Founder Biases #5: Tech products don’t change how people behave. Initially.

If there’s one myth that still persists in the tech industry, it’s the way some founders believe that a digital product can upend human behaviour and introduce a new way of doing things. The truth of the matter is you need a very specific set of circumstances to shift personal, organisational, even societal habits through technology.

When we say that 4 in 10 startups fail because there’s no market need, most of us create the simplest scenario in our heads: the product was built, launched, and then there weren’t enough people using it to drive further investment into the product. Other than the founders actually doing the work, very few decide to dig deeper into the ‘why’.

Why isn’t there any need for a specific digital product?

Some startups fail because the problem they’re trying to solve isn’t in fact a technology problem. Take for example HomeHero, a US healthtech marketplace for families looking for specialised in-home carers. A technology-powered matching platform, their services were faster than how local agencies worked recruiting based on word-of-mouth and matching based on family visits. Yet they still couldn’t turn a profit when you mixed in newer and stricter regulatory updates. Incumbents weren’t using technology the way startups in the space were doing it, but the industry itself didn’t need digitalisation to innovate, but legislation and institutional policy changes to manage the high costs of the service.

Other startups fail because the solution they offer is a vitamin-type product, a non-essential nice-to-have, and, despite the backing of VCs or celebrities, that reality doesn’t change. Startups like Quibi or Juicero come to mind. These types of products ask for time, effort, money, or a combination of these from users, giving back little value to the users compared to the existing enjoyment they could get out of their existing passions or products, like Netflix or a straightforward blender.

Products that succeed are built on people’s existing behaviours

If products that ignore the industry specifics and the users’ circumstances fail, underlining the need for doing product validation, what do products that succeed do right? To put it simply, they integrate well into the jobs people are trying to get done and make them easier. Let’s take a look at a couple of examples. 

Fitness and wellness apps

Fitness and wellness apps, like Freeletics, Fabulous, or Headspace, help people adopt healthier habits, whether it’s working out, eating better, or maintaining a better schedule. These apps do two things well:

1. They break down introducing the new habit into small, accessible actions you can do daily, reducing the friction of doing them as much as possible
2. They each introduce social support in the product as a means for users to have the support of their social network as they work to build new habits. This can be in the form of having a friend hold the user accountable to follow through, or offer encouragement as tasks are completed.

Remove the digital product from the equation, and you’ll realise that the techniques of building healthy behaviours are being used by health professionals and fitness trainers even outside the digital setting. Because the way people learn, and the way people need the support of their peers to change their lifestyle has been widely researched and hasn’t changed much.

Community-based apps

There’s been a shift from open, large communities towards more niched, tightly-knit communities. In an Andreessen Horowitz analysis, reading focused social apps like Goodreads and Wattpad are the stickiest apps, while gaming and media focused social apps like Discord and Twitch are generating as many visits as messaging apps like Whatsapp, Facebook Messenger, or Telegram.

To understand why niched communities have better engagement, drive better conversions, and have longer retention, we’ve got to step out of the tech space for a moment and look at how communities work. If you’ve ever dabbled with being a gamer or a fan of something, volunteered for something, been in an action group for a cause, or have been part of a hobby group like cyclists, you’ll know what I mean in a minute as I explain what media studies calls participatory culture.

Participative cultures keep groups working when a couple of things are happening at the same time:

• people are there for a common interest
• the older members informally mentor the newer members in creative endeavours, organising, or the interest itself
• there’s a low barrier of entry for making things or organising activities around that interest, and there’s constructive feedback and validation going around for everything
• the group builds a common language of learning, jokes, and signals of belongings (e.g. memes, GIFs, art, online avatars, or even merchandise)
• the good vibes motivate people to keep coming back
• If things get serious (i.e. a stronger community is formed within the larger group), the community self-moderates and creates a code of conduct

Community-based apps, products that try to build a social component into their ecosystem, even online communities and web3 projects succeed in this space because they tap into the way people have always positively organised into groups.

Interactive databases

The bigger an organisation, the more specialised data it produces. Yet if teams have different specialisations and goals, they’ll need data in different formats to get their projects done.

This used to mean different databases for different departments that needed to be kept updated across the teams on a regular basis. More often than not, spreadsheets were kept in various formats and versions.

Tools like Airtable allow users to build interactive databases. They build upon the user and organisational behaviour that data exists to be viewed, changed, handed off, and only as a last thought, stored. The database becomes an interactive interface through which users can build their own ways of viewing and work with the data, without affecting the underlying way of storing it.

Media editing

Ask any writer about their process, and they’ll tell you it’s some form of putting words on paper, reorganising them, cutting them out, and adding new bits.

Move into the space of audio or video of content creation, and suddenly the process becomes much more complicated, between recording, listening or watching to edit, listening some more, adding the finishing touches of a soundtrack and visual effects. Only then can you finally say you’re done.

In fact, if you’re not in the business of doing movies, editing media was until recently a very cumbersome process. That’s why products like Descript, powered by machine learning, can channel the familiar workflow of content creation from writing into media and be successful. The mental model of how to do creative work was already there, what was missing was the technology that made it possible.

Takeaways

We’ve seen examples of startups that failed because they tried to introduce behaviours that nobody actually adopted because there were no mental models for them. Other products failed because they tried to fix with technology problems that weren’t in fact fixable with technology. Both underline how important it is to look at the market with a wider lens than that of ‘can I fix this with my own skills and resources?’. Often, that means doing the work needed to properly understand how the people in your audience actually live their lives regularly. Such hands-on understanding is essential for B2C products and only a sheer dose of luck can replace it. Other times, it means getting out of the comfort zone of doing your own research and asking experts from other fields for advice, to incorporate that into your own work, as a starting point that gives you the confidence to tackle a new problem others experience.

Founders are extraordinary people because they look around them and see problems as pure opportunities. But they can significantly increase the chances of solving those problems when they understand that the best product they can build works with existing mental models, expectations, and behaviours. They’ll iterate on getting their product to fit into what’s already in their users’ mental space until they get those people engaged with their solution. And only after that moment will they be ready to scale.