Have you ever felt stuck when trying to decide which product idea to pursue? Many product teams face the challenge of choosing the right path forward, often getting sidetracked by exciting but potentially irrelevant ideas. In my book, Continuous Discovery Habits, I delve into structured approaches to product discovery. Today, I want to share a powerful visual tool that can transform how your team Compare Product options and make strategic decisions: the Opportunity Solution Tree.
This tool emerged from years of experience helping product teams navigate complex choices. I recently presented this framework at Mind the Product London. You can watch the full presentation here:
Full Transcript
This is a detailed transcript based on my talk, emphasizing the core principles and practical application of the Opportunity Solution Tree for effective product comparison.
Image alt text: Teresa Torres presenting on Critical Thinking for Product Teams at Mind the Product London 2017, highlighting the importance of strategic product comparison.
Hello everyone, I’m thrilled to be here to discuss a critical thinking tool I’ve developed, designed to help product teams like yours make better decisions, especially when you need to compare product directions and features. To illustrate its value, I’ll start with a story many of you might find relatable, before diving into the specifics of the tool itself.
Let’s rewind to 2008. I was a product manager at a startup focused on building online communities for university alumni associations. We faced a common product challenge: initial excitement followed by declining user engagement.
When we launched a new community, alumni would flock to explore their new online space. However, over time, site activity would consistently dwindle to a mere trickle.
While alumni associations, our direct customers, were satisfied with our product’s features, the actual alumni, our end-users, weren’t deeply engaged long-term. The initial launch always brought a surge of traffic as alumni checked out their community, but sustained interaction was a consistent problem.
Our user research provided valuable insights. Alumni enjoyed sending messages within their community, seeking advice on everything from career transitions to choosing neighborhoods in new cities. This type of interaction was exactly what we aimed to foster.
Image alt text: Illustration depicting a user in Dallas receiving irrelevant emails about jobs in San Francisco, rentals in Boston, and items for sale in Chicago, emphasizing the issue of spam and poor product targeting, relevant when you compare product features.
However, there was a significant flaw. No one wanted to receive these messages. Alumni in Dallas were bombarded with emails about apartments for rent in Boston, bikes for sale in Chicago, and internships in San Francisco – completely irrelevant to their location and needs.
We realized that to boost engagement, we had to drastically reduce the volume of these unwanted messages. The system was essentially enabling users to spam their entire alumni network. Improving alumni engagement hinged on solving this issue of irrelevant communication.
If you’re like me, your mind is already racing with potential solutions. But when I proposed a brainstorming session to my team, the response was unexpected. Seth, one of our engineers, enthusiastically suggested, “Let’s integrate Google Maps!” His idea was to embed a map using the Google Maps API, visualizing where alumni lived around the globe.
Image alt text: Humorous image of a brainstorming meeting with Darth Vader, highlighting the often chaotic and unfocused nature of brainstorming sessions when teams need to compare product features.
I was taken aback. Google Maps seemed completely unrelated to the spam problem we were trying to solve. Puzzled, I asked Seth how a map would address the issue of irrelevant messages. He responded, “It won’t solve spam, but it’ll be cool and drive engagement!” Looking to the rest of the team for support, I was surprised to find they agreed. “Maps would be cool,” they echoed.
At that moment, I struggled to articulate my frustration. Intuitively, I knew that building “cool stuff” wasn’t enough. Knowing where alumni lived didn’t strike me as a critical user need, and adding a Google Map felt like a superficial feature, a mere gimmick.
This story isn’t about Seth being wrong and me being right. It’s far more nuanced. It’s about my struggle as a product manager to effectively involve my team in deciding what to build, especially when we needed to compare product directions, without a productive framework for collaboration.
Today, as a product discovery coach, I see this scenario repeated across countless teams. We often lack a structured approach to move from a desired outcome, like increased engagement, to identifying and executing solutions that genuinely drive that outcome. We struggle to effectively compare product ideas and choose the most impactful ones.
