Will Product Intelligence Develop the Next Sensor Device?
Over the last decade, we have seen a shift in the types of information that fuel business decision making. There has been a transition from basing decisions and knowledge on limited qualitative and quantitative research to the development of a more comprehensive, real-time decision making process that utilizes today’s increased information flow. Company statements, online marketplaces, social media, intellectual property registries, and academic publications are just a few examples of publicly available sources of information that hold potentially game-changing value to business leaders. But how do we see this shift playing out in how actual decisions are made? How does this change the way companies develop and create new products and services? These are the questions and fields of Product Intelligence inquiry that our teams at Signals pursue every day.
How do you even begin to think about creating a new sensors device that utilizes all of the sophisticated technological advancements linking medical condition to application, but meets a mass-market consumer appetite, and addresses their real personalized health needs? What is the most efficient and effective way to identify this product and its specifications in the shortest time based on evidence?
We’re sharing a Personal Healthcare case study that demonstrates how open source intelligence, or the practice of connecting the dots between publicly available source types, enables a new path to evidence-based decision support for new product development. Let’s see how it works.
There has been a lot of hype surrounding the field of sensors. An emerging product category, the application of sensors is segmented into niche products (targeted towards athletes and early-adopters), and specific patient categories. While the potential for sensors and their integration into wearable devices is huge, they still have not yet reached mass market.
The product trend is real (as was the video phone for decades), the opportunity is there, but the right winning product has not yet been developed. So, as an exercise in testing our own thinking and tools, Signals decided to try to create a winning product in this space that maximizes this potential.
Our approach relies on understanding the current overall opportunity: an intersection between what is currently in the market, what is coming into development, cross-verified with consumer needs. First, we needed to get a comprehensive picture of the overall sensors ecosystem. We always look deeply into three domains:
1. Landscape: What are the sensors, solutions and conditions that are in the market and currently under development? What were the technological trends and what is the maturity of specific features and benefits from the supply side? We gathered evidence on university projects, companies, products, patents, trials, publications, and conferences to map the world of sensors and its applications to health conditions.
2. Competition: To identify the right product opportunity requires a strong understanding of competitive efforts. We profiled the competition in three segments to gain a clearer understanding of the types of competitors out there: i) the Big and Direct competitors – companies currently playing in the personal healthcare sensors space; ii) The Big and Indirect competitors – large companies eyeing the same target market but are not specifically focused on personal healthcare; and iii) Disruptive competitors – companies at the forefront of ideation and who are growing fast.
3. Consumer: Then we wanted to understand consumer met and unmet needs and profile them to relevant segment groups, so we listened to what consumers were discussing in medical forums for certain conditions; i.e., what do they like about their devices and what is missing?
To take an actionable next step towards developing a new product, the next phase was focused on understanding the pairing between solution capabilities and potential market applications. We created a purposeful juxtaposition between current and emerging solutions and their specific use-case applications. That exercise reveals the connection between technological capabilities and consumer needs.
We know a sensor has the ability to screen for allergens: but how does this solution connect to a consumer need?
For example, such a sensor could be incorporated into an allergy red flag: a GPS-linked crowdsourcing application that will empower people susceptible to the allergy to proactively decide on locations to go near or not to go near based on specific allergen concentrations. Whenever an individual has an allergic reaction following visiting a location with specific allergen concentrations, the sensor would act as a screening indicator for allergens, and distribute this knowledge across a shared network.
Based on this principle of linking solutions to applications, we take the next step in our process, and design a data taxonomy and ontology search tools to comb a wide range of sources first independently, and then connecting technologies to applications and usage. This is how we defined the data scope of our project. Our tools allow us to do this in weeks, not months.
Identifying and profiling each solution in the market or under-development to the type of sensor and application was the next necessary step. Here we are able to objectively look at other products in line with our specific area of sensors. We also narrowed to view important segments such as which organ is involved, parameters, application, target use, interconnection, and maturity. This is an important step of the landscaping process shortly before truly new, valuable insights and interconnections can be revealed.
Next we segmented competitors into three tiers based off of “relevancy and disruptiveness.” Using our data visualization Visual Insights software, we mapped the “Big and Direct” competitors by company, the benefit addressed (vital signs, hypertension, etc.), and the activity type (in-market product, product under-development, partnership).
All competitors (Big & Direct) then undergo more extensive analysis. We interrogate the data surrounding each competitor to understand their partnerships, signals revealing their technological capabilities and strategic R&D hires, IP holdings, etc. to answer key questions on solution focus, the time-to-market of forthcoming technologies and solutions, and understand where in this industry particular companies might be focusing on next. And, yes, much of this is publicly available.
This supply-focused assessment is cross-listed with our “Said Vs. Done” analysis, a sanity check for measuring what companies are saying they are doing via assorted types of company statements versus their actual activities. It provides a good perspective around buzz vs. reality (i.e. Google Glasses!). We further analyzed specific technologies to assess the technological specifications and pricing to evaluate what the distinctive features of each product were.
Finally, we linked the learning of the supply side to the demand side. We used work that was already performed in the organization of consumer outcome statements from internal surveys and expanded it with a social listening exercise of over 500,000 discussions in social platforms to better understand what consumers were saying about their needs. We also connected those statements to professional buzz and sentiment from Health Care providers on their view of the world on patient engagement. The questions at consideration were ‘Where is the opportunity around consumer groups?’ and ‘What are the needs that will ensure usability of each group segment?’ These questions can be answered by looking at the intelligence collected along the way and that can be solidified by analyzing consumer listening.
OUTCOME: PRODUCT DEVELOPMENT PROTOTYPE (ROADMAP)
The insights aided the innovation team in determining which direction to pursue and provided confidence because they were able to rigorously evaluate the opportunity and in turn were able to see their insights directly connected to evidence. The team was able to return to their bosses with confidence that that they had thoroughly interrogated their hypotheses, and with the evidence of real data, propose creating a credible solution. We leave you to reflect on how you can improve your decision making process by embedding intelligence as a framework to be threaded and used inside of your new product development process.
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Kobi Gershoni is Chief Research Officer and Co-founder of Signals Intelligence Group, where he oversees Signals’ research methodologies and analytics team. Kobi has conducted research for multi-national corporations and venture capital funds, and has managed the research department and overseen hundreds of Signals client engagements. Kobi has provided consultancy services to hi-tech companies and investors, and served in an IDF intelligence unit as a senior analyst and information officer. He holds a BA in International Relations and an MBA specializing in Finance and Strategy from Hebrew University in Jerusalem. Follow @SignalsGroup
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