and Maths at Maynooth University.

Mark Ryan has previously served as Co-Founder at Pavlov, Member Board Of Trustees at International Centre for Life, Director of Product, Senior Product Lead at Shopify, Senior Product Manager at Intercom, and Director and CEO at Blooie.

Highlights

Jun 17 · linkedin

You need very little usage data to make great product decisions.

Moreover, if you’ve gotten to a state of ‘comfortable’ with the data you’re basing a call on, you’ve likely waited too long. Beware of “we need more data to make the call”.

Context: Last week, a startup I advise asked me how much user data they need to be confident in a decision they’re making. My answer was (and always is): “Often very little, sometimes only enough to get directional signals, very rarely (if ever) enough to get stat sig”

This isn't because usage data isn’t important, but, because the types of decisions you usually make when building product often don’t need it to move forward. To explain what I mean there are two important concepts to frame upfront

One-way doors vs. Two-way doors:

  • One-way doors are difficult-to-reverse, high-impact decisions that can require thorough analysis.
  • Two-way doors are easily reversible, low-impact decisions that usually allow for experimentation.

Leading vs Lagging data:

  • Leading data can give foresight and help predict future trends or outcomes.
  • Lagging data is used to understand the impact of decisions already made

Below is the image I used to explain my point to the founder. It frames the reality of product decisions and touches on the concepts above.

Most product decisions fall into the top right quadrant – two-way doors that require little leading usage data.  And honestly, most one-way doors don’t need a lot of leading usage data either. These decisions are usually platform choices or strategy decisions based on external market dynamics (market data here is VERY useful).

The aim of leading data is mostly as a guide. You need just enough to point you towards good options and rule out bad paths.

The best data teams I know work like this. Their value lies in looking at the whole chessboard, guiding strategy, allocating their limited time to areas with the highest impact, and relying on qualitative customer feedback and minimal quantitative data for the rest.

The above is about product decisions but the same is true for any team. Pavlov helps GTM teams get to great decisions quickly and then gives them the tools to help drive customer behaviour. If you want to learn more, reach out.

PS: Excuse my shitty drawing.I couldn’t make chatGPT clean it up AND keep the clarity

Mark Ryan
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Location

United Kingdom