Weekly Research and Commentary on the Future of Business and Technology.
radical Briefing 0025 - Sorting Strong Trends from Weak Signals.
Jul 1, 2020
Looking into the second half of 2020, we see a world transformed and still transforming. The systemic shocks of the early COVID crisis have been followed by waves of response. Challenges have been targeted with novel (if not always successful) solutions; markets have attempted to adjust to the new reality.
Amidst all the volatility and uncertainty, 2020 to date has been a fascinating time to listen for what the quantitative futurist Amy Webb described as weak signals talking at the fringe. This year, the fringe—the zone of what-might-be that exists on the periphery of the economic and cultural mainstream—crashed into the fore. The COVID crisis effectively gave some emerging trends and weak signals a dramatic boost. In a radically altered ecosystem and under new selective pressure, some previously weak signals emerged as strong trends (grocery delivery, social/gamified home workout systems, serious conversation around universal basic income, biosurveillance merging big tech and government data) and others, as defining features of an emergent landscape (digital distributed teams/workforce, telemedicine, distance learning, toxic disinformation on social media platforms, diminution of US leadership on the global stage).
Hindsight might be better than 20/20. But what about foresight? How can we survey the profusion of signals mid-year and sort them to identify those more likely to become fully-fledged disruptive trends?
At be radical, we’ve developed a simple, practical framework for more effective signal sorting. It combines our past work on tech adoption with a clever set of lenses for evaluating new product/service possibilities that we learned from radical expert and blitzscaling guru Chris Yeh.
We suggest five factors for assessment—each with a relevant guiding question.
](https://cdn.substack.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Fb0678a06-bb01-4cd5-9ae6-0f33be4f57ae_1080x608.gif)TIMING — *Is the prospective solution readily available and potentially scalable? If the tools and/or tech required are still developing, where are they on the learning curve of innovation?* Telemedicine was ready to blow up when COVID hit because the tech required had been widely available for years. On the other hand and despite the many optimistic projections, VR seems—once again—to have not been *quite* ready for the moment.
INSIGHT — What are the enabling contextual conditions that would need to change for this solution to become the right fit for the targeted problem? What else needs to be true for this signal to reach full strength? Try to identify what we like to call the “gestalt” of the tipping point, when something that has been a curiosity becomes undeniable. Think through the STEEPS dimensions: What are the Scientific and Technological breakthroughs still required? What needs to be true about the Environmental and Economic context or future impacts for this possibility to be realized? What Political and Social conditions need to change? If you’re watching weak signals in heavily regulated environments (healthcare, biotech, financial services), these questions are particularly critical.
FREQUENCY — How often do we encounter the problem? If a permanent change to a remote work policy affords my staff two hours each day that they previously lost to commuting in the SF Bay Area, you can bet that they’ll be ready for that solution to become a lasting feature the work environment.
DENSITY — How long/deep is our engagement with the problem? Sticking with remote work here: Employees transitioning to digital-distributed arrangements suddenly found themselves working much longer stretches from home. Solutions promising quick optimization of the home office (to say nothing of the Zoom shirt) were bound to spring up.
FRICTION —How much pain do we experience with the problem? If the experience of no-solution is sufficiently severe, barriers to development and adoption can prove more porous or fall away entirely. We see this in the hurried application of AI tools to COVID treatment research and the wild acceleration of data and research findings shared pre-publication in the medical and science communities. Or, to illustrate with a counterfactual: In the soft dystopia of COVID-world social distancing, video conferencing and TikTok were enough to help us fill the void left by reduced human contact; in a full-on, harder dystopian scenario…well, we might all be driven to embrace VR after all.
](https://cdn.substack.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Fc3f58f38-3548-4d8e-8dca-02c3d6601c34480x198.gif)If the timing is right or nearly so, and you can see the convergence of enabling conditions, then your weakly signaled solution has a much stronger chance of becoming a viable product or bankable trend—particularly if it addresses a problem or need that registers highly in terms of _frequency-density-friction.
Using this intuitive framework in your assessment of how present signals suggest possible futures, you’ll be better able to leverage contextual information and market data to identify real opportunities and reliable timeframes for action.
And while your foresight won’t hit 20/20, your view of what’s still to come in 2020 should be significantly clearer.
Jeffrey and the be radical team
P.S. Interested in exploring how this applies to your organization and your products & services? Find out how be radical can help you. Simply hit reply to this email, tell us a bit about yourself and the opportunity/challenge you face, and we will be in touch.
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