Behavioural science and how to develop tech products people really want

Developing tech products that people really want is hard. Even the biggest companies have major fails. Remember Microsoft Zune in 2001, Google Lively in 2008 or the Windows Phone in 2010. Probably not. They are among the 40–50% of failures of new products we see every year – little better than chance.

By definition, most tech products are new. We can never really be sure how people will react to them. There is a popular view that it is best to leave the product development to the imagination of the engineers and designers because consumers don’t know what they want and can’t tell you how they will use something they have never seen before. We can do the concept tests and make prototypes and people will still ask ‘why do I need this?’ or complain that ‘it’s just too hard to use’.
These types of responses should set alarm bells ringing but many companies go ahead and launch the product anyway, convinced that their idea will catch on eventually. And sometimes they are right: people didn’t immediately catch on to fitness trackers but now around 30% of people under 30 years of age own one.

But we can do better than this and behavioural science is one of the ways we can improve the odds that our new product will be a success. Behavioural science can give companies confidence in their product development and innovation. This is because behavioural scientists use theories and validated models of behaviour to understand the factors, benefits and attributes that people respond to and that really drive behaviour and decisions in the real world.

These types of models are particularly useful when dealing with ‘new to the world’ products. At Innovia we use them when designing cutting edge tech products because we have found them to be more predictive of behaviour than just asking customers what they want. It means that early on in the process we can build in features that are predicted to drive uptake and then conduct experiments to test whether our hypotheses are correct.

Schematic of Acceptance of Technology Model

The model has gone through several iterations but the central concepts have been proven to be highly predictive. The main ideas are that the best predictor of actual usage of new technology is intention to use and that two fundamental factors influence the intention – how easy it seems to be to use and how useful the new technology will be. Whether or not people think the technology is
useful or easy to use depends to some extent on their previous experience.
Now this seems obvious doesn’t it? But these basic questions are often overlooked. Are you sure at the design stage that you are making the products easy and intuitive to use? Have you asked people whether it is really solving a problem they care about? And if at concept testing stage you find that your potential customers still find it hard to engage with or haven’t understood the benefits in terms of usefulness can you change either the product or the strategy?

Behavioural scientists have a plethora of models and theories to draw on. These mental models of how people make decisions are invaluable at the product development stage because we can make hypotheses about what people want and how they might behave and test them before we have to go to full prototype.
In this way, behavioural science can focus and shorten the development process and give you more confidence that your new tech baby will not join the long list of failures we see every year.

Dr Helena Rubinstein is Head of Behavioural Science at Innova Technology, and the author of new book, Applying Behavioural Science to the Private Sector (Palgrave)

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