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This past week I was in London speaking at Mind the Product. As usual, the Mind the Product team hosted a phenomenal event. You can watch the video here:
Full Transcript
This transcript is the script of the talk as it was written. It is not word-for-word the talk as given.
Hi everyone. I’m thrilled to be here. Today, I want to introduce a visual critical thinking tool that I’ve developed, but to truly understand its power, we need context. I’m going to begin with a relatable story, a product challenge many of you might recognize, and then we’ll delve into the tool itself, exploring how it helps us effectively compare and contrast options.
Back in 2008, I was a product manager at a startup focused on building online communities for university alumni associations. Like many product teams, we faced persistent challenges in driving user engagement.
Whenever we launched a new community, alumni would eagerly explore their new site. However, over time, the initial surge of traffic would invariably diminish, settling into a mere trickle.
While our customers, the alumni associations, appreciated our product, the alumni themselves—our end-users—didn’t engage as deeply as we hoped. We observed a pattern: initial excitement followed by declining activity.
Our user research revealed that alumni enjoyed sending messages within their community. They sought advice on a variety of topics, from career transitions to housing in new cities. This type of interaction was precisely what we aimed to foster.
However, there was a significant problem. No one wanted to receive these messages. Alumni in Dallas were inundated with emails about bikes for sale in Chicago, rentals in Boston, and internships in San Francisco. Our system inadvertently facilitated spamming entire alumni networks.
We realized that to boost engagement, we had to address the issue of unwanted messages. It was clear: reducing spam was crucial to enhancing the user experience and driving meaningful interactions.
Now, if you’re anything like me, your mind is already racing with potential solutions to this problem. But when I proposed brainstorming to my team, I received an unexpected response. Seth, one of our engineers, suggested, “Let’s integrate Google Maps!” He envisioned using the Google Maps API to display a map showing the global distribution of alumni.
I was taken aback. This idea seemed completely unrelated to the spam problem we were trying to solve. I struggled to see how Google Maps would help reduce unwanted messages. When I asked Seth for clarification, he admitted, “Oh, it won’t, but it will drive engagement because it’s cool.” To my dismay, the rest of the team echoed Seth’s sentiment. Maps were indeed considered “cool.”
At the time, I lacked the vocabulary to articulate my frustration. Intuitively, I knew that building “cool” features wasn’t sufficient. Knowing alumni locations didn’t strike me as a significant user need, and a Google Map integration felt more like a superficial gimmick.
This anecdote isn’t about right versus wrong, or about dismissing Seth’s idea outright. It’s more nuanced than that. It’s about my struggle as a product manager to involve my team in crucial decisions about what to build, while lacking a productive framework to guide that process.
Today, as a product discovery coach, I observe this scenario repeatedly across various teams. We often struggle to bridge the gap between a desired outcome, such as “increase engagement,” and the execution of solutions that genuinely contribute to that outcome.
This experience led me to deconstruct the underlying issues, and here’s what I discovered.
The Pitfall of Idea Fixation
One of the primary obstacles in effective product development is our tendency to become attached to initial ideas. We often fail to pause, reflect, and critically evaluate the merit of our own suggestions.
Generating ideas comes naturally. We encounter a user need, and almost instantaneously, a solution springs to mind. This immediate closure feels satisfying, leading us to develop an emotional connection with our initial concept.
When we become enamored with an idea, we bypass crucial steps of examination and critical analysis. We neglect to ask fundamental questions like, “Is this idea truly viable?” or “Is it the best approach?”.
This is precisely what occurred with Seth. He discovered the Google Maps API, became enthusiastic about its capabilities, and proposed its integration. His excitement was contagious, and the team quickly embraced the idea without sufficient critical evaluation.
The Limitation of Narrow Idea Consideration
Our predisposition to fixate on initial ideas often prevents us from exploring a wider range of potential solutions. In essence, by prematurely settling on a concept, we limit our options and potentially miss out on superior alternatives.
My team, captivated by the Google Maps concept, was eager to jump into development. They were driven by the desire to implement something that would immediately boost engagement.
