Mental model = system model, says Intercom's Michelle Fitzpatrick.
In Summary: How do users understand new products? The answer is that people make sense of things they see by mapping them onto existing categories. When they can’t map something to what they already know, they can find it confusing.
Even asking someone to describe the process of making toast leads to a variety descriptions. If something as simple as toast causes confusion, imagine how hard it is to communicate a brand new product category like Snapchat?
When something is new you need to show and explain it to people. The easiest route is via tutorials or guides. You can also use standard design patterns as well as language and terms that are well recognised. Finally, you can use metaphors (like Apple Wallet) that piggyback off real world objects, despite the fact they have no physical properties.
By considering people's mental models and the system model of how it actually works, you can better help people understand what new products are and how they work.
Lots of gain and some pain says Greylock's Sarah Travel.
In Summary: In non-transactional products, real value is created when you create accruing benefits and mounting losses.
Sticky products use the data a user creates while engaging with the product as fuel to make the experience even more engaging (accruing benefits) and at the same time harder to leave (mounting loss).
A product has accruing benefits if a user would say 'the more I use the product, the better it gets.' On Facebook, the more friends you connect with the better your newsfeed experience. If you killed your Facebook account, imagine how much value you would lose? The combination of the 2 makes the product incredibly sticky.
This is why anonymous products like Secret fail to achieve long term traction. Without persistent identity your experience remains essentially the same.
Check your prejudice, says Martin Bottcher.
In Summary: Biases are mental shortcuts for making decisions. They save time and energy but can lead to bad decisions. Just because you know about a bias doesn’t mean that you won’t experience it yourself.
The Lindy Effect is an important rule in today's world of shiny, new things. It states that new technologies are much less likely to persist than current ones. Next time you hear about a killer, new app: relax...things won’t change as fast as has been predicted. You have more time than you think to research before moving into a market.
Availability bias describes our tendency to make judgements based only on the (limited) data available to us, groupthink describes how conformism to group norms crushes critical thinking. The Fallacy of the Single Cause arises from our innate tendency to prefer singular reasons for an event rather than a (more probable) variety of different factors acting together.
Keep asking why and accept that it's a complex world is Martin's advice.
Stuff to avoid, by VLT Labs' Serene Gan.
In Summary: A concise summary of Product Management fundamentals that are easy to understand but even easier to overlook in the chaos of feature requests and data that characterises a day in the life of most Product Managers.
First up is the importance of knowing your customers, their needs and behaviours rather than demographics. Next is avoiding trying to compete on features. It's too easy to end up down this path as requirements rain down from all sides. Features can easily be copied by your competitors so focus on what makes your product truly unique.
Lastly, there's the fine art of saying 'no' (diplomatically, of course) to avoid the pitfall of building what’s not necessary right now. Remember, the backlog only has space for one, maybe two 'priorities.'
Start with a guess, says Jock Busuttil.
In Summary: As a writer about Product Management and freelance Head of Product, Jock gets the chance to speak to lot of Product Managers about their data challenges.
In doing this he has amassed a number of critical insights about how PMs can better work with data. The first is that having some data is much better than having no data and simply guessing what to do next.
Another common issue is a disconnect between roadmap items and product goals. Either teams have no measurement of success at all, or they try to measure absolutely everything because their goals are too vague.
Usually, the trickiest part of getting started with using data is knowing what to track. It's important to look to the future, rather than react to the past, but finding decent leading indicators is tricky.
What you should measure depends on the level of maturity of your product and your business as a whole. User success, churn and 'product health' as measured by bugs & crashes are common fixtures. Josh also emphasises the importance of monitoring 'team health' in terms of morale as a key signal of long-term product success.
Stakeholder management zen from HourlyNerd's Colin Lernell.
In Summary: Writing for UserVoice, Colin thoroughly deconstructs the different audiences for a Product Roadmap and the different stages of development (from planning to post-release review). He then outlines the methods for communicating your roadmap in each situation and for each audience.
From formal presentations, to wall charts and 'information radiators', Colin covers every possible combination and configuration in depth.