On
Onva – how do your customers feel about your product?
Hacker NewsOnva – how do your customers feel about your product?
Share cardActual performance
1points
Did not reach leaderboard
Launch Intel predictions
Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto41%41% predicted probability of success on BetaList, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released41%41% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting28%28% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue28%28% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive18%18% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Correct prediction on native model
Similar products
Sh
ShipIT – From Customers to Product43%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
ShipIT – From Customers to Product
Hacker News2Novaseed21%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Find customers already asking for your product
Indie HackersKe
Keep a Pulse On How Your Customers Feel39%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Keep a Pulse On How Your Customers Feel
Hacker News2Ahmedabad Call Girls13%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
You are never going to feel lonely and exhausted while you..
Indie Hackerscommitment-full-time
btw
Delight your customers
BetaListId
Identifying, Segmenting and Contacting our Customers51%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Identifying, Segