Case Study: Watz Founder Alessandro Latif Learns The Fine Line Between What Customers Want And What They Need

Keeping the conversation fixated on the problems they currently experience helps bring clarity to product development priorities.

Aug 29, 2022

Introduction:

Not analyzing wearable and performance data in conjunction makes it impossible to personalize health decisions. Closed hardware ecosystems and the lack of adequate analysis tools stifle global health advancement and the impact that wearables can provide. Our niche of elite sports scientists, coaches, athletes, and teams competing professionally, collegiately, or at the Olympic level has a TAM of over $200 million. Once we have permeated the high-performance sports market, we will transfer these insights to wearable consumers whose TAM is over $530 billion.

Watz ingests thousands of performance data points and biomarkers to deliver insights to athletes, coaches, and consumers that maximize their performance. Think of it how your doctor is able to digest graphs and data and tell you exactly what you need to know, based on years of experience and intuition. Watz is founded by ex-pro athletes and software/hardware engineers from companies like Apple. We are advised by a council of scientists from the Howard Hughes Medical Institute, Nike, and Olympic teams.

Executive Summary:

Problem: Helping Fitness Users Understand More From Their Wearable Devices

We began engineering a more affordable device, but while prototyping with coaches and athletes, we learned of a far more important problem they were experiencing - these athletes didn't need new sensors. They needed to understand more from the sensors they already had.
Market: Athletes Looking For A Quantitative Edge In Their Fitness

To ensure product market fit, we worked with an initial user base comprised of elite coaches, so we could map out the ways in which they interact with our product and analyze their data. These elite coaches were the ones who needed the most intricate detail possible.
Solution: Intense Customer Education Builds Momentum For The Product And Team

We learned the importance of momentum early on and made sure that every engagement we had with a customer got them a significant amount more excited about the product, team, or mission. We created workshops between our early users and our technical team where we would take turns to present findings, ideas, or feedback.
Team: Healthy Conflict Enables Critical Paths To Be Executed

A big thing is to ensure conflicts don't prevent critical paths from being executed; in other words, product discussions need to happen well in advance.
Fundraising: Alignment On Expectations Is A Critical Result Of A Successful Fundraising Round

Startups often feel the pressure to pivot against their own convictions to please investor experience. Yet, few investors understand the technology and people you are helping, and a lot are inexperienced on the operating side of a company.
Takeaway: Founders Must Know The Difference Between What Customers Want And What They Need

What a customer tells you they want is often not directly correlated to what they need. Keeping the conversation fixated on the problems they currently experience helps bring clarity to product development priorities.

Case Study: Watz

Problem: Helping Fitness Users Understand More From Their Wearable Devices

Tell me about a problem or set of problems that you’ve had to solve on your journey to product-market fit.

We started solving the problem of access to technology by building a more affordable cycling power meter. A device is used on any bike to measure the useful energy being delivered through the pedals by a cyclist's legs. Typically these cost between $300 to $1000, but the strain gauge itself can be bought for a few cents on Alibaba. We began engineering a more affordable device, but while prototyping with coaches and athletes, we learned of a far more important problem they were experiencing - these athletes didn't need new sensors. They needed to understand more from the sensors they already had.

Why were these problems so critical to solve? What was it like personally struggling to overcome these challenges to achieving PMF?

This problem is critical because people are overwhelmed with health data, they don't have data science skills to utilize it fully, and they are coming to unquantified conclusions based on their instincts. Our journey begins with Olympic coaches and athletes, as they provide the greatest granularity into human health. They are working at the limit of human performance, so solving their problems is the lynchpin to understanding the human body in as much detail as possible. From there, we can develop consumer applications that can help everyday people achieve their health goals as quickly and simply as possible.