This experience led me to deconstruct the problem, and I discovered some recurring patterns.
The Pitfalls of Product Development: Why We Struggle to Compare Products Effectively
Through my work with numerous product teams, I’ve identified key reasons why we often falter in our product development efforts, particularly when it comes to effectively compare product options. These pitfalls hinder our ability to make strategic choices and build truly impactful products.
1. Falling in Love with Initial Ideas
It’s remarkably easy to generate product ideas. We hear about a user need or a market trend, and our minds instantly jump to solutions. This rapid ideation, while seemingly productive, often leads us astray.
Because it feels rewarding to quickly close the loop from problem to solution, we often become enamored with our first idea. This phenomenon, known as confirmation bias, clouds our judgment and prevents us from critically evaluating the merit of our initial thoughts.
And when we fall in love with an idea, we stop questioning it. We don’t pause to reflect, to ask the crucial question: “Is this idea actually good?” This is precisely what happened with Seth. He discovered the Google Maps API, got excited by its possibilities, and immediately envisioned a feature. He shared his enthusiasm, and the team quickly jumped on board, captivated by the “coolness” factor.
2. Not Considering a Diverse Range of Ideas
Image alt text: Text graphic stating “We Don’t Consider Enough Ideas”, visually highlighting the importance of exploring multiple product solutions for effective comparison.
When we become fixated on our initial idea, we fail to explore a sufficient range of alternatives. My team, captivated by the Google Maps concept, was eager to start building immediately. They wanted to implement a feature that would, in their minds, drive engagement right away.
Now, the Google Maps idea might not be inherently bad. However, research on brainstorming consistently demonstrates that generating a larger quantity of ideas leads to a higher quality of ideas overall. The more options we consider, the better our chances of discovering truly innovative and effective solutions when we compare product directions.
When we generate more ideas, we generate better ideas.
More importantly, by considering multiple ideas, we set ourselves up for “compare and contrast” decisions, which are far more effective than “whether or not” decisions.
A “whether or not” decision forces us to evaluate an idea in isolation: “Is this idea good or not?” This is a difficult question to answer because “good” is treated as an absolute, undefined quality.
Image alt text: Text graphic contrasting “Whether or Not” decisions with “Compare and Contrast” decisions, emphasizing the superior approach of comparing product alternatives for better decision-making.
Instead, we should aim for “compare and contrast” decisions: “Which of these ideas is the best?” This question is easier to address because it frames “good” as a relative quality, assessed through comparison.
Think about speed. Is Usain Bolt fast? If you only see him running alone, it’s hard to judge. But when you compare him to other runners, his speed becomes undeniably apparent. “Compare and contrast” decisions make it easier to evaluate relative qualities, whether it’s speed or the potential of a product idea.
Ask “compare and contrast” questions, not “whether or not” questions.
Now, some of you might be thinking, “We do consider many ideas.” And you might be right. Many teams are overflowing with ideas. But the problem isn’t necessarily the quantity of ideas, but rather their focus and strategic alignment, which I’ll address shortly.
Returning to my team’s challenges, we not only fell in love with our first idea and neglected to consider alternatives, but we also…
3. Lack of Alignment on a Target Opportunity
Image alt text: Text graphic stating “We Don’t Align Around a Target Opportunity”, visually representing the lack of focus and alignment that hinders effective product comparison and decision-making.
… we failed to align on a shared understanding of the problem or opportunity we were trying to address. Seth’s Google Maps idea frustrated me not because I thought it was inherently bad, but because it felt irrelevant. It didn’t solve the spam problem I was focused on.
I hadn’t taken the time to ensure that my team was aligned on the problem we were solving before we jumped into idea generation. As a result, Seth was thinking about the overarching goal of “increasing engagement,” but he wasn’t focused on the specific problem of “reducing spam,” which was my priority. Without this alignment, it’s impossible to effectively compare product ideas against a common objective.
