Reflection – Learning Launch

Submitted by: Steven Richards

Date: November 25 2018

Challenge and Selection:  My first experience of using tools related to Design Thinking was the  Learning Launch – or Experimentation.  I’ve been working with a number of global consultants from  all over the world in seeking hypothetical, disruptive innovations that World Wild Foundation might  potentially consider commercializing and scaling as part of their existing Consumer Health portfolio.   This was an entirely hypothetical example for them to consider – and may never see the light of day  – but we need to show them how the innovation process works and what it might produce.  As part  of that innovation phase – a critical part of it involved collecting external data from pharma  executives, technologists and actual consumers to validate if any of the rather zany products or  services we were creating might actually have merit in considering.  Thus a Learning Launch was  undertaken to develop a hypothesis for each of the proposed products (10 in all), and we quickly  realized that, as many of our ideas utilized the same technologies (block chain, VR, big data, nano- technology, wearables, etc.) we might be able to consolidate 10 product Learning Launches and  reduce that number to about 4, as we had very limited time and needed to move fast.  Learning  Launch provided us the critical platform to take our rather wild ideas and see if there were any  issues, worries, challenges or out-right fails associated with chasing these further down the  innovation path.

Application:  We started by looking at the core or ‘crux’ hypotheses we needed to test with external  sources – and identified 4 key hypotheses to address.  In each of these, we need then to determine  what were the most effective means of gathering the most illustrative data that would either  validate our concepts – or prove them to be ineffective, too expansive, technologically impossible or  otherwise of little concern to the consumer and/or problematic with our client company.  We  ultimately landed on using on-line surveys using Survey Monkey, where we sought demographic  data to address ethnographics, then dug into the challenges/problems the respondents experienced,  and further drilled down (based on answers to key questions), as to what their experience was with  each of these elements in terms of available relief or cure.  This was used to qualify respondents to  be able to participate in the next phase of questioning.   We then introduced a prototype concept to  them, simply explaining the basics and what use cases we planned to implement. This then framed  up the follow on questions that queried whether or not they would feel comfortable engaging with  the technologies we proposed, the techniques and methods involved, and whether or not they  would consider using these concepts were they readily available tomorrow.  We were seeking, as Heiko Meier described in week 4’s Learning Launch presentation, the truth about whether or not our ideas  were even worth considering – or would the fall at the first hurdle.  We further sought open-ended  responses as to why they might use or not use our disruptive innovations – and then poured through  the results in earnest.  We launched these extensive surveys via various social media tools to gain  access to as large a sample size as we could – to maintain a level of statistical significance.

We then divided the responses into appropriate groups covering each of the disruptive innovations  we were seeking insight about – then into ethnographic categories, to see if patterns would emerge  on that demographic breakout.

In some cases – where responses appeared to lead towards deeper, more insightful learning – we  reached out to individuals for one-to-one interviews (with their permission, of course), and dug  further into the details of why they responded the way they did, and why they would either choose  to engage or pass on the concepts we created.  We were deeply aware of confirmation bias, and  seeking to really understand why the sceptics were feeling the way in which they did.

We then met as a team with a specific focus – which was only to validate or invalidate the concepts  – based solely on the data we received back, and sought to ignore any personal attachment to the  concepts we’d created initially.

Insight and Approach:  The insights we gained by reviewing the data from the surveys and interviews  was rather insightful – and challenged all of us to reconsider several aspects of our disruptive  innovations.  We found the people had very specific notions and pre-conceptions about the pharma  industry, misuse of personal data, the use of wearable data gathering devices and multiple  perspectives (POVs) represented from each of the constituent groups (internal pharma execs,  consumers, technologists).  What we quickly realized was that all parties felt that the innovation  activity that led to our particular concepts were laudable and worth exploring ultimately as ideas for  the future.  But in most cases – only the least invasive concepts were ultimately rewarded with an  appropriate level of validation – mostly in the realm of using wearable devices such as smart  watches or using VR tools (but not implants, smart tattoos or nanotech ingestible technology).   Other ideas that involved genomics, open source data-base queries using their personal data (even  anonymously using block chain management) and creating a highly personalized ‘Health Cockpit’  were met with high levels of scepticism – based on the multitude of data breaches, misuse by profit  driven insurance companies, and poor data management in previous efforts to create data oceans  by the likes of Google working with the National Health Service in the UK – as well as the typical  commercial selling of personal data to marketing companies.  Invasive technologies were also met  with a level of concern – mostly around the unknown effects these devices might have on the  human body – not to mention what is done with the data they create and who owns it ultimately.

This was massively mind-expanding and brought to bear many aspects and knock-on effects of  intense media coverage, negative perceptions of capitalistic organizations, misuse of personal  information, the seemingly tone-deaf and uncaring approach that major technology companies have  architected their early experiments – as well as how they’ve dealt with security breaches –  ultimately leading to an overall mistrust of new technologies potentially producing the same  negative outcomes for our respondents.  Had we simply went along with our own biases, where we  felt new technologies offered powerful diagnostic insights, leading to new treatment regimes, new  drug creation or use of alternative means to reduce anxiety, ease pain and suffering and reduce  dependence on drugs – we might have imagined humanity would receive us with open arms and  consume what we created with great enthusiasm.  We would have used traditional business  modelling, creating presumptive market penetration and use acceptance data – likely using  previously existing research that was confirming of our innovative use of technology – and never  would have seen the brick wall sitting in front of us as we sped along that the road ahead was wide

open.  This exercise in seeking ‘the truth’ and removing confirmation bias challenges, while then  focusing only on whether or not the raw data received either validated or invalidated our original  hypotheses save us time, money, and wasted innovation and manufacturing cycles, not to mention  failed marketing and sales activity and reputational damage we could have imposed on an unwitting  client who otherwise might have been completely sold on our ideas.