Personally, what I struggled with most was communicating how we are setting out to revolutionize human health. When you build something that has never existed before, we had to learn to be very clear about what problems we were going to solve and how those problems fit into the bigger systemic system. We believe in order to build the best map of human health, it needs to be a discourse between all experts and tinkerers, built on open foundations. Tying a big idea into a tangible next step and describing the reasoning for this tactical choice has been challenging. Ultimately for us to translate meaning into wearable and health data, we have to tap into the dynamics of evolution, professional sports is all about the pinnacle of evolution, and it is through this insight that we plan to send ripples across the ocean of digital health's future.

Developing an analysis platform and decision support system can be very challenging because it's very easy to jump to conclusions for solutions. This is a problem as it can bias the discovery phase with current and prospective customers.

VECTORS - Finding attack vectors to probe a problem. As the founding team at Watz are all former athletes, we already had a sense of how coaches manage their athlete data. This gave us an attack vector, a strong foundation that we could use to probe the specific problems coaches had in ingesting, tracking, and analyzing their athletes' data.
TRUST - Building trust between you and a potential client is critical. You need to demonstrate that you have the deep industry knowledge to potential customers and are speaking the same language. Watz does this by studying sport-specific research whilst being experts in math, machine learning, physics, and building the best possible analysis tool. Having them trust you can solve their problem and then being very personal with communication, feedback, and demo sessions with our technical team is valuable because it shows you care, thus building even greater levels of trust.
LABELS - Once we identify a new problem, we need to label it in a way that allows us to prioritize. Is the problem assumed, qualified, or quantified? What are the costs associated with solving it? What are the effects of solving it? These labels allow us to form a plan of approach and tackle problems in order of importance.
DATA - What you don't know about a specific problem is much more valuable than what you know. As a startup CEO, this requires a lot of introspection about why this problem exists and why the market hasn't yet solved it. Industry leaders are frequently so deep in their industry that they don't think about their business from an outsider's perspective. Alternatively, industry leaders don't have the tools or capabilities to solve a pain point. This is how Watz has come in and solved the issue of data aggregation for coaches, very few of whom have the data science skills to achieve this on their own.

Market: Athletes Looking For A Quantitative Edge In Their Fitness

Let’s get deeper into the pain point or points you were trying to solve. Imagine I’m a customer thinking about using your product or service. How do you go about understanding my pain and creating a solution to address it?

Since sports is a winner takes all game, the knowledge of the top 0.01% of coaches and sports scientists is more valuable than the bottom 99.99%. The top 0.01% have a level of understanding of biology, nutrition, and biomechanics to make Eliud Kipchoge run a marathon in under 2 hours, breaking all ideas of what is humanly possible. We at Watz have a council of elite sports scientists, all of whom specialize in different sports and who are the brains driving the insight for coaches. They distill what they know from reading hundreds of graphs into a single sentence insight for a coach to act on.

Over the course of working with top coaches, we learned that they had informational blind spots on metrics as simple as the way an athlete recovers. There are so many data variables to consider (lactate flushing, heart rate, sleep quality, nutrition, and hundreds more), as well as individual differences across all of them. All of these biological processes working together produce recovery. However, learning the cause and effect of how each of these metrics intertwines to produce athlete recovery requires extremely complex analysis. Coaches lack the tools and the know-how to be able to explore all of these avenues with scientific approaches and instead have to rely on a support team of bio-mechanists, nutritionists, physiotherapists, sports scientists, and many more assistants.

Discovering your pain, whether a coach, athlete, or consumer, becomes a game best played through conversation. Being mindful that language fails to fully describe reality, eventually leaving us with information loss of what the actual problem is. We encourage our customers to encode the problem more clearly through questions that bring to light details we need more clarity on. Our ultimate goal as a company translating health data is to model the mechanics of human health.

Assuming you’ve managed to address the pain points I face as a customer, what additional information did you discover in your journey to PMF that there’s a large market in need of a solution to the existing problem?