Even when teams do align on an opportunity…
4. Insufficient Consideration of Diverse Opportunities
Both Seth and I entered that brainstorming session fixated on a single opportunity. I was convinced that “reducing spam” was the primary opportunity, while Seth was focused on “connecting alumni geographically.” We were both operating within a limited scope.
Image alt text: Text graphic advocating for asking “Which of these opportunities looks best?” instead of “Is this opportunity worth pursuing?”, highlighting the importance of comparing opportunities in product strategy.
Just as we should avoid “whether or not” questions for ideas, the same applies to opportunities. We shouldn’t ask, “Is this opportunity worth pursuing?” Instead, we should ask, “Which of these opportunities is the most valuable to pursue?” This requires us to have a set of opportunities to compare. Without this comparative approach, we risk solving problems that are ultimately unimportant or less impactful.
We should have stepped back and asked, “What are all the opportunities that could drive alumni engagement?” before narrowing our focus. This broader perspective allows for a more strategic and effective product comparison process.
Product teams rarely consider enough opportunities before jumping into solutions.
So, how can we avoid these common pitfalls and make more strategic product decisions? The answer lies in visualizing our thinking.
The Opportunity Solution Tree: A Framework for Effective Product Comparison
To overcome these challenges and facilitate better product decision-making, especially when you need to compare product options, I developed the Opportunity Solution Tree. This visual tool helps teams structure their thinking, explore opportunities systematically, and compare potential solutions effectively.
Anders Ericsson, in his book Peak, summarizes the key differences between experts and novices. He argues that experts utilize more sophisticated mental representations than novices.
He defines mental representations as:
Image alt text: Text quote from Anders Ericsson defining mental representations as pre-existing patterns of information used for effective and quick responses.
“… 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 primary benefit of sophisticated mental representations is their ability to enhance our understanding, interpretation, organization, and analysis of information. They are crucial for effective decision-making.
And he argues, “The key benefit of mental representations lies in how they help us deal with information: understanding and interpreting it, holding it in memory, organizing it, analyzing it, and making decisions with it.”
Isn’t this exactly what product teams need? A tool that enables us to understand, interpret, organize, and analyze the vast amounts of information we gather, ultimately leading to better product decisions and more effective product comparison?
Reflecting on my team’s challenges, I realized that both Seth and I brought different “mental representations” to the brainstorming session. I came armed with deep knowledge of our users from recent extensive user research. Seth, on the other hand, was excited about new technology, specifically the Google Maps API.
We each relied on our individual mental frameworks to make rapid judgments. However, effective product teams require a shared mental representation, combining their collective knowledge to make informed decisions, especially when they need to compare product strategies.
Product teams need to make decisions based on their combined knowledge.
Image alt text: Opportunity Solution Tree diagram visually representing the framework for product discovery, from Desired Outcome to Experiments, enabling systematic product comparison.
The Opportunity Solution Tree is my answer to the question: “How can we externalize our individual mental representations and align our teams around a shared understanding, especially when we need to compare product options?”
Let’s break down how to build and use this powerful tool.
Step 1: Define a Clear Desired Outcome
Every product initiative should start with a clearly defined desired outcome. What are we trying to achieve? This outcome serves as the root of our Opportunity Solution Tree, guiding all subsequent exploration and decision-making.
My team did have a clear desired outcome: to increase alumni engagement. However, as we saw, a clear outcome alone isn’t sufficient. We needed to delve deeper and ask, “What will increase engagement?” Before jumping to solutions, we should have first mapped out the opportunity space.
Opportunities can be viewed as user needs, pain points, or areas for improvement. But they can also encompass opportunities to delight users, replicate successes, or capitalize on emerging market trends. Thinking broadly about opportunities is crucial for effective product comparison.
Step 2: Explore Opportunities Through User Research
Image alt text: Text graphic stating “Opportunities Should Emerge from Generative Research” with an image of user interviews, highlighting the role of research in identifying and comparing product opportunities.
