Inside Wearables - Part 3

The rocky path towards personalized, insightful wearables

January 2016

Dan Ledger, Principal, Endeavour Partners

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Foreword In 2015, we experienced an explosion in the variety of wearable devices entering the market. Well-established categories like activity trackers and smartwatches were legitimized with Fitbit’s IPO and Apple’s eventual entrance into this space with the Apple Watch. We saw several glimpses of how augmented and virtual reality are poised to change the way we experience the world, share experiences and entertain ourselves. We saw several devices that interact with our brains to improve mindfulness, prevent migraines, and increase alertness and relaxation. We saw devices that aim to measure high-level physiological states like stress, and caloric intake. And this is just the beginning of the list.

Connected “smart” wearable devices, such as activity trackers and smartwatches, began to really emerge about 5 years ago. And on Christmas Day in 2015, the Fitbit app, which people must install to setup a new Fitbit activity tracker, was the top downloaded app in the Apple App Store. Smart wearable devices have moved from a niche product just a few years ago to a mass-market product category in just a few years.

It hasn’t been all smooth sailing. As Endeavour Partners research has shown, many consumers who adopted smart wearable devices, such as activity trackers and smartwatches, have subsequently abandoned them at higher rates than other mainstream consumer products. Wearables OEMs have faced a wide range of challenges around building robust, compelling offerings that keep people wanting to wear their device each and every day. However, things have improved significantly in the last few years. In addition to solving an increasingly diverse and interesting set of problems, wearable devices are becoming more invisible, more lifestyle compatible with longer battery lives and more comfortable materials, and easier to own and wear.

We are still in the very early days of wearable technology. The next 5-10 years are going to be uniquely interesting and exciting for several reasons which we will discuss in this paper. What is most compelling perhaps is that it is becoming increasingly clear that wearable devices are not yet another category of consumer gadgetry but rather a transformational class of products that promises to help people live happier, healthier and more productive lives.

There are several companies in this industry today that are working towards a future that will give us hyper-personalized, intelligent and useful wearable experiences—essentially, making the “smart” wearables we have today much smarter. Many in this industry share compelling visions of devices that help us manage our stress, optimize our health, establish better habits, improve our fitness, prevent chronic diseases, reduce chronic pain, improve our focus and memory, and much more. A big part of this vision stems from learning how to use data that we’re gathering from the increasingly diverse collection of sensor and data sources in more intelligent ways to both unlock valuable and highly personalized insights about what makes us actually tick, and optimize aspects of our minds and bodies.

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While this vision is incredibly compelling, there is one particularly challenging question the industry is wrestling with: how do we transform all of the data we’re increasingly able to gather into meaningful, intelligent insights that will be needed to deliver the products and user experiences that the industry is striving towards?

For many, this may seem like a simple question. After all, we have been increasingly taught that machine learning is a panacea for problems like this. Companies like Netflix, Amazon and Google have become highly adept at interpreting our patterns of interactions with their services to make intelligent predictions about what we might want next. Can’t wearable offerings apply those same types of algorithms to uncover more insights about our physiology like stress, fitness, and health in general?

The short answer is that we’re dealing with an interesting set of constraints that makes the problem trickier to solve. However, the rewards for solving these challenges are immense and the implications are huge. This white paper, the third in Endeavour Partners’ Inside Wearables Series, will specifically explore the nature of these new challenges, and discuss the new opportunities that are emerging for several different types of players in the broader wearables ecosystem.

This white paper is organized in four parts:

Part 1 explores an exciting and unexpected future for wearable technology. In this section, we look at several factors that are shaping this future, including unique patterns of innovation that will lead to unanticipated, and exciting new offerings both in the short and long term.

Part 2 examines why the wearables industry is stuck, and presently struggling to make meaningful forward progress on the user value proposition. In particular, part 2 explores the serious challenges we face transforming all of the data we can now gather from wearable devices into valuable, accurate, helpful, and safe insights for users. As we learn to address these data challenges, we’ll begin to unlock far more compelling use cases and experiences.

Part 3 explores how these challenges may be addressed, and specifically discusses the implications and opportunities for different types players in the ecosystem including OEMs, service providers, healthcare providers, payers, employers and semiconductor suppliers.

Part 4 describes a few case studies of projects in which Endeavour Partners has helped companies in the broader wearables ecosystem with related challenges.

