If there’s such a thing as unconscious bias for startups, then I had it.
I thought I knew how to judge good ideas from bad ideas. Then I spent some time as an EIR working side by side with the talented Frontline team in London. Seeing how Will and team went about their due diligence was an eye-opener. It turns out I had a lot to learn.
VC’s are professional pickers. Having good judgement is probably the most important part of their job. They say no to most deals and invest in just a handful of startups each year. Those they commit to need to show power law returns in order for it to make a dent in their fund. So it’s fairly binary. Good decision = great return. Bad decision = little or no return. Backing the winner is what matters. Everything else is secondary.
During my time at Frontline, I probably went through 15 startup ideas. 10 of them were really bad, 3 were just bad, 1 was OK and 1 had actual potential. In order to put some structure into these ideas, I wanted to find a better way to compare and contrast them against one another. I also wanted to see what the relative pros and cons were within each idea.
The inspiration for the approach came from a podcast between Shane Parrish and the Nobel laureate Daniel Kahneman. During the interview, they discuss the role that intuition plays when making key decisions.
What gets in the way of clear thinking is that we have intuitive views of almost everything. So as soon as you present a problem to me, I have some ready made answer. What gets in the way of clear thinking are those ready made answers, and we can’t help but have them.
The lesson being that if you want to make better decisions, you should try to marry your intuition with thoughtful analysis. So I created a basic algorithm to run product ideas through — it’s called the Opportunity Scoring model.
The goal was to objectively rate different dimensions for each idea while still leaving room to follow your nose. Having now applied the model to multiple concepts in the Product Buffs course, it’s been proven as an effective way to stress test ideas upfront. This has saved teams from going down the wrong path much earlier in the product discovery cycle.
In keeping with the theme of my last post, I’m going to use a real example for a product idea that I have called ARRO. This is a digital goal planning app aimed at independent workers who could benefit from group support. It scored 79 out of a possible 100 points.
You can access the template below and make your own copy — feel free to play around with it and use the sheet for your own product discovery initiatives 👍
Opportunity Scoring Example 🔮
You can access and copy the original sheet 👉👉 here 👈👈
How it Works ⚙️
The sheet is split into four main sections:
Macro Trends - what outside influences could help or hinder the concept?
User/Product Insights - what data leads us to believe that our proposition will be fit for purpose in relation to our target market?
Company Dynamics - what are the relative company strengths which apply?
Intuition Score - based on all of the above, how would we intuitively rate it?
1. Macro Trends 🌍
CHANGE EVENT CONFIDENCE
Ranked 0 - 10, Weighting 1 (0 = no change event, present focus, 10 = clear change event, future focused)
Is there a change event that facilitates the insight (tech or adoption inflections)? Are you inventing the future or just finding gaps in the present?
In short, this input boils down to a question of timing.
Are you too early or too late? Or can you capitalise on a growing trend at just the right time? Mike Maples from Floodgate talks about ‘living in the future’ and how these change events create the incubation space needed for a new product to survive.
There’s two types of change events:
Technology inflections — for example, around the time that Mike invested in Lyft, GPS locators in cell phones were good enough for ride-sharing.
Adoption inflections — also around the time of Mike’s Lyft investment, it had reached a point where enough people had smartphones so that anyone who wanted to drive/ride was able to do so.
A larger change event typically provides the type of tailwind needed to help propel a product forward — stasis being the enemy for high growth startups.
This post explains what a change event is in more detail.
Ranked 0 - 10, Weighting 1 (0 = small market, 10 = huge market)
How big is the total addressable market?
Market sizing goes wrong when you pick the biggest number you can find that justifies a huge market size. I’ve definitely been guilty of this — Soundwave (my previous startup) literally got called out on Crunchbase for making this mistake 😬
This video by Rob Moffat, partner at Balderton Capital, is a great primer on how to do market sizing properly. Instead of focusing on top down market sizing, you can apply a more grounded approach that reflects reality.
A) What’s the size of the largest incumbent in your market? Look at their user base, revenues, gross margins and other factors. Some industries are huge (eg hairdressing) but they don’t have any large companies.
B) What’s the bottoms up market size? This is basically the current pricing model applied to the number of people in your target market segment.
C) Is there some latent demand? Can your product tap into some market that is only a fraction of the size that it could be someday (eg Uber which grew beyond the taxi market).
D) What adjacent markets could you tap into? Could you increase your market share by moving into a new market that compliments your product strategy (eg Spotify moving from music streaming into a bigger audio market including podcasts).
MARKET GROWTH RATE
Ranked 0 - 10, Weighting 1 (0 = low growth, 10 = high growth)
What's the compound annual growth rate of the chosen market? How does this compare to other markets you could invest in?
The market growth rate is an important factor when evaluating the viability of a new or existing product idea. By assessing the current rate of growth and comparing it to other industries, you can make informed decisions about market potential.
Usually a high growth rate means low saturation and high demand. A low growth rate could suggest that the market is overly saturated or lacks enough demand.
When you consider different compound annual growth rates (CAGR), the gap can be startling. A market growing at a CAGR of 5% will be 1.28x bigger after 5 years — whereas a market growing at a CAGR of 25% will be 3.05x bigger after 5 years.