Now, to be clear, the Google Maps idea might not have been inherently bad. However, research on brainstorming consistently demonstrates that generating a larger quantity of ideas leads to a higher likelihood of discovering truly exceptional ones.
Generating more ideas increases the probability of discovering better ideas.
Crucially, considering multiple ideas sets the stage for compare and contrast decision-making, a far more effective approach than a simple “whether or not” evaluation.
It’s challenging to definitively answer the question, “Is this idea good or bad?” because it treats “goodness” as an absolute quality. This binary, “whether or not” approach limits our ability to make nuanced judgments.
Instead, we should strive to frame our decisions as compare and contrast exercises. Asking, “Which of these ideas is the best?” encourages a relative evaluation, acknowledging that “goodness” is often context-dependent and comparative.
Consider Usain Bolt running alone on a track. Is he fast? It’s difficult to gauge in isolation. Now picture him racing against other athletes. Suddenly, his speed becomes undeniably evident. A compare and contrast scenario provides a clear benchmark for evaluating relative qualities.
Prioritize “compare and contrast” questions over “whether or not” questions in product decisions.
For those who believe they already consider numerous ideas, it’s important to clarify. While you might generate many ideas, they are often scattered and lack focus. I will revisit this point shortly.
Returning to my team’s challenges, we not only became fixated on our initial idea, limiting our exploration of alternatives, but we also…
The Absence of Opportunity Alignment
… failed to achieve alignment on a target opportunity – the specific problem we were aiming to solve.
Seth’s Google Maps idea frustrated me not because I deemed it inherently flawed, but because it seemed irrelevant to the core issue. It didn’t address the spam problem I was determined to tackle.
However, I neglected to ensure that my team was collectively aligned on the problem definition before we commenced idea generation. Consequently, while Seth was focused on the broader goal of “engagement,” he wasn’t addressing the specific problem of “reducing spam,” which was my primary concern.
Even when teams successfully align on an opportunity…
The Scarcity of Opportunity Exploration
Both Seth and I entered our brainstorming session preoccupied with a single, pre-conceived opportunity. We lacked a broader perspective on the range of possibilities.
… we often fail to explore a sufficient range of opportunities.
I entered the brainstorming session assuming that “reducing spam” was the definitive opportunity. Seth, conversely, was focused on “helping alumni connect with others nearby.” Both of us were operating within a limited scope, considering only one potential avenue for improvement.
Just as with solutions, we should avoid “whether or not” questions when evaluating opportunities. Instead of asking, “Is this opportunity worth pursuing?”, we should ask, “Which of these opportunities is the most promising?” This necessitates having a set of opportunities to compare and contrast. Without this comparative approach, we risk solving problems that are ultimately insignificant or low-impact.
We should have taken a step back and broadened our perspective, asking, “What are all the opportunities that could potentially drive alumni engagement?”.
Product teams often neglect to explore a sufficient range of opportunities before prematurely diving into solutions.
So, how can we avoid these common pitfalls and cultivate more effective critical thinking?
Visualizing Thought with the Opportunity Solution Tree
To address these challenges, I want to introduce you to the concept of an Opportunity Solution Tree. This framework is designed to help teams visualize their thinking, facilitate compare and contrast analysis, and ultimately make more informed product decisions.
Anders Ericsson, in his book Peak, summarizes the distinctions between experts and novices, arguing that experts utilize more sophisticated mental representations.
Ericsson defines mental representations as:
“… representations are preexisting patterns of information—facts, images, rules, relationships, and so on—that are held in long-term memory and that can be used to respond quickly and effectively in certain types of situations.”
The key advantage of sophisticated mental representations is their ability to enhance our information processing capabilities. They enable us to better understand, interpret, organize, and analyze information, leading to more effective decision-making.
The primary benefit of mental representations lies in their ability to improve information processing: understanding, interpreting, organizing, analyzing, and decision-making.
Isn’t this precisely what product teams require? A mechanism to understand, interpret, organize, and analyze the vast amount of information we gather, enabling us to make superior product decisions?