We hope you enjoy it and find it useful. If you have comments, questions or would like to discuss the evolution of the wearables space, please email the author, Dan Ledger at dan@endeavourpartners.net.

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Part 1: Why the future of wearable technology is more exciting and unexpected than we previously thought Unlike many other categories of consumer electronics that have marched along year after year on a predictable evolutionary path, wearable technology is already beginning to evolve in exciting and unexpected ways, and holds the promise to deliver compelling new benefits both at an individual level as well as at a societal level. There are three important factors that are contributing to this exciting and unexpected future.

Factor #1: The pattern of innovation in wearable technology will be increasingly unexpected and difficult to anticipate

Let’s start with a counter-example. Over the years, we’ve gone from owning several well-understood, single-function consumer devices—digital cameras, MP3 players, video cameras, radios—to owning a smartphone that has absorbed all of this functionality. This evolution was not entirely unanticipated. In fact, many consumers, particularly those who had an interest in technology, could anticipate this slow and eventual convergence because it has been centered around well-understood, existing product concepts collapsing into a single device.

Wearable technology, on the other hand, is becoming increasingly difficult to predict and anticipate. This is due to the fact that so much of the innovation in wearable technology is coming from the interface between technology and human physiology and biology. This an area that, A, very few people really understand well and B, is evolving in several interesting directions. It’s not necessarily obvious to most what is and what is not in the realm of possibilities as we look towards the future.

For example, NeuroMetrix recently launched a chronic pain relief wearable called Quell that is worn on the calf. Quell stimulates a nerve in the leg, which in turn triggers a natural pain-blocking response in the brain that blocks pain signals throughout the body. Quell is an incredibly promising product as it has the potential to rep lace drug-based approaches to pa in management which have several adverse long-term side effects such as addiction.

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The Quell leg band for chronic pain relief by NeuroMetrix

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Another example is, GoBe, a promising (albeit untested at scale) new wearable device that measures caloric intake (amongst several other things). When we ingest carbohydrates, our body converts these to glucose. As our cells absorb glucose, they expel water. As the concentration of water increases, the impedance of our skin changes. GoBe works by measuring these water balance changes that occur within the body and translating these measurements into caloric intake. GoBe is particularly promising because up until now, the approaches for tracking caloric intake have been rudimentary in comparison. These have involved manual logging of food, or taking photos of each meal that a service analyzes (either manually or algorithmically), and estimates the caloric content.

Both of these are examples of novel and non-obvious approaches to interesting and important problems. There are several other companies exploiting other biological and physiological mechanisms to create products that have the potential to be impactful. Below is a list of such companies that are developing and/or have launched novel product concepts.

These companies are at various stages of maturity and validation, and for reasons discussed in the next section of this paper, some are destined to fail because their product won’t work reliably at scale, amongst other reasons. However, as the underlying sensor technology improves, in terms of accuracy, capabilities, cost and power consumption, we can expect to see more surprising and interesting use cases that exploit similarly interesting biological and physiological mechanisms.

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Factor #2: Wearable technology is beginning to emerge as a powerful tool to transform chronic disease prevention, and there are huge financial incentives at stake

In the US, we spend upwards of $3T per year on healthcare, and presently 86% of this goes towards the treatment of chronic diseases. Many of these conditions are more expensive to treat than to prevent. For example, The annual cost of care for someone with diabetes is $10,000 higher than that of someone without the disease. The average lifetime cost of treatment for diabetes is around $85,000 per patient. Furthermore, the costs of treating chronic diseases are rising.

There are huge financial incentives for companies that can create preventative solutions that can either delay the onset of chronic conditions by helping people establish healthier habits for example, or reduce the costs of managing these chronic conditions.

Wearable devices, such as activity trackers that motivate people to walk more, are proving to be a promising new preventative tool for chronic diseases. A growing body of evidence suggests that getting people to walk more can be an effective tool for delaying the onset of chronic diseases, or reducing the cost of managing them. Partners Healthcare, a major healthcare provider, recently performed a fascinating study on diabetic patients. One group was incentivized to walk more, the other was not. Individuals who were part of the walking group on average logged 1,400 more steps per day, and this group experienced a 1% drop in hemoglobin A1c, a proxy for how well diabetes is being controlled. A (seemingly insignificant) 1% decrease reduces the risk of heart attack by 14% and diabetes related deaths by 21%. 1

Another important component of activity tracking is that it may lead to other healthy habits. Charles Duhigg explores the concept of “keystone habits” in his book, The Power of Habit. Wholesale behavior change doesn’t usually work (as evidenced by the number of people who abandon New Year’s resolutions); however, when people establish one simple healthy habit (like walking more), this often leads to other healthy habits.