These opportunities should emerge from generative user research – primarily customer interviews and customer observations. To maintain a user-centered approach, I recommend framing opportunities in a way that reflects what a customer might actually say. This ensures that our product comparison is grounded in real user needs and perspectives.
Opportunities should emerge from generative research—customer interviews and customer observations.
My team had recently completed a series of alumni interviews, and we could have easily generated a list of opportunities based on direct user feedback:
We should have dedicated time to identify the opportunities we were uncovering in our customer interviews. This user-centric approach is essential for effective product comparison.
- “I get too much email.” – My initial opportunity focus.
- “I’m moving to a new city and want to know who lives there.” – Seth’s opportunity focus.
- “I need help finding a job.”
- “I want to stay connected to my alma mater.”
- “I want to know what my college friends are up to.”
- “I’m looking for something interesting to read / learn.”
- “I want to keep up with my school’s sports.”
- “I want to hire a recent grad.”
- “I’m willing to donate but want to know the impact.”
- “I’d like to give back to the community.”
- “I’d enjoy mentoring a student or recent grad.”
This list represents direct user feedback from our alumni interviews, forming the basis for product comparison.
Now what? Many teams would jump to prioritizing this list, asking, “Which of these opportunities is most important to address next?” However, directly comparing items on this list is problematic.
It’s illogical to compare an aspirational opportunity like “I’d like to give back to the community” with a more specific need like “I want to hire a recent grad.” The items aren’t directly comparable, making prioritization difficult. Furthermore, some items are not entirely distinct. “Hiring a recent grad” and “mentoring a student” are both ways alumni might “give back to the community.” We’d be comparing apples and oranges, hindering effective product comparison.
Don’t prioritize a list of unlike items that aren’t distinct from each other.
Step 3: Structure Opportunities for Effective Prioritization
Image alt text: Text graphic stating “An Opportunity Solution Tree Simplifies Prioritization” with a diagram illustrating grouped opportunities, emphasizing simplification for product comparison.
Grouping similar opportunities makes the prioritization process much more manageable and facilitates more meaningful product comparison. By clustering related items, we can create distinct categories that are easier to compare against each other. In our alumni example, grouping similar opportunities yields 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 the long, disparate list, we can now prioritize these three broader opportunity groups. Based on our research, the most prevalent theme was “I need help.”
Image alt text: Opportunity Solution Tree diagram with “I Need Help” opportunity highlighted, visually emphasizing the prioritization of key user needs for product comparison.
Now, notice where my “I get too much email” opportunity fits in. It falls under the “I need help” category, suggesting that users perceive spam as hindering their ability to get help effectively.
Suddenly, Seth’s idea starts to seem more relevant. While Google Maps directly addresses “I’m moving to a new city and want to know who lives there,” it also indirectly relates to “I need help” by facilitating connections within a local alumni network. This reframes the product comparison and highlights potential synergies.
If Seth and I had used this tree structure back then, we could have moved the conversation to a higher level. Instead of arguing about “spam” versus “geographic connection,” we could have focused on prioritizing the top-level opportunities. It would have been easier to agree that “I need help” was paramount for alumni. We could then have shifted our focus to prioritizing the sub-opportunities within “I need help,” leading to a more strategic product comparison.
Image alt text: Opportunity Solution Tree diagram showing the initial structure without highlighted opportunities, demonstrating the flexibility and iterative process of product comparison using the tree.
However, I would have still raised a concern. I would have argued that “I get too much email” is intrinsically linked to “I need help.” Connecting help-seekers with helpers is crucial, but if we overwhelm users with spam, they’ll be less inclined to offer or seek assistance.
This realization suggests that our initial tree structure wasn’t quite right. If two opportunities are inherently connected, they should be closer together in the tree to reflect that relationship. Let’s refine the structure for better product comparison.
Step 4: Refine the Opportunity Structure
There’s no single “correct” way to structure your Opportunity Solution Tree. The structure should reflect your user research findings and facilitate effective prioritization and product comparison.
