How to Validate Your Startup Idea Before Building Anything? | VC Jobs
Don’t Price Based on Costs, Frameworks For Evaluating an AI Startups’ Tech & Paul Graham's The Fatal Pinch....
👋Hey Sahil here! Welcome to this bi-weekly venture curator newsletter. Each week, I tackle questions about building products, startups, growth, and venture capital! In today’s newsletter, we dive into -
Deep Dive: How to Validate Your Startup Idea Before Building Anything?
Quick Dive:
Investors Use These Two Frameworks For Evaluating an AI Startups’ Tech Stack.
Don’t Price Based on Costs, Why?
If you've got less than a year's cash left, you need to read The Fatal Pinch..
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TODAY’S DEEP DIVE
How to Validate Your Startup Idea Before Building Anything?
Did you know that over 35% of startup failures happen because they launch products that ultimately have no market need? This is an avoidable risk. It’s not just about talking to users and gathering insights—it’s more than that. Every product development journey follows a familiar pattern:
Day 1: 'This is going to be huge.'
Day 90: 'This is terrible...'
Many founders today focus solely on building products and testing them. But there are other, more effective ways to validate your business ideas with the market.
Market validation is the process of confirming that people will buy your product or service.
Doing this early on can save you from wasting time and resources on something that won’t work. It also increases your chances of attracting investor interest.
Once you’ve validated your idea, you’ll have a clearer understanding of how your product can meet your customers’ needs. The insights you gain will allow you to create an offering that not only solves their problems but also converts them into paying customers.
How To Test Your Business Ideas With Low-To-No Budget
You could spend tens of thousands of dollars on detailed market surveys or focus groups, but assuming you’re not a multinational conglomerate you’ll probably be wanting to validate your market more cost-effectively. Luckily, there are some very effective ways to do this. Here are 4 ways you can do market validation for less than:
1) Using customer interviews for market validation
First, identify your ideal customer. You want to find the person within an organization who will not only use the tool you’re promising but who also has the authority to purchase tools for their function. That way, they’ll not only give you feedback on whether your product is useful, but they also be able to say whether it’s likely something that they would buy and roll out more broadly.
Next, find where these users like to hang out. Often, that will be LinkedIn. But you can also attend Meetups, where like-minded users gather. For example, if I was looking to build Product road mapping software, I’d linger around a Product Manager’s Meetup, looking for potential volunteers for customer interviews. We’ve covered more ways to find your target customers (aka early adopters).
Once you’ve found your target customer, you’ll want to interview in a way that reduces bias and answers two questions:
Is this a real problem?
Is there real demand for my solution?
Here are some questions you can ask:
Is this a problem you regularly encounter yourself?
How much of a problem is it?
How do you work around the problem at the moment?
Would you pay to solve the problem?
These questions should be enough to get you started and there are plenty of great articles on the web on how to conduct these interviews.
Pay close attention to their answer to the third question. Are they using other products? Do those products fully solve the problem, or are they cobbling together multiple tools? Often, people create complex workarounds using things like Google Sheets and Zapier integrations. These solutions are often fragile and only partially effective.
Lastly, make sure there's real interest in paying for a solution. Some products fail because, even though they solve a problem, customers either don't value the solution enough to pay for it or simply can't afford it.
You might discover your target market is cash-strapped, or that the businesses you're targeting are locked into contracts with competitors.
2) Running a Crowdfunding Campaign
Platforms like Kickstarter offer a great way to showcase your product ideas through crowdfunding campaigns. One of the best things about these platforms is that you don't need a working prototype or model to get started. You're presenting your idea, and if consumers like what they see, they can "back" it - essentially pre-paying for your product.
When people back your project, they're not just giving you money. They're joining a community of early supporters for your product. To encourage higher contributions, you can offer additional, exclusive benefits to your backers.
While the funds are helpful, the real value of these platforms lies in their target-based structure. Take Kickstarter, for example. You set a target amount of "investment" from backers and a time limit to reach it. If you don't hit your goal within that timeframe, nobody gets charged, and you don't receive any money.