There is a growing body of anecdotal evidence that suggests that some people who use activity trackers pick up other healthy habits like eating better, exercising regularly, and joining gyms. In Endeavour Partner’s 2016 Wearables Survey, we found that several owners of both activity trackers and smartwatches (a majority of which are Apple Watches) reported that these devices have helped them establish other healthy habits, as shown in the chart on the following page.

Kvedar, Joseph - The Internet of Health Things, 20151

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Individuals who were part of the walking group on average logged 1,400 more steps per day, and this group experienced a 1% drop in hemoglobin A1c

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Finally, the users of these products are increasingly not necessarily the ones purchasing the devices. Several Fortune 500 employers are providing employees with activity trackers as part of corporate wellness programs. For example, Fitbit announced in 2015 that Target would be equipping 335,000 of its employees with Fitbit activity trackers.

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Factor #3: Leveraging neighboring technologies enables innovation at the architectural level, which can radically improve the performance of product or technology

Sometimes the most interesting forms of innovation come from a reimagination of how the pieces of a system might fit together (rather than continuing to optimize the components within an existing architecture). This form of innovation is known as architectural innovation. Innovation at the architectural level can lead to impressive improvements over the more traditional incremental innovation (in which components are optimized within an existing system architecture).

For example, years ago, cell phones supported speech recognition, but it was very limited. Due to the small processors that were used in cell phones, they often had a limited dictionary of spoken words that they could understand. When 3G networks emerged, Wi-Fi became more ubiquitous, and cloud computing became economical, companies began to realize that they could capture a voice command on a cell phone (a smartphone in this case), and quickly send the recorded speech to the cloud for interpretation via a relatively high speed 3G or WiFi network. And once in the cloud, these companies could deploy a great deal more processing power to interpret the voice command. The result was a massive increase in the accuracy of speech recognition. Apple and Google were amongst the first to employ this alternative speech processing architecture (with Siri and Google Voice Search, respectively), and both achieved significant improvements in voice recognition accuracy over existing approaches. By reimagining how the pieces of the system could fit together, these companies essentially created a new architecture that vastly outperformed the existing one.

The same patterns of architectural innovation are also taking place in the broader wearables space. There is a wide array of important, rapidly evolving neighboring technological assets (shown in the table below) that companies are leveraging into creative new architectures that may be far more efficient at solving existing problems.

• Compute and store capabilities in infrastructure (aka “the cloud”)

• Explosion in the volume and variety of open data sets

• Explosion of open APIs, open platforms, and exposed capabilities

• AI, machine learning, and predictive analytics

• Ubiquity of 4G/LTE coverage

• Ubiquity of smartphones and mobile devices

• Emerging low-power cellular networks (e.g. Sigfox)

• Genomic and biomic sequencing

• Ubiquity of social networks

• Improvements in bio-sensing and imaging technologies

• Advancements in component technology (size, power, cost)

• Shifts in healthcare, employee wellness, digital health funding, etc.

• Advancements in material science

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By exploring how these assets may enable new approaches and new architectures, companies within this broader business ecosystem will be able to similarly unlock meaningful improvements in system performance and find compelling new approaches to difficult challenges. Furthermore, because the pace of progress is so high in these neighboring domains, a solution may be emerging today that wouldn’t have been possible even a year or two ago.

We are seeing some examples of architectural innovation taking place in the wearables space already. For example, for the last several years, most wearable devices have connected via Bluetooth to a smartphone. That has been the dominant architecture thus far because historically, it hasn’t really been feasible to add cellular connectivity to a wearable device due to the size, power and cost constraints of cellular modems. However, over the last few years, we have seen a few things quietly happen.

The first is the emergence of significantly smaller, and lower-power cellular modems from companies like Qualcomm. Smartwatches with cellular modems now have comparable (1-2 day) battery life to smartwatches with just Bluetooth. These cellular connected devices are now becoming available in appealing form factors, such as the LG Watch Urbane 2nd edition.