Reflecting on my team’s challenges, I realized that we each brought different mental representations to the brainstorming session. I possessed deep knowledge of our users from recent extensive user research. Seth, on the other hand, was well-versed in new technologies, having just explored the Google Maps API.
We were both relying on our individual mental representations to make rapid decisions. However, effective product teams must make swift decisions based on a shared mental representation – a collective understanding derived from their combined knowledge.
Successful product teams make decisions informed by their collective knowledge, forging a shared understanding.
The Opportunity Solution Tree emerged as my response to the question: “How can we externalize our individual mental representations and cultivate a shared understanding across our teams?” This visual tool facilitates this process by structuring our thinking around desired outcomes, opportunities, solutions, and experiments, enabling us to compare and contrast options at each level.
Starting with a Defined Desired Outcome
The foundation of an Opportunity Solution Tree is a clearly defined desired outcome. This outcome serves as the North Star, guiding all subsequent exploration and decision-making.
A product team must have a clear understanding of what they are striving to achieve.
My team had a defined desired outcome: to increase alumni engagement.
However, as illustrated by my team’s experience, a clear outcome alone is insufficient. We needed to delve deeper, asking, “What factors will drive increased engagement?” Before rushing to solutions, we should have systematically mapped out the opportunity space.
Opportunities can be viewed as customer needs or pain points, but they also encompass avenues for delighting users and replicating successes. Thinking broadly about opportunities is crucial for comprehensive exploration.
Opportunities Rooted in Research
These opportunities should be grounded in generative research—insights gleaned from customer interviews and observations. To maintain a user-centric approach, framing opportunities in customer language—phrases a customer might actually utter—is highly effective.
Opportunities should be derived from generative research methods: customer interviews and customer observations.
My team had recently completed a series of alumni interviews, providing a rich source of potential opportunities. We could have easily compiled the following list based on our findings:
Instead of fixating on pre-conceived notions, we should have dedicated time to identify the opportunities emerging directly from our customer interactions.
- “I get too much email.”
- “I’m moving to a new city and want to connect with local alumni.”
- “I need help finding a job.”
- “I want to stay connected with my alma mater.”
- “I want to know what my college friends are doing.”
- “I’m looking for interesting content to read or learn.”
- “I want to stay updated on my school’s sports teams.”
- “I’m looking to hire recent graduates.”
- “I’m considering donating but want to see the impact.”
- “I’d like to give back to the alumni community.”
- “I’d enjoy mentoring current students or recent graduates.”
This list directly reflects the feedback we gathered from our alumni interviews, representing genuine user needs and desires.
Now what? The typical next step is to prioritize this list, asking which opportunity is most critical to address. However, directly comparing and contrasting items on this unrefined list is problematic.
It’s illogical to compare and contrast an aspirational opportunity like “I’d like to give back to the community” with a more specific need such as “I want to hire a recent grad.” Prioritizing becomes challenging when the items being compared are inherently dissimilar.
Furthermore, the items on this list are not entirely distinct. For example, “hiring a recent grad” and “mentoring a student” are both expressions of “giving back to the community.” We would be comparing apples and oranges, hindering effective prioritization.
Avoid prioritizing a list of disparate and overlapping items.
Opportunity Solution Trees for Streamlined Prioritization
Grouping similar opportunities simplifies the prioritization process, making compare and contrast evaluations more manageable and meaningful.
By grouping related opportunities, the list becomes more manageable. In this case, the initial list can be condensed into three distinct categories:
- “I need help.”
- “I want to stay connected to my alma mater.”
- “I want to give back to the community.”
Instead of grappling with a lengthy, disparate list, we can now focus on prioritizing these three overarching opportunity groups.
Our research indicated that the most prevalent opportunity was “I need help”—alumni frequently turned to their community for assistance.
Now, consider my “I get too much email” opportunity. Where does it fit within this refined structure?
It begins to appear that Seth’s Google Maps idea might have some relevance after all, albeit indirectly.
Suddenly, Seth’s perspective gains traction.
If Seth and I were having this discussion today, the Opportunity Solution Tree would guide us to elevate the conversation. Instead of debating “too much email” versus “alumni near me,” we would start by comparing and contrasting the top three opportunity groups. It would likely become clear that “I need help” was the most pressing opportunity for alumni. We could then redirect our focus to prioritizing the sub-opportunities within the “I need help” category.
However, I might still argue that “I need help” is intrinsically linked to “I get too much email.” Effective help exchange requires connecting those seeking assistance with those who can provide it. If users are overwhelmed with excessive email, their willingness to engage and offer help diminishes.
This realization indicates that our initial opportunity structure may not be optimal. If two opportunities are inherently interconnected, they should be positioned closer together in the tree to reflect their relationship. Let’s refine the structure further.
Iterating on Opportunity Structure
There isn’t a single “correct” way to structure opportunities. The ideal structure should reflect the insights from user research and facilitate effective prioritization decisions through meaningful compare and contrast analysis.
First, I merged “I need help” and “I want to give back” into a broader opportunity: “I want to connect with other alumni.” This acknowledges the reciprocal nature of community interaction—both seeking and offering support.
Beneath this overarching opportunity, we have:
- “I want to connect with alumni professionally.”
- “I want to connect with people near me.”
- “I don’t know who to connect with.”
This revised structure brings both sides of the alumni network—those seeking and those offering help—closer together under a unified set of opportunities. This makes it easier to prioritize different types of connections (professional, location-based, etc.) without inadvertently favoring one side of the network over the other.
It also mitigates the initial disagreement between Seth and me. We can now both agree on focusing on this “I want to connect” branch. Our remaining difference lies in prioritizing the specific sub-opportunities within it. This refined structure allows us to leverage data to resolve our differences. We might ask, “How many alumni want to connect with people nearby?” versus “How many alumni struggle to identify relevant connections?”.
Finding the optimal structure often requires iteration. The key is to ensure it accurately reflects customer insights and supports sound decision-making about prioritization.
The structure of your Opportunity Solution Tree should mirror the insights gathered from customer interviews, ensuring relevance and accuracy.
Remember, there are no definitive right or wrong answers in structuring your Opportunity Solution Tree. The structure will evolve as your team gains deeper understanding of your customers. The value of visually externalizing your thinking lies in its ability to facilitate team alignment and resolve differing perspectives through shared understanding.
Once you have an opportunity structure that resonates with your team, you are ready to explore potential solutions.
Managing Idea Overload
Earlier, I mentioned that teams often don’t consider enough ideas, which might not resonate with everyone. Many teams, in fact, grapple with an abundance of ideas. Let me clarify my earlier point.
While teams may generate numerous ideas, these ideas are often scattered across various opportunities within the Opportunity Solution Tree, rather than focused on a single opportunity.
The true benefit of generating a high volume of ideas is to move beyond the initial, obvious solutions, spark creative thinking, and uncover truly innovative approaches. When brainstorming is spread thinly across the Opportunity Solution Tree, we generate many first ideas, but lack depth and exploration within specific opportunity areas.
This not only diminishes the creative potential of brainstorming but also results in a collection of solutions that are difficult to prioritize effectively. As previously discussed, prioritizing a list of disparate items is inherently challenging.
Focused Ideation for Target Opportunities
Instead of scattering ideation efforts, we should select a target opportunity – prioritizing row by row within the Opportunity Solution Tree – and then concentrate idea generation exclusively on that specific opportunity.
The goal is to delve deeply into a single opportunity, generating a diverse range of potential solutions. This sets up a robust compare and contrast scenario for determining the optimal approach to address that specific opportunity.
We should aim to generate a multitude of ideas specifically for solving a single, targeted opportunity.
Concentrate idea generation on a single target opportunity to foster depth and creativity, rather than spreading efforts thinly across multiple opportunities.
After generating a substantial list of ideas for a target opportunity, the natural inclination might be to prioritize the list and immediately begin experimenting with the top-ranked idea. However, this approach reverts to a “whether or not” decision: Is our top idea good enough? Instead, we should maintain a compare and contrast mindset. We should ask, “Which of these solutions appears most promising?”. When working with a large number of ideas, I recommend two techniques for effectively comparing and contrasting solutions.