Imagine you bought an Oura ring, which does a great job of tracking your sleep, and it senses you had a bad night despite a seemingly normal bedtime routine. From this, you might assume that your room was too warm or a noise woke you. If, however, you had a blood glucose monitor, you would see that last night, you had an unusually long decay in your body's glucose levels after you ate dinner. These two metrics together tell us that you didn't sleep well because your body could not digest effectively one of the ingredients you ate last night. While this is a rudimentary example, it shows how the two tools, analyzed together, give you a clearer insight into your health and teach you what to do if you want to sleep well.

The same is true for how we eat, train, sleep, work and recover. There are many variables to consider when recommending decisions that maximize our performance and health. As new sensors come online, the insights we can draw will become ever clearer. Ultimately we are unlocking the secrets to the decisions that lead to optimal human performance and health. There are over 50 million Americans who currently use wearable health trackers, but 1 in 3 of these people give up on their devices due to the inability to receive health-altering recommendations.

We know there is a very large market in need of the insight we are building, but we have very immediate problems we are solving in elite sports performance. Our guiding principle is to solve with detail problems whose outputs can lead us to solve larger systemic problems.

How did you narrow your scope of what portion of the market you wanted to tackle first? Who did you decide would be your first beachhead customers and why?

We took a view on the macro problem being addressed in the health and wearable space: our bodies respond differently when we train, eat or sleep. However, tracking 100s of metrics doesn't provide a consumer with the full picture of the health decisions they need to make to generate improvement.

Coaches and sports scientists have spent several decades of their lives learning how to help their athletes perform at their best. We knew that the insights elite athletes have would help consumers make better health decisions. We focused on what problems they were dealing with when using biometric data, and as we dug deeper, we learned that few companies solve these pain points, which gave us even more conviction to focus on this narrower portion of the market.

ICP → Growth

It's essential to categorize and define ideal customer profiles (ICPs), even if they change over time. We hypothesized how coaches, athletes, and sports scientists would use Watz, how different sports would use Watz, and how different levels of an athlete (Pro / Semi-Pro / Amateur) use Watz. Even though not all of these hypotheses turned out true, it allowed us to build out questionnaires and research that was tailored to each ICP.

Cold → Warm Conversations

We found cold emails and social media outreach to be the most effective way of getting in touch with coaches. We initially opted against surveys in favor of one on one conversations. This allowed us to build out a small Alpha program of elite coaches to trial our tool. We then waitlisted the other coaches we had spoken with. We were thus able to track what elite coaches wanted in a tool using our Alpha program and what regular coaches wanted in a tool via automated questionnaires.

Qualify + Identify

There is a balance between exciting potential customers about what Watz is doing, qualifying they are a fit for our product, and identifying their pain points. Watz's goal is to spend the least time exciting people. This usually happens intrinsically through our brand image and the glimpses of the product we show. Qualifying whether a user might be a fit for the Alpha program means validating whether they have the technical and theoretical subject knowledge to be able to use our "rough and ready" tool. All of this has to occur whilst ensuring we get as much data as possible on their pain points.

Data-Driven Refinement

A big asset we had early on was a platform called Tegus to perform market research on the landscape of solutions available. With our hypothesis, conversations, identified pain points, and an understanding of the overall market landscape, we ensured we used as much data as possible in refining our ICP, market positioning, and product features.

Solution: Intense Customer Education Builds Momentum For The Product And Team

How did you build your solution to maximize its relevance with the customer and ensure product-market fit? If you haven't found PMF yet, what have you learned? What are the blockers for getting to PMF?

Analyzing biometric data is about effectively supporting people with information that helps them make better decisions and teaching them why it is the optimal decision for them. Consumers don't want to get lost in the weeds, spending hours reading studies and analyzing graphs. They just need the "so what." What could you do or learn about with this insight?

We initially built this product with elite coaches. All of whom understood the technical detail of what they were using and wanted to use data for. This group helps us decipher meaning from the data, simplifying graphs and numbers into concrete insight and recommendations.