Second, we have seen the emergence of alternative low-power cellular networks like Sigfox. These networks rely on a completely different network technology than current cellular networks from AT&T, Verizon and other carriers. These low power cellular networks offer low-power and low-cost cellular connections that work very well for certain types of wearable devices.

This specific set of developments has enabled a new architecture in which wearables devices can begin to maintain a continuous connection to the cloud, even when a user is without a smartphone, or more importantly, for people who do not own smartphones. And this is the tip of the iceberg.

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Part 2: Our biggest challenge is creating meaning from all of the data in a safe and reliable manner It’s exciting to consider the role that wearable technology may play in helping us lead healthier, happier, and more productive lives. Today, we are experiencing the very beginnings of what will become possible over the coming decade. The future use cases for wearable technology are going to be incredibly varied and powerful. And these future use cases will increasingly be driven by smart experiences that are far more intelligent than what we experience today.

As we’ve learned over the last five years, designing, manufacturing, and selling robust, appealing, and useful wearable devices is hard. As we explored in our first two wearables white papers, there are several things that OEMs must get right, and a failure in any one area can lead to a product failure or, at a minimum, accelerated abandonment rates. These are summarized in the table below.

• Selectability

• Aesthetics & invisibility

• Out of box & setup experience

• Fit and comfort

• Quality & robustness

• User experience

• API and integratability

• Lifestyle compatibility

• Overall utility

The good news is that the industry has come a long way over the last few years. We are building activity trackers, smartwatches and other wearable devices today that work better, look better, are more robust, that people wear for longer periods of time.

We’re also seeing a lot of new categories of devices, as described earlier, that are able to piggy-back on many of the industry’s learnings thus far.

The bad news is that this industry has reached a subtle but important plateau. Despite our ability to gather more physiological data than we have been able to in the past with the wide range of new sensors we’re seeing on devices (heart rate, galvanic skin response, temperature, etc.), we haven’t yet figured out how to robustly translate this data into meaningful insights for users, outside of very narrow use cases. While to some this may seem like a simple data science problem, it is actually a much deeper issue, and it is potentially the most important issue standing between us and the future experiences we’re aiming to realize.

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Getting closer to meaningful insights from data

Imagine that a new gauge suddenly appeared on your car’s dashboard called “engine oil viscosity” and someone told you that oil viscosity influences the proper long-term functioning of your car’s engine. You would glance at this gauge regularly but wouldn’t necessarily know how to interpret the reading. You may notice that a combination of factors such as ambient temperature, engine temperature, and the age of the engine oil influence this reading. Or, it may appear to fluctuate randomly from day to day. You would very likely find yourself wondering why it was high one day, and if you should be worried. This is similar to what can happen when the average person is presented with continuous, raw physiological measurements like heart rate.

Today, the industry is more or less stuck presenting physiological measurements to users without much else. Most devices provide raw data, such as heart rate, with some broad explanations of what types of things impact this parameter. In some cases, devices may attempt to make correlations using the relatively limited amount of information they gather. For example, my Jawbone UP4 will tell me that my heart rate average for the day was 2 beats per minute lower than the day before. But what does that really mean? How can I use this information to make better choices about my health or fitness level?

The industry is aspiring to provide us with more intelligent wearable devices that can better describe our current physiological states, predict future states, and prescribe activities personalized to us that can make us feel better, happier, etc. And despite the addition of more sensing capabilities on these devices, we haven’t really moved beyond the ability to play back raw, descriptive data to users.

The figure below presents a hierarchy of intelligent operations that may be performed using the raw data. As an industry, we are struggling to move past the first level, in which we simply play back the data with some generalized commentary, and the second level, where we correlate two sets of data to produce basic observations.

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Why is this hard?

With the advancements we’re making in artificial intelligence, it’s hard to believe that we aren’t on the verge of building a wearable device with the right sensing capabilities and algorithms that can give people detailed information about their health, helping them make better decisions, and even begin predicting future physiological states or episodes based on previously observed patterns. After all, this isn’t too far from the other intelligent experiences that machine learning is enabling in other types of consumer experiences like shopping and watching TV.

With wearable technology, though, it isn’t simply a matter of developing better machine learning algorithms to make sense of the data. When it comes to interpreting measurements from the human body, we are dealing with a unique set of challenges.