Dot Voting for Initial Solution Filtering
First, use dot voting to narrow down a long list of solutions to a smaller, more manageable consideration set of 3-5 ideas. Research indicates that groups are more effective than individuals at evaluating ideas, and dot voting offers a quick and efficient way to gauge team sentiment.
Experimentation for Solution Comparison
Following dot voting, utilize experiments to compare and contrast the remaining 3-5 ideas, identifying which solution demonstrates the most promise.
Employ dot voting to refine a large solution list into a smaller set. Then, use experiments to rigorously evaluate and compare this smaller set.
It’s crucial to emphasize a key distinction here. Many teams use experiments to validate a single idea—to determine if it’s “good” or not, again falling into the “whether or not” trap. Instead, experiments should be designed to help you choose among a set of viable solutions, facilitating a compare and contrast decision based on empirical data.
The most effective way to achieve this is to identify the key underlying assumption for each solution and design experiments to test these assumptions directly.
Run experiments to systematically compare and contrast your top solution ideas, gathering data to inform your decision.
For example, if our top solutions for reducing email spam are:
- Recommending message recipients based on location criteria
- Automating message matching with relevant recipients
- Sending messages only to friends-of-friends networks
The key assumptions for each solution might be:
- Will users seeking help trust system-generated recipient recommendations?
- Test: Prototype the user interface and gauge user response to recommended recipients.
- Can we accurately predict suitable message recipients?
- Test: Machine learning team conducts feasibility experiments to assess prediction accuracy.
- Are friends-of-friends more likely to offer assistance?
- Test: Analyze historical data to determine response rates from friends-of-friends in past messages.
A common question arises: “How do I determine if experiment results are ‘good’?” For instance, is a 15% conversion rate satisfactory? This is akin to asking, “Is Usain Bolt fast?” when he’s running alone. It’s difficult to judge in isolation. However, by experimenting with multiple solutions concurrently, you can ask, “Which of these solutions performs best based on the collected data?” This comparative question provides a much clearer and more actionable answer, just as Usain Bolt’s speed is undeniable when compared to other runners.
Design experiments to choose among a set of solutions, facilitating comparison, rather than simply evaluating a single solution in isolation.
The Advantages of Opportunity Solution Trees
If, like me, you aim to involve your entire team in crucial build decisions, but frequently encounter challenges similar to my Google Maps experience, or if your team gets bogged down in unproductive opinion-based debates, I strongly recommend adopting the Opportunity Solution Tree framework.
Taking the time to visually map out your thinking with an Opportunity Solution Tree helps teams identify and rectify common critical thinking errors. These errors include framing decisions as “whether or not” choices instead of more insightful compare and contrast evaluations.
The Opportunity Solution Tree also serves as a dynamic discovery roadmap, enabling your team to align on a shared understanding of the opportunity landscape and the potential pathways to achieving your desired outcome.
And like traditional roadmaps, it effectively communicates your learning and progress to leadership and the wider organization.
Your Opportunity Solution Tree functions as a powerful discovery roadmap, fostering team alignment, enhancing communication, and driving informed decision-making.
Implementing Your Own Opportunity Solution Tree
Use these practical tips to begin building your own Opportunity Solution Tree and transform your product discovery process.
Start by defining a clear, measurable desired outcome.
Next, systematically map out the opportunity space, ensuring opportunities are grounded in generative research—customer interviews and observations.
Experiment with different opportunity structures. Varying structures can unlock new perspectives and possibilities.
Prioritize opportunities row by row within the tree, enabling focused decision-making.
Limit idea generation to your selected target opportunity, fostering depth and creative exploration.
Finally, design and execute experiments to rigorously evaluate and compare and contrast your solution set, driving data-informed decisions.
If you develop your own Opportunity Solution Tree, I’d be delighted to hear about your experience. Please reach out via email or Twitter. And if you wish to delve deeper into the research referenced today, I’ve compiled a list of sources for your convenience.
Thank you!