This all-or-nothing approach is incredibly effective for determining whether your idea can build a critical mass of support. If you meet your goal, it's a strong signal of market interest. If you fall short, it might indicate that you need to rejig your idea to appeal to a broader audience.
3) Be a reseller first
If you're thinking about launching a consumer product business, there's a clever strategy you might want to consider: start by reselling existing products. This approach can give you valuable firsthand experience of how your target market responds before you dive in headfirst.
Take Nick Swinmurn, the founder of Zappos.com, for example. Back in the early days of e-commerce, when everyone was still figuring out what would sell online, Swinmurn had a hunch about shoes. But instead of immediately investing in inventory and warehousing, he got creative.
Swinmurn would visit local shoe stores, snap pictures of their products, and post them online. When an order came in, he'd dash back to the store, buy the shoes, and ship them out. It was a bit unconventional, sure, but it allowed him to test his idea without any upfront investment.
This scrappy approach gave Swinmurn direct insight into whether there was a real demand for an online shoe store. Remember, this was during the early dotcom boom when online retail was still a big question mark. By acting as a middleman, Swinmurn found a low-risk way to gain experience and test the market directly.
The beauty of this strategy is its simplicity and low barrier to entry. You're essentially piggybacking on existing products to see how your potential customers react. It's a way to validate your business idea and learn about your market without the hefty costs and risks associated with launching a full-fledged operation.
Of course, this method isn't just about proving demand. It also gives you a chance to work out kinks in your process, understand customer preferences, and build relationships with suppliers.
4) Set up a ghost store or smoke test
This technique works particularly for direct-to-consumer (D2C) companies but can be adapted for B2B as well. First, create a landing page and checkout flow for your potential product. If you're selling a physical product, the e-commerce-specific platform, Shopify, will be the best website builder to do this. If you’re selling software, you may want to use a more general website builder like Webflow or WordPress.
Next, you'll need images of your product. If you're not a designer or don’t have a product to photograph, you can get product images designed through a platform like Upwork. There you’ll find a range of UX and 3D designers who can quickly mock up a prototype of your software product or physical good.
Next, set up some Google or Facebook ads and start sending people to your store. Make sure you target high-intent keywords. For example, if you’re selling candles for men, set up ad campaigns for “candles for men”, and “where to buy candles for men” instead of “male scent diffusers”.
This way, you’re more likely to reach buyers as opposed to window shoppers. If people purchase the dummy product, send them an email saying "Sorry, we've sold out". Refund their money and offer them a discount when your stock has been replenished.
Run this test with 100-1000 people and then work out if there is enough demand for your product to sustain a business. If it costs you $300 to acquire your customers, but you only made $10 in sales, then there might not be a market for your product.
If you spent $300 on ads and made $1000 in sales, then there is a demand for your product and your market is validated! If it’s somewhere in between, you’re going to have to use your discretion and other data points to validate whether there is market demand.
Validate Your Startup Idea Before Building Anything
Once you’ve launched into product development, there’s no turning back. You’re then committed. Making changes becomes more expensive, and your first wave of customers will have their expectations set. Investors, too, will be looking closely at how the product pans out.
You can make changes to a product once you’ve pulled the trigger on its development, and indeed an agile, iterative approach to development is modern best practice. However, to completely pivot after you discover your initial idea has fallen flat can easily become more expensive than starting anew. Startups simply don’t have the time and resources available to waste on false starts this way.
Market validation can’t give you the perfect guarantee that your business idea will be successful. Even if all the indicators are that there’s a viable market for your business, there are any number of reasons that it won’t translate into success.
However, by undertaking a robust market validation process, you maximize the chance of success. Many of the most successful businesses had a hypothesis, only to find through market validation that they were looking at the idea from the wrong angle.
If they had launched with the initial idea, their business would have struggled. Thanks to market validation they were able to identify where they needed to pivot, and quickly do so before the product had even been built. They would then go on to enjoy an enormously healthy launch for the business.
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QUICK DIVES
1. Investors Use These Two Frameworks For Evaluating an AI Startups’ Tech Stack
Many founders want to know how investors evaluate AI startups’ tech stack. So in this quick dive, I will share two frameworks that GP shared with me.