To ensure product market fit, we worked with an initial user base comprised of elite coaches, so we could map out the ways in which they interact with our product and analyze their data. This allowed us to understand what they were looking for, so we could tailor the product with a top-down approach. These elite coaches were the ones who needed the most intricate detail possible. Collecting what they found most useful from all the different data points allowed us to pick the metrics that produce the most output (in terms of maximizing health and performance). From here, we could build a consumer-friendly product that produces the best health decisions.

Product market fit is a continuous challenge. There are proxies to know when you've reached a good point - usage and willingness to pay being principle - but PMF is still a process. Prioritizing the most important next thing to build is something we focus on at Watz. The biggest challenge to PMF arises when you want to introduce a feature that the consumer doesn't know they need yet. We haven't yet built against PMF yet, in this way, but it is a chase that never ends!

What are some of the things you did that “didn’t scale” to shape your solution today?

We realized early on that hardware does not scale well without large capital. We started out building cycling sensors but realized the costs were high, R&D was slower, and the market was smaller. Most importantly, people didn't need more data. They needed more understanding.

Software allows us to scale quickly, pivot easily, and address new markets by tweaking or adapting our product. At Watz, we identified all the markets that we could move into and what it would take to adapt the product so that we could solve the problems of those markets.

Pipeline Buckets

As we sourced leads that matched our ICP, we would have them go through 4 stages, the first being an initial outreach, the last being a qualified customer. Watz's pipeline is specifically set up so that a qualified customer is either bucketed between our waitlist or one of a select few early alpha users.

Alpha Importance

We specifically chose to have an Alpha program of no more than 20 coaches and sports scientists. This forced us to select extremely high-quality individuals who are capable of helping us develop an intricate and precise analysis tool. We worked closely with the Alpha users to understand what functions they wanted, how they were using the tool, and ensuring they were touching as much of the tool as possible. Being a data company, we tried to quantify as many of these touch points as possible. This allowed the Watz growth team and Watz product team to track all of these interactions.

Operating Systems

We use Notion to manage our entire company. For product, growth, engineering, and everything in between. To have all information flow through 1 tool allows us to connect Operations with Engineering - a very critical link when finding product market fit. We have a structure in place that allows the Growth team to be highly detailed on the pain points discovered during customer conversations and for these pain points to be tracked all the way to the features Engineering builds. Engineers, therefore, have visibility in the nature of the problems being solved beyond myopic requirements, and they also have the ability to contact customers directly to deepen specific pain points.

Validating Success

When designing a feature, we build a set of 1-3 KPIs that allow us to track the effectiveness and success as we build and iterate it. It is not always possible to build these KPIs, so we also gauge success through qualitative interviews and usage patterns.

What did you learn to best engage with your customers? How did you build a tight feedback loop with your customers to rapidly improve your solution to their problems?

We learned the importance of momentum early on and made sure that every engagement we had with a customer got them a significant amount more excited about the product, team, or mission. We created workshops between our early users and our technical team where we would take turns to present findings, ideas, or feedback. This allowed us to hone in on the solutions suited to solve the most painful problems across our users.

Often the user does not know what he wants but leaves clues as to where their pain lies. We found it very important to collect and process all the key pieces of information each customer and prospect customer provided. Seen together and consolidated, it provides a very good compass for the solutions to their biggest problems.

We also study usage analytics for each feature, and if possible, we construct KPIs like usability and time-to-action. These help us validate features and provide measurable value to accompany the qualitative data we get from conversations. To summate, having effective conversations with our customers and to be data-driven about how we design solutions are ways that we maximize our feedback loop.

Walk me through how you landed your first few customers as you were building your product or service.

Speaking to our mission of creating a clearer picture of the decisions that are optimal for our personal health is important, especially when the product is in its early stages. It sets the tone of why you have chosen to speak to a prospective customer; it excites them for why they could be a part of it.

We are very selective about who we onboard as users and customers to the platform. It allows us to focus our development and build the right details into our analytics tool and larger infrastructure. So we go through a heavy set of qualification questions to ensure they fit what we were looking for in our early customers. If you were, we invited the coach to join our development program, but otherwise, we would keep them updated on our progress before the platform is available to the general public.