Challenge #1: The cost of being wrong is much higher

If the intelligent algorithms that power the Netflix recommendation engine provide you with a bad movie recommendation, you’ll likely ignore it and move on. If an intelligent algorithm associated with a wearable device provides a person with descriptive or prescriptive information about their health that is incorrect, that individual may take steps that have a negative and potentially dangerous impact on their health.

Providing even basic interpretations of biological parameters has several safety implications. This is particularly true as we move further towards predictive and prescriptive guidance. Thus if a system is reading bio-parameters and advising the user to take certain steps, it’s very important that these steps do not have the potential to harm the user’s health.

Furthermore, any intelligent system that is providing insights must carefully manage the user’s belief and trust in the system. If an intelligent system begins producing observations or recommendations that are obviously wrong, users lose confidence. Remarkably, it doesn’t take many failures before people will stop engaging with a system following such errors. Therefore, it is incredibly important that, at least initially, reliability is prioritized over breadth of features and capabilities.

Challenge #2: Building intelligent, insightful wearable devices that work safely and reliably at scale is incredibly hard

As discussed earlier, a lot of exciting new product concepts have emerged over the last few years that promise to provide deeper insights about our complex biology and physiology, from detecting stress and various emotions to understanding the caloric content of our meals, all from the surface of our bodies.

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it is incredibly important that, at least initially, reliability is prioritized over breadth of features and capabilities

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There is a very critical set of challenges that companies developing these products face as they attempt to make the transition from validating their approach in a lab environment with tens of subjects to launching a commercial product that works reliably for millions of users.

The human body is an incredibly complex system and we are only beginning to understand how this system works. While most human bodies are anatomically and functionally similar at a high level, we are each unique systems that behave slightly differently. The way my body manifests stress may be distinctly different from someone else’s, for example.

To understand what is happening within our body, we often need to understand context. For example, increased skin conductance could indicate stress, but it can also indicate sexual arousal, excitement, fear, or a number of other high arousal states depending on the context of the situation. In other words, stress may cause an increase in skin conductance, but an increase in skin conductance doesn’t necessarily mean we’re experiencing stress.

This has huge implications on how we interpret a physiological stress response. Any intelligent algorithm we develop in an attempt to accurately diagnose a mental or emotional state must first be able to deal with the diversity of human physiology (particularly important as products get rolled out to millions of users). And furthermore, these algorithms need pieces of our contextual picture to determine the nature of the response.

The biosensors that we use today are non-invasive. We are attempting to understand what is happening inside the body based on what can be reliably detected from the outside. This is akin to walking down the hallway of a movie theater and hearing the bass rumbling through a door to a theater. Is that an action film or a love story? Similarly, our body may present a similar physiological response at our skin’s surface when we are experience positive arousal (laughing at a joke) as to when we are experiencing negative arousal (on a stressful phone call).

What happens if we launch a product and the intelligence only works reliably for 75% of the people that use it? And what happens for the 25% of people? Many wearables companies are finding it better to be safe than sorry, and despite the addition of new and interesting sensing capabilities, continue to provide a very basic level of intelligence and insight that have a high probability of being accurate and reliable across a vast majority of their users.

Challenge #3: Building insights from data correlations is risky

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Stress may cause an increase in skin conductance, but an increase in skin conductance doesn’t necessarily mean we’re experiencing stress.

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If a wearable device identifies an interesting correlation in my data, I am likely to interpret this as a causal mechanism. For example, if my wearable device told me that I tend to sleep better on days that I jog, I may then believe that jogging causes better sleep. But what if this isn’t the case? What if I have more free time on certain days of the week to run, and am generally less stressed on those days and thus sleep better.

The problem stems from the fact that wearable devices have access to a very narrow slice of our lives and are missing a set of broader, contextual variables that are needed to discern the real causal mechanisms that affect our happiness, stress, energy levels, etc., (many of which we’re just beginning to understand).

Thus before we can begin to build meaningful intelligence into our wearable offerings that works safely and reliably at scale, we need to be sure that these offerings are capable of leveraging well understood existing causal mechanisms (likely in narrow use cases), or are able to synthetically identify new causal mechanisms (in broader use cases) in a reliable manner.

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Part 3: Implications and opportunities The challenges ahead are non-trivial. But there is a significant opportunity for those in the ecosystem to be part of the solution to these problems. The implications of addressing these issues will be incredibly powerful both at an individual level, and at a societal level.

There are at least two fundamental components that we’re broadly missing that will be required to address the challenges presented in part 2:

• More contextual data that can help us better understand and interpret the data we’re gathering from the increasing number of sensors on wearable devices.

• More robust computational biological models that are adaptive enough to reliably handle variances in human physiology such that these models are robust at scale.

Depending on the types of products we’re looking to build, a lot of pieces of missing contextual data may already exist within our social networks, location history, calendars, health records, etc. However, as we dig deeper into the types of data that we need to build more robust insights, we can expect to see creative new approaches emerge to fill in the missing pieces of this data puzzle. This is particularly true when we begin to consider new architectural approaches—thinking about new ways to arrange the pieces of a system to create a better performing solutions.

There has been a lot of interesting research and some commercialization of computational biological models, which are essentially simulation models for aspects of human physiology. For example, platforms like LifeQ and Firstbeat use heart rate and heart rate variability readings to estimate factors like fitness level and readiness to train. Google’s efforts with the Baseline Study, in which Google plans to collect large amounts genetic and molecular data from thousands of willing subjects to better understand disease prevention and good health. IBM’s Watson cognitive computing platform, which has been used in healthcare applications, is now being used by Under Armour to better analyze data from wearables (and other sources) to provide evidence-based, highly personalized coaching.

The rate of progress is high, and several large players like IBM and Google have joined the broader effort to getting us to a point where we can safely and reliably use this data to be more descriptive, prescriptive and predictive in wearable and broader health experiences that are being conceptualized and developed. And beyond the large players, there is immense opportunity for companies who are thinking creatively and challenging the established architectural approaches. There are emerging opportunities for organizations to explore non-traditional partnerships and collaborations to bring disparate pieces of this puzzle together.

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Implications and opportunities for specific players in the broader wearables ecosystem

These challenges have several implications on various types of players in the broader wearables ecosystem. There are several opportunities to explore creative and novel approaches to these issues. The prize is high and so too is the level of motivation to make meaningful headway on these challenges.

OEMs

• The core wearables segments, such as activity tracking, are becoming increasing crowded making differentiation more difficult—it will be hard for new offerings to break through the noise unless they’re incredibly compelling.

• There are several other important problems out there (e.g., pain management, stress management, insomnia, to name a few) for which creative solutions may be becoming possible given advancements in sensor technology, material science, etc.

• Unless you’re focused on narrow, well-bounded problems, the data problems discussed in this paper are going to be important. However, there are increasingly interesting partnership opportunities to address the data challenges with non-traditional partners so look broadly across this business ecosystem which is becoming expansive.

• Look beyond selling directly to consumers—chronic disease prevention is becoming a greater priority world-wide and this is opening up buyers beyond consumers, such as employers, payers and even governments.

Service providers

• There are emerging opportunities to form non-traditional partnerships to address the challenges discussed in this paper.

• Even without offering a consumer offering, service providers have important capabilities and infrastructure assets that are going to be increasingly important in future wearables experiences. Consider, for example, how broad capabilities such as those within IBM’s Watson are now being offered behind a standard API. But also consider how smaller micro-services could provide a valuable ingredient to those building next-generation offerings.

• And with these alternative models for service delivery also come new revenue models based on asset and service licensing and utilization.

• Existing sales forces are going to be an increasingly important asset as intelligent wearable devices appeal to enterprise, industrial and healthcare clients—this is an asset that many service providers have in place, and which take a long time for new companies to replicate.

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Healthcare providers

• Providers are incredibly well positioned to help accelerate and steer how smart wearable devices are used to help prevent chronic diseases, and help people maintain healthy lifestyles.

• Providers are closely connected to acute health problems and are increasingly looking for new ways to reduce costs while improving outcomes. Technology innovators in these organizations will be important to help accelerate the understanding and application of new, promising approaches.

Payers and employers

• Emerging wearable technologies have the potential to reduce the cost of care and the cost of insurance. However, be sure to honor personal dignity. Some companies are learning the hard way that taking these programs too far can create anger and animosity amongst employees.

• As intelligent wearable devices increasingly rely on a broader set of data to derive insights, privacy will become increasingly important. Users need to understand how any shared information will be used, and to what extent this information can be used against their best interest. This will be a major challenge for payers and providers from both a policy and a PR perspective.