First, I combined “I need help” and “I want to give back” into a broader opportunity: “I want to connect with other alumni.” This recognizes the reciprocal nature of community interaction – giving and receiving help are two sides of the same coin.
Underneath “I want to connect with other alumni,” we now have more specific sub-opportunities:
- “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 help and those offering it – closer together under a unifying opportunity. This facilitates a more balanced product comparison of different connection types (professional, location-based, etc.) without favoring one side of the market.
It also minimizes the initial disagreement between Seth and me. We could both agree on focusing on the “I want to connect with other alumni” branch. Our disagreement now narrows down to which sub-opportunity within this branch is most crucial. This makes it easier to use data to resolve our differences. We could, for instance, investigate: “How many alumni want to connect with people near them?” versus “How many alumni don’t know who to connect with?” Data-driven product comparison becomes more focused and effective.
Finding the optimal structure might require a few iterations. The key is to ensure it accurately reflects user insights and supports sound decision-making about prioritization and product comparison.
The structure of your opportunities should reflect what you are hearing in your customer interviews.
Remember, there are no definitive “right” or “wrong” answers here. Your tree structure will evolve as your team learns more about your users and refines its understanding of the opportunity landscape. The value of visually externalizing your thinking is that it helps your team resolve disagreements, align on a shared perspective, and make more informed choices when you compare product directions.
Once you’re satisfied with your opportunity structure, you’re ready to explore solutions.
Step 5: Generate Solutions for a Target Opportunity
Earlier, I mentioned that teams often don’t consider enough ideas, even though many feel they have too many. This seeming contradiction arises because teams often generate ideas across a broad spectrum of opportunities, rather than deeply exploring solutions for a specific opportunity.
When we brainstorm across multiple opportunities simultaneously, we generate a lot of first ideas, but we miss the creative depth and innovation that comes from focused ideation. This scattershot approach can lead to shallow products and hinders effective product comparison.
Image alt text: Diagram illustrating solutions scattered across multiple opportunities, visually representing the lack of focused ideation and hindering effective product comparison.
The true value of brainstorming lies in pushing past those initial, obvious ideas to unlock truly innovative solutions. By spreading our ideation efforts too thinly, we lose the creative benefits of focused brainstorming, and we end up with a plethora of solutions that are difficult to prioritize and compare meaningfully. Remember, it’s hard to prioritize a list of unlike things.
Step 6: Focus Ideation on a Single Target Opportunity
Instead of brainstorming across the entire opportunity tree, we should select a target opportunity – prioritized row by row – and then generate a wide range of solutions specifically for that opportunity.
We want to delve deep into a single opportunity, generating numerous potential solutions to address it. This sets up a “compare and contrast” decision for determining the best way to capitalize on that opportunity and allows for a more focused product comparison.
We aim to generate a wealth of ideas for solving a single opportunity. This focused approach is key for effective product comparison.
Generate ideas for one target opportunity rather than across opportunities.
After generating a diverse set of solutions, the next inclination might be to simply prioritize the list and start experimenting with the top idea. However, this leads us back to a “whether or not” decision: “Is our top idea good or not?” Instead, we want to maintain the “compare and contrast” approach. We should ask, “Which of these solutions looks most promising?” When faced with numerous ideas, I recommend two methods for effectively comparing and narrowing down solutions.
Step 7: Use Dot Voting to Narrow Down Solutions
Image alt text: Diagram illustrating dot voting for selecting top product ideas, visualizing a team using dot voting for efficient solution comparison.
First, use dot voting to whittle down a large list of solutions to a smaller, more manageable set of 3-5. Research indicates that groups are more adept at evaluating ideas than individuals, and dot voting provides a quick and democratic way to poll the team and identify the most promising solutions for further product comparison.
Step 8: Use Experiments to Compare and Contrast Top Solutions
Next, use experiments to determine which of the remaining 3-5 ideas is the most promising. This experimental phase is crucial for data-driven product comparison.
Use dot voting to whittle a list from lots to some. Use experiments to evaluate the smaller set.