The size of the market matters but so does the rate of growth for that market. As Einstein pointed out, compound growth is the eighth wonder of the world.
Ranked 0 - 10, Weighting 1 (0 = low market/model fit, 10 = high market/model fit)
Does this pass the market>model 100m ARR test? How favourable is the business model?
This input is a way to measure how much value you could potentially capture from the market you’re going after. It combines bottoms up pricing with the TAM.
Brian Balfour from Reforge wrote a helpful post on market/model fit. The methodology is simple enough:
Take the average annual revenue per customer/user, multiply it by total number of customers/users in your target market, then multiply that by the percentage you think you can capture. That should equal or be greater than $100M.
So the formula is -> ARPU x Total Customers In Market x % You Think You Can Capture >= $100M.
Not all new product ideas set out to capture $100m. Even still, using this approach you’ll be able to gauge your market model fit. It will force you to think about your pricing strategy and how many customers you’ll need to build a sustainable business.
2. User/Product Insights 📊
Ranked 0 - 10, Weighting 1 (0 = low confidence, 10 = high confidence)
Is your insight actually earned? Is it non-consensus or obvious? How well understood is the actual problem? Is this experienced first hand?
This input relates to the confidence you have in the actual insight itself.
Typically, your confidence in the insight should increase with the amount of domain expertise you have in that problem space. Balaji Srinivasan calls this the idea maze:
A good founder is capable of anticipating which turns lead to treasure and which lead to certain death. A bad founder is just running to the entrance of (say) the “movies/music/filesharing/P2P” maze or the “photosharing” maze without any sense for the history of the industry, the players in the maze, the casualties of the past, and the technologies that are likely to move walls and change assumptions.
There’s generally no shortcut to expertise. If you do your time and learn about a space over a sustained period of time, you’ll be well positioned to discover problems that need solving.
Ranked 0 - 10, Weighting 1 (0 = poor distribution, 10 = excellent distribution)
How strong are the distribution channels for this project? How favourable is the go-to-market strategy? How expensive would that strategy be?
Poor distribution is one the biggest startup killers out there.
I always ask founders and product teams what their distribution strategy is with a new proposition. The worst answer is that the product is so good it will grow by itself.
The best answer explains how the product has been built from first principles to lean into a free or unsaturated distribution channel. Products should match distribution channels. You can’t reverse engineer it after it’s built — channels won’t adapt to a new product. It sounds obvious but I rarely see product teams thinking deeply about distribution early on. They want to launch an MVP and then scale up.
The best products are very clear on their distribution strategy before scaling. Hopin is a great example of this. The founder and CEO Johnny Boufarhat knew that his product was going to go viral before officially launching it. As he confidently exclaimed in an interview with Nathan Latka:
It would be hard for us not to be one of the fastest growing companies in the world
He knew that roughly 3% of all event attendees would become hosts themselves and then create new events as customers. So he had a clear viral loop and that flywheel drove the majority of early paying customers.
Distribution is as important as product and that’s why I’ve weighted it accordingly.
EASE TO BUILD
Ranked 0 - 10, Weighting 1 (0 = very hard to build, 10 = very easy to build)
How easy would it be to build V1? Are there many dependencies that might stop you from building this? Is the team a good fit for the tech requirements?
This input basically can be distilled down into tech feasibility.
Given what you know, how hard would it be to get the MVP out the door.
If you’re building a standard Jamstack web app, maybe the ease to build is high. On the other hand, if you need to create your own proprietary ML recommendation system, this complexity should factor into the overall product idea score.
Ease to build doesn’t just refer to how straightforward the tech stack is either. There might be dependencies from other teams who are going to act as a bottleneck and slow you down. You should factor this into the equation too.
Ranked 0 - 10, Weighting 0.5 (0 = poor unit economics, 10 = excellent unit economics)
How strong are the unit economics for this proposition? What are the margins like? Would cashflow work in your favour?
This really relates to profitability. If you’ve got great unit economics, you can make more money as you scale. Getting paid in advance also helps a lot.
You can do some quick back of the envelope calculations to make sure that you’re going into a space with solid gross margins. SaaS platforms can have 70-90% gross margins. eCommerce businesses will usually have gross margins of 20-50%. Marketplaces typically work on a take rate.
If you look at the types of businesses on MicroAcquire, you’ll get a good sense for how the profitability changes. If you look to sell your business down the road, the standard tech valuation model is ~3-5x your annual profit. Revenue is vanity, profit is sanity.
It might seem premature to think about unit economics at this point but ignoring it can be fatal. As Peter Thiel called out in Zero to One, you want your customer acquisition cost (CAC) to be significantly lower than your customer life-time value (LTV). Otherwise, you risk falling into the ‘Dead Zone’ where you can’t acquire new customers profitably. He explains a simplified version as follows:
For a product priced around $1,000, there might be no good distribution channel to reach the small businesses that might buy it. Even if you have a clear value proposition, how do you get people to hear it? The product needs a personal sales effort, but at that price point, you simply don’t have the resources to send an actual person to talk to every prospective customer.