Before jumping into the frameworks, let’s first assess the basic factors that come into play when assessing data quality. Four factors generally play an important role in assessing its quality…
Relevance: Data must align with the problem the AI model is solving.
Risk: Irrelevant data leads to inaccurate or useless models.
Accuracy: Data must be correct and precise.
Risk: Inaccurate data can cause misdiagnoses or wrong decisions.
Coverage: Data should be comprehensive, covering all necessary aspects.
Risk: Incomplete data results in limited model capabilities.
Bias: Data should be collected without prejudice.
Risk: Biased data leads to discriminatory model outputs.
Framework 1: Tech stack pyramid for data generation
To avoid investment in ineffectual AI startups, there is a need to first evaluate the processes behind the data. Picturing a company’s tech stack as a pyramid is a good place to start, where the foundational tiers tend to have the biggest impact on the predictive outcome. Without this solid base, even the best data analysis and machine learning models face significant constraints.
Here are some basic questions that a VC might initially ask to figure out if a startup’s data generation process can create usable results for AI:
Is data capture automated to enable scale-up?
Is the data stored in secure cloud environments with automated backups?
How is access to infrastructure and relevant compute resources managed and guaranteed?
Are data processing pipelines fully automated, with rigorous data quality controls implemented to limit pollution from contaminated data points?
Is the data readily accessible across the company to empower ML model-building and data-driven decisions?
How is data governance implemented?
Is there a data management strategy in place?
Are data and ML model versions tracked and accessible, ensuring ML models are always working on the latest data version
Receiving robust answers to these questions can help determine a company’s grasp of the underpinning principles of its data pipelines. This understanding, in turn, will help gauge the quality of the model’s output.
Framework 2: The five V’s of data quality
Once a company’s tech stack has been deemed suitable for AI, there is also a need to carefully consider the quality of the resulting data being used to train its models. A common framework used to capture the classification of data quality is the five V’s of data quality. They represent five key dimensions of data quality that VCs should consider when evaluating AI startups:
Veracity: The data must be accurate and truthful.
Variety: The data must be diverse and representative of the real world.
Volume: The data must be large enough to train the AI model effectively.
Velocity: The data must be updated frequently to reflect changes in the world.
Value: The data must be useful for the AI model to learn from.
Here are some introductory questions to help evaluate a company’s data for the five V’s:
Does the startup have a good hypothesis about which data they need to create to build a differentiated capability or useful model?
What data do they collect?
Do they also collect any relevant metadata?
How do they ensure the correctness and consistency of the data they collect?
How does the startup plan to deal with data bias?
Do they collect multiple examples for the same question or experiment?
How useful is this data for the product they are building?
What’s the rationale behind collecting this data?
Do they have evidence that their predictions improve by collecting and using this data? If yes, how does the data amount correlate with prediction improvement?
How easy is it for a competitor to collect the same data? How long would it take and how much would it cost for them to do so?
Specifically for a biotech, how well does the proxy they are predicting correlate with a clinically relevant endpoint? Is there evidence for this?
What is the startup’s plan for ensuring the quality of its data over time?
How does the startup plan to protect its data from unauthorized access?
How does the startup plan to comply with data privacy regulations?
By carefully considering the five V’s of data quality, VCs can make sure they are investing in AI startups that have the data they need to succeed. If the startup can answer the above questions convincingly and their data scores highly in the five dimensions, it is a good sign that they are serious about data quality and are properly equipped to apply their AI models.
2. Don’t Price Based on Costs, Why?
Founders often struggle with pricing their products correctly early on. Many make the mistake of setting prices based on their costs, especially in non-software startups. While this might seem logical, it's not the best approach. Here's why you should consider "unreasonable" pricing instead:
The Problem with Cost-Based Pricing
It doesn't account for the value you're providing to customers.
It can limit your growth potential and ability to invest in improving your product.
It may attract price-sensitive customers who aren't your ideal target market.