A crucial point to emphasize: Most teams experiment to validate a single idea – a “whether or not” approach. Instead, I advocate using experiments to compare a set of good ideas, reinforcing the “compare and contrast” decision-making framework.
The most effective way to do this is to identify the key underlying assumption for each solution’s success and then design experiments to test those assumptions. This direct product comparison through experimentation provides valuable data for informed decision-making.
For example, if our top solution ideas for “I want to connect with people near me” are:
Run experiments to compare and contrast your top ideas.
- Recommending recipients based on location criteria.
- Auto-matching messages with recipients based on proximity.
- Sending messages to friends-of-friends in the same location.
The key assumptions and corresponding experiments could be:
- Assumption for Location Recommendations: “Help-seekers will trust recipient recommendations based on location.”
- Experiment: Prototype the user interface for location-based recommendations and gauge user response.
- Assumption for Auto-Matching: “We can accurately predict suitable message recipients based on location.”
- Experiment: Machine learning team conducts feasibility experiments on location-based auto-matching algorithms.
- Assumption for Friends-of-Friends: “Friends-of-friends are more likely to offer help based on geographic proximity.”
- Experiment: Analyze existing database data to determine if friends-of-friends geographically closer to message senders are more likely to respond to previous messages.
A common question is, “How do I know if my experiment results are ‘good’?” For example, is a 15% conversion rate sufficient? This is akin to asking, “Is Usain Bolt fast?” when he’s running alone. It’s difficult to assess in isolation. However, when you experiment with multiple solutions concurrently, you can ask, “Which of these solutions performs best based on the data?” This comparative approach, like comparing Usain Bolt to other runners, provides a clear and actionable answer.
Run experiments to choose amongst a set of solutions, not to evaluate a single solution.
The Benefits of Using an Opportunity Solution Tree
Image alt text: Text graphic summarizing the benefits of Opportunity Solution Trees: Navigate opinion battles, Frame decisions as compare and contrast, Create a discovery roadmap, highlighting advantages for product comparison.
If you, like me, want to involve your entire team in strategic product decisions, but find yourselves bogged down by opinion-based debates or encountering your own version of my Google Maps dilemma, I highly recommend adopting the Opportunity Solution Tree. This tool provides a structured framework for effective product comparison.
Taking the time to visually map out your thinking using an Opportunity Solution Tree helps your team identify and correct common critical thinking errors. It prevents “whether or not” decisions and promotes “compare and contrast” evaluations, leading to more strategic and data-informed product comparison.
The tree also functions as a dynamic discovery roadmap, fostering team alignment around a shared understanding of the opportunity space and potential pathways to achieving your desired outcome. It facilitates communication with leadership and stakeholders, showcasing your team’s learning and strategic direction.
Your opportunity solution tree serves as a discovery roadmap—helping your team align and communicate.
Getting Started with Your Own Opportunity Solution Tree
Use these practical tips to begin building your own Opportunity Solution Tree and improve your team’s ability to compare product options effectively:
Use these tips to get started with your own opportunity solution tree.
- Start with a Clear Desired Outcome. Define the overarching goal you’re striving to achieve.
- Map the Opportunity Space. Explore opportunities through generative user research: customer interviews and observations.
- Experiment with Opportunity Structures. Refine the tree structure to best reflect user insights and facilitate effective prioritization and product comparison.
- Prioritize Opportunities Row by Row. Select a target opportunity to focus on.
- Focus Ideation on the Target Opportunity. Generate a wide range of solutions specifically for the chosen opportunity.
- Use Experiments to Compare Solutions. Test and compare your top solution ideas using data-driven experiments.
Image alt text: Text graphic “Let me know how it goes! Email or Tweet me.” encouraging user feedback on Opportunity Solution Tree implementation for product comparison improvement.
If you build your own Opportunity Solution Tree, I’d love to hear about your experience. Please reach out via email or Twitter. For further reading on the research referenced, I’ve compiled a list of sources for you here.
Thank you!