Our approach was powerful because it was us that questioned the fit, and so if it was important enough, it made them think about why they needed our access to our product.

Team: Healthy Conflict Enables Critical Paths To Be Executed

If you have a cofounder, walk me through a time that you two had a conflict. What was it about? How did you handle the situation? What was the resolution, and how did it impact your working relationship with your cofounder?

Product is quite often the center of conflict, and for good reason. We are all contributing blood, sweat, and tears to making the best product possible for our customers, and when there is deviance in what we as a collective deem important, then we need to align what the best direction to proceed is.

A big thing is to ensure conflicts don't prevent critical paths from being executed; in other words, product discussions need to happen well in advance.

Misalignment is important, we see the world differently, and aligning differences yields more robust outcomes that have been thought through from different perspectives. Misalignment, where there is friction, is not pleasant; however, so creating systems like decision leaders for a specific domain can help make decision-making more effective.

Continuing to nurture trust is fundamental, and we often do roundtables where we talk about where specific values where broken and where specific values where demonstrated. The honesty of conversation builds lots of trust, and this trust does wonders in conversations where there is misalignment.

What key qualities did you look for in key early hires to increase your chances of discovering product-market fit, and how did you prioritize what types of hires you needed to make first?

We look for early hires that are 'assassins' in their specific field. They need to know this field deeper and more broadly than anyone else on the team and, at the same time, can be acutely aware of the big picture and the important questions.

We are a very technical team, even on the operating and commercial sides. All of us have backgrounds in professional or collegiate sports and have worked with the best companies and athletes in the world - Nike, Apple, and several Olympic programs across a domain of sports. Our technical expertise is centered around biophysics and artificial intelligence, and we are partnered with the foremost research institutions in these fields.

If you have an athletic background and want to be at the forefront of health tech, it would be a pleasure to connect. Our prioritization currently is very much scaling our technical team - software engineers and data scientists with a biophysics background - but we also have a few roles to fill on the commercial and marketing side.

As CEO, my job is to scale myself as much as resources allow. My focus is balanced between building relationships and systems, winning deals, and refining the vision, strategy, plans, and tactics across product, growth, and other business functions. It's important to me that I touch on all of these segments in a given week.

Identifying skillset gaps early in Watz's growth and defining the roles that spearhead certain functional pieces of the business was the first big step. Once I had the team built out, my job became to ensure that the team had systems in place allowing them to organize efforts and track progress against goals. This is then the most important piece of all to establish clear goals of success for each spearhead position.

The introduction of new hires meant Watz's capabilities began growing, but with it, bottlenecks in areas requiring more resources. Identifying these bottlenecks in output and finding ways to automate them without hiring is a primary objective of mine, but then knowing that there might be areas where hiring and outsourcing do make sense. This is especially important as the spearhead positions will be responsible for a lot, and so you don't necessarily want to blind them in the depths of something that could otherwise be outsourced.

When our team grew, the challenge was to imagine the structure of a wider team that would yield the greatest output against the company's objectives. Planning this in advance is important as it takes time to build relationships with good people and incredibly strong hires. Always be hiring is a strategy spoken to often by founders, and it is an approach I adopted even when roles were not necessarily available at the time. This meant cultivating relationships to the point where by the time it comes to hiring, you have almost already done so.

If there was a potential employee of your startup reading this Case Study right now, how would you convince them that joining your team is the next best step in their career?

Startups and corporates developing applied technologies typically cut corners to build revenue-generating products and services as quickly as possible. Our stance is to understand and optimize a system as complex as the human body. Corners cannot be cut, and this quest is one that may know no end.

It's exciting as it is a highly multidisciplinary effort combining the frontier of product development, software development, artificial intelligence, and decision sciences to optimize any given system with technology. The term we use to describe the technologies we are building is cybernetic knowledge-based decision support systems.