Semiconductor suppliers

• Focus on novel sensing approaches that will be difficult to replicate — these will be important ingredients for the smarter wearables. Back these up with scientific evidence to help ease adoption anxieties.

• There are increasing opportunities to develop platforms and sensors that are easy for emerging startups to obtain, evaluate and develop on. If your technology is strong, this will help improve the chances chips from the early prototypes make it into the commercial product.

• Picking the right customers to focus on in this market is hard. It is hard for wearables companies to succeed in an increasingly crowded market. Furthermore, for companies commercializing approaches (like sensing emotion) that haven’t been proven, or at least proven at scale, the risk of failure high. A lot of valuable effort has been lost on wearable companies with promising concepts but which failed to get a technology working at scale, or lacked an ability to execute a viable commercial strategy.

• There are new opportunities to explore novel partnerships in the broader ecosystem and uncover interesting approaches to the challenges discussed in this paper. Several semiconductor suppliers are already partnering with IoT device and data management platform providers, for example.

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Part 4: How we help Endeavour Partners helps many types of companies who are participating in the broader wearables ecosystem. Below is select list of Endeavour Partners’ prior engagements that showcase the breadth of the help we can provide, and the types of the players in the industry that we have worked with.

A major US service provider

Endeavour Partners worked with a major US service provide to develop a set of strategic options to pursue in the emerging wearables market. We developed detailed models and conducted consumer research to support these recommendations. As a follow-on engagement, we helped this service provider assess several of its technological capabilities to understand how these capabilities could be offered and monetized in the wearables market. As part of this work, we developed a detailed set of scenarios for how the wearables market would develop, which experiences were likely to be the most meaningful, and which foundational technological capabilities they would be dependent on.

A professional European sports team

Endeavour Partners worked with a top European futbol/soccer team (Serie A) to help them conceptualize and develop an offering for consumers and elite athletes based on a combination of wearable technologies, as well as a set of proprietary training, stress management, injury prevention, and data analytics methodologies the team uses with its own players. As part of this engagement we conducted a detailed assessment of the competitive landscape, conducted interviews and surveys with coaches, athletes, and focused groups of consumers. We identified and prioritized several market opportunities, and developed go-to-market plans with partners, and execution models for each.

A wearable device startup

Endeavour Partners worked with a new wearable hardware startup to help the company understand how the industry was evolving, how consumer behaviors and attitudes towards wearable devices were shifting, and the nature of the opportunities. We worked closely with the product team to conceptualize not only a wearable offering, but a set of complementary services and partnerships, as well as the retail and product launch strategy.

A major semiconductor company

Endeavour Partners worked with a major semiconductor company to help them understand how wearable technology and future medical devices will impact the disease management space.

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About Endeavour Partners

Endeavour Partners is a technology and strategy consulting firm based in Cambridge, MA. We work with organizations both large and small, helping them develop viable business strategies. We help leaders of technology organizations anticipate changes in their industries, navigate emerging threats and opportunities, and develop innovative strategies for growth. We strive to deliver strong client experiences and crisp, insightful, and actionable work products. We are easy to work with, adaptable and responsive due to our collaborative culture and passion for our work. The Endeavour Partners team is more than 30 strong, located in the heart of Boston’s startup locus, right across the street from MIT (and many members of our team are MIT grads). Most importantly, we love what we do.

About the author

Dan Ledger is a Principal at Endeavour Partners where he has led a great deal of work and research on wearable technology. He has advised numerous startups, larger service providers, and OEMs on product definition, service design, ecosystem strategy, and go-to-market planning within this space.

Dan has appeared on NPR, CNN and Bloomberg talking about the future of wearable technology. His research on wearable technology has been covered by GigaOm, The New York Times, The Guardian, Fast Company, Fortune, Forbes, TechRepublic, and CBS, among others. Dan speaks regularly at wearables and health-related conferences and is currently on the planning board for the WEAR2016 conference.

Prior to joining Endeavour, Dan worked in the semiconductor industry for 15 years in engineering and marketing roles where he developed a deep working understanding of many of the underlying technologies that are driving the wearables market. Dan holds a Masters degree from the Massachusetts Institute of Technology and Bachelor's degrees in engineering from Washington University in St. Louis.

For more information about Endeavour Partners, please visit our website at http://www.endeavourpartners.net. Or, if you are interested in discussing the future of wearables, contact Dan Ledger at dan@endeavourpartners.net.

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