3. Company Dynamics 🏣
Ranked 0 - 10, Weighting 0.5 (0 = poor company fit, 10 = excellent company fit)
How well does this opportunity align with the founder or company? Is this fit authentic? Does the company have brand permission to move into the space?
Each founder and company will have a completely unique set of skills and merits.
Charlie Munger of Berkshire Hathaway talks a lot about his ‘Circle of Competence’:
You have to figure out what your own aptitudes are. If you play games where other people have the aptitudes and you don’t, you’re going to lose. And that’s as close to certain as any prediction that you can make. You have to figure out where you’ve got an edge. And you’ve got to play within your own circle of competence.
This means that any new product you set out to develop should leverage your specialised knowledge. If you stretch too far, you’re unlikely to succeed. Think of the countless record labels that tried and failed to build digital music libraries. Creating software was too far from their circle of competence. It took a company like Apple to come along and solve the problem.
Nike is a great example of a company that continues to innovate from its core. They’ve successfully built sizeable businesses in multiple sports markets — from jogging to golf to tennis to basketball. They use a repeatable formula each time 1) establish a leading position in athletic shoes in the target market 2) launch a clothing line endorsed by the sport’s top athletes 3) use that traction to move into accessories and other complimentary products. The alignment is high and the results show it.
Ranked -5 to +5, Weighting 0.5 (-5 = lots of competition, +5 = little competition)
How much competition is there in this space? How big are the competitors? Can the incumbents be dislodged or is it winner takes all?
This score addresses the competitive landscape for any new idea. No product exists in a vacuum. Customers will have certain ‘jobs’ that they hire your product for. If you do your job right, they’ll be switching from a direct or indirect competitor to your proposition.
Competition in itself is not a bad thing — it proves that there’s an existing market. All competition isn’t the same though. If you’re building a search engine then you should recognise that it’s a winner takes all market and Google will probably eat your lunch. If you’re building a niche indie SaaS tool for a specific segment, there’s probably room for multiple players.
It pays to work out the competitive risk before you launch into a market.
Ranked -5 to +5, Weighting 0.5 (-5 = negative impact, +5 = positive impact)
Would you be proud of the impact that your product makes on the world? Does it align with your values? How would society benefit?
This input into the algorithm is the ‘feel good’ one. It probably won’t impact your financial bottom line but it may help you stay the course when the going gets tough.
It’s a fairly well known startup truism that founders should be missionaries, not mercenaries. If you don’t love what you do, it shows. You’ll find it harder to sell your vision to others. Hiring will go more slowly. You’ll lose enthusiasm more quickly. People aren’t stupid, if you’re in it for the money, they’ll figure that out.
One of the easiest ways to fall in love with what you do is to build something that lots of other people need, which actually improves their lives. So by default, if you focus on an area with a high impact, you’ll be more easily motivated to do good work.
Think about your triple bottom line — what’s the potential social, environmental and financial impact of your idea? Many of the societal problems today have come about because companies chose to ignore negative externalities that wouldn’t show up on their P&L sheet.
Life is too short to build another gambling app. We need more builders who improve the world.
4. Intuition Score 🤔
Ranked 0 - 10, Weighting 1.5 (0 = feels wrong, 10 = feels perfect)
Given the above information, how would you rate the overall opportunity? Can you really see this being a viable product/business? Do you truly believe in it?
The last input in the scoring algorithm actually has the highest weight attached to it. That’s because no amount of data can completely replace our gut feeling about an idea. There’s a vast amount of genius hidden in the subconscious brain.
Logic is how the mind knows reality, intuition is how the mind experiences reality. Osho summarises this well in his book ‘Intuition: Knowing Beyond Logic’
The very word intuition has to be understood. You know the word tuition—tuition comes from outside, somebody teaches you, the tutor. Intuition means something that arises within your being; it is your potential, that’s why it is called intuition.
If you’re working as a team, you can ask everyone for their individual intuition score and then assign an average to that. If you’re building out the model yourself, then leave this input until the very end and put in a score that reflects your honest feelings. You’ll be surprised how often this matches the analytical score.
Final Thoughts 🎬
Some critics may be right to point out that analysis paralysis and false precision are real problems. There’s times when you see an opportunity and just know that you need to move on it. I’m in all favour for following your instincts when the signal is that strong.
A lot of times though, the signal is lukewarm at best. And even if you think the idea is a sure thing, running it through this framework will only help. Think of it as a quantified business plan that’s actually useful. You can use it to compare and contrast multiple product ideas against one another. You can falsify your assumptions. You can share it with others to find blindspots in your decision making. It’s better to run a pre-mortem than a post-mortem.
If investing an hour in the model could save you years of your time, it seems like a decent tradeoff. My first two startups were complete flops. In retrospect the market was non-existent for #1 and we were way too early for #2. So in all, I probably spent 3+ years working on terrible ideas — putting my heart and soul into ventures that went nowhere. Startup limbo is a lonely place.
So my hope is that the Opportunity Scoring model might save some other founders and product teams from going after the wrong opportunity. If you’d like to take the model and try if for yourself, just copy it here. If you’ve got any questions or feedback, please feel free to reach out 🤙