We are proud to have a young team, all of whom are under the age of 28. These individuals are anomalies in their respective fields, often on the side of the controversy, but people who diligently work from fundamental principles and higher dimension questions about what is important to solve and how to best solve it. Despite our young team, we are supported by experienced advisors stemming from top research institutes, Olympic teams, and Fortune 500s.

Watz is an AI R&D company whose ethos is the fusion of Nike and Deepmind. We build technologies and products that optimize human fitness, health, and performance. I would encourage anyone with a background in sport, a passion for data-driven software products, and deep tech to reach out and learn more about what we are bringing to life.

Fundraising: Alignment On Expectations Is A Critical Result Of A Successful Fundraising Round

How did you set expectations with investors at seed and Series A? What is the main difference in those expectations as your company grows from one stage to another?

The most fundamental expectations are value-driven. If these do not align, then it is not in the interest of any party to transact. Startups often feel the pressure to pivot against their own convictions to please investor experience. Yet few investors understand the technology and people you are helping, and a lot are inexperienced on the operating side of a company. We ensure our investors can go beyond deploying capital but are expected to take a company to a new phase of operation.

Our seed round is on the horizon, and we are bringing together an awesome roster of investors: owners of sports teams, medical practices, and ex-hall of fame athletes, with investment backgrounds mainly in health, deep tech, and web3. We only reveal round details with trusted warm intro'd investors, but what I can say is the round is in the seven figures for the opportunity to scale the foundation of what we have built so far.

In preparation for this new phase, we ensure alignment on our research, product, growth, and marketing strategies. Ensuring that viable and measurable objectives are set and that there is awareness of the systems to be built in order to attain them. We also require a great level of trust to be open, honest and transparent on the challenges currently being faced so everyone can help win together.

How does dilution work as you go from seed to Series A?

Typically all stock issued at seed is owned by founders, investors and an options pool for early hires. Dilution at subsequent raises will dilute ownership by the amount raised against the round valuation. Investors can protect dilution in two big ways:

Pro rata rights - provides them the right to buy up the original percentage owned at a previous round. This means that, in theory, investors can experience no dilution whilst founders would almost always experience dilution.
Broad-based weighted anti-dilution clause - lessens the dilution experienced at subsequent rounds.

Takeaway: What A Customer Wants Is Different From What They Need

What are the key lessons have you learned so far from your journey to achieve product-market fit?

Always being receptive when pre-conceived ideas about the product don't feel right. Often the shift to the right idea is gradual and can lead to ineffective product development instead of taking the more drastic shift early on.

What a customer tells you they want is often not directly correlated to what they need. Keeping the conversation fixated on the problems they currently experience helps bring clarity to product development priorities.

What’s the hardest problem you’re facing now after solving the prior one(s)?

Fundraising was a previous challenge. It was hard because, as a technical founder, I was trying to balance building product and raising. You can develop bootstrapped, or you can raise and develop 10x faster, and we knew we had gone beyond the threshold of understanding what our market needed. I had to personally step away from development to spend my time building relationships with awesome investors and hires.

Our challenge going forward is to continue to foster a culture of pioneers venturing into a hard problem with grit and determination to win. Ensuring that we have superstar players but that we can operate as a championship-winning team. Most of us have been there before, but this time the challenge is to ensure we can build the map and compass to personalizing decisions from health and wearable data - something that every company in the space has either shied away from or has opted for closed proprietary morals. The latter is important to collaboratively piece our intelligence on human health.

Click Here for Watz’s Founder File:

Watz’s Founder File: Building Practical Cybernetic Decision Support Systems

Description: Algorithms will continue to influence and control our economies, our mobility, and even our information biases. Yet we build algorithms whose output cannot often be predicted or explained and we deploy autonomous machines with little consideration of social, political, and ethical ideas. Importantly our ideas about the world are ever moving, growing, and adapting.

Applying systems thinking to the development of AI would support human decisions in ways that promote organization and control of any open system. But how do we practically get there?

Watz's Founder File

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