The Benefits of "Unreasonable" Pricing
Identifying Your Ideal Customer Profile (ICP)
High initial prices help you quickly identify customers who feel the pain point most acutely.
These customers are willing to pay more for a solution, even if it's not perfect yet.
Validating Market Demand
If people are willing to pay a premium, it confirms there's a strong need for your product.
This validation can save weeks of experiments and help you focus on the right target audience.
Accelerating Product Development
Higher initial revenue allows you to invest more in improving your product faster.
You can build specifically for your ICP, ensuring product-market fit.
Real-World Example
I see a lot of founders launch a website, with:
A logo that was just an emoji
An onboarding flow using Typeform
A database in Airtable
Also, set the initial price on the higher end. Despite this - they have seen good outcomes of getting a few people onboard 5-6, why? They felt the pain point so strongly that they wanted it solved at all costs.
The Inverse Relationship
There's an inverse relationship between how painful a problem is and how "perfect" the product needs to be:
More painful problem = less perfect product + higher willingness to pay
Less painful problem = needs to be perfect + price sensitivity
When to Launch
Once your product is 70-80% ready for your ICP, they will likely convert. If not, it may indicate that the problem isn't worth investing more time into, as the product will be hard to grow.
Long-Term Strategy
While starting with "unreasonable" pricing can be beneficial, it's important to note that this approach isn't permanent. As you grow and expand your market, you may need to adjust your pricing strategy. However, the initial high pricing helps validate the market, identify your ICP, and provide capital leverage for faster growth.
Remember, don't let perfect be the enemy of good. If you're solving a truly painful problem, your early adopters will be willing to pay a premium for an imperfect solution.
Even in the previous newsletter, I have shared 5 frameworks that can help you to find out if your pricing strategy working.
3. If you've got less than a year's cash left, you need to read this now
If you're a founder with less than ~12 months of runway going into 2024, you have to re-read Paul Graham’s The Fatal Pinch. Assume you will NOT get a bridge round, and figure out what other options you have ASAP.
Let me share a key point that every founder should consider while considering their burn rate -
“The "fatal pinch" that happens to a lot of startups. It's when they've got some cash in the bank, but they're bleeding money every month and not growing. They think they'll be fine 'cause they'll just raise more money from investors. Big mistake.
The problem is, that founders often kid themselves about how easy it'll be to get more funding. It's way harder the second time around because:
The company's burning through cash faster now
Investors expect a lot more from companies that have already raised money
The startup is starting to look like a failure
This whole situation is like a nasty feedback loop. Founders think they'll easily raise more money, so they don't try hard enough to become profitable, which makes it even harder to raise money.
To avoid this mess, YC tells founders to act like the money they raise is the last they'll ever get. If you're already in the fatal pinch, you've got three options: shut down, make more money, or spend less (which usually means firing people).
If you decide to keep going, you need to focus on making more money ASAP. This might mean changing what you're selling or doing more consulting-type work. Just be careful not to slide too far into pure consulting.
The good news is, that lots of successful startups have had near-death experiences and survived. You just gotta realize you're in trouble before it's too late. If you're in the fatal pinch, you are.”
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THIS WEEK’S NEWS RECAP
Major News In VC, Startup Funding & Tech
Ex-OpenAI chief scientist Ilya Sutskever’s startup Safe Superintelligence (SSI) has raised over $1 billion from investors including NFDG, a16z, Sequoia, and DST Global, valuing the startup at $5 billion. (More Here)
Andreessen Horowitz (a16z) has closed its Miami Beach office due to disuse, just two years into a five-year lease. (More Here)
With $50M in new funding, You.com thinks its AI can beat Google. (More Here)
OpenAI now has over 1 million paying users for its ChatGPT Enterprise, Team, and Edu products, up from 600,000 in April. (More Here)
Salesforce has acquired Own Company, a data management and protection solutions provider, for $1.9 billion in cash. (More Here)
Elon Musk's company, xAI, has brought an AI training cluster named Colossus online, claiming it is the world's most powerful AI training system. (More Here)
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✍️Written By Sahil R | Venture Crew Team
Nice read. Thanks for all the insights.