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Yardstick (YC W22) – Data-Driven Startup Valuation, KPI Benchmarking

Hacker News

Yardstick (YC W22) – Data-Driven Startup Valuation, KPI Benchmarking

Hello, HN community! We're Ravi and Vijay, data enthusiasts and startup founders who've faced the significant challenge of quantifying startup success. Through our experiences in building and growing companies, we've recognized a glaring need in the market for transparent, accessible benchmarking and valuation metrics—tools that rely on data rather than just intuition or incomplete data. Here is a quick demo - https://app.storylane.io/share/916eqxtsotpo What Yardstick Offers: Yardstick delivers comprehensive tools for accurately benchmarking startup performance and estimating valuations. Our platform utilizes an extensive dataset of startup performance metrics, providing growth rate percentiles to precisely position startups against a broad database. Moreover, Yardstick computes valuation estimates through sophisticated models that analyze KPIs in relation to valuation data and qualitative insights from hundreds of startups. The Problem and Our Journey: Navigating the vibrant startup ecosystem, we found that evaluating these ventures has traditionally been more art than science. Our experience in assessing hundreds of early-stage startups for equity and debt investments revealed the difficulty of understanding their market standing without resorting to costly market research or external consultants. This challenge inspired the creation of Yardstick. Our Solution: Yardstick seamlessly integrates into the workflows of fintechs, investors, and entrepreneurs wanting to underwrite a startup’s performance. Our solution offers not just KPI benchmarking but also insights into current valuations. With both an API and a user-friendly web application, Yardstick serves both technical and non-technical users, ensuring comprehensive accessibility. Give it a try: We are thrilled to introduce Yardstick to the HN community and are eager for your feedback. Whether you are a fintech looking to perform KPI benchmarking on potential clients, an investor in need of data-driven tools, or a startup founder trying to understand where your startup stacks up against peers or what should be the latest valuation based on current traction, we invite you to try out Yardstick - https://ventureinsights.ai/yardstick/ (without a need to sign up!) and share your insights. For those interested in integrating our tools directly, check out our API documentation - https://documenter.getpostman.com/view/32615745/2sA35G3MMX We look forward to your thoughts and discussions! Thank you, Ravi and Vijay

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Actual performance

3points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
96%96% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: model, user, models · Missing: mac, agents, macos
80%80% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, friendly, users · Missing: plus, intuitive, reviews
46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: lua, io · Missing: https docs, excited, just released
45%45% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: entrepreneurs, users · Missing: mobile apps, ios, personal
28%28% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: growth · Missing: arr, mrr, revenue
9%9% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: introduce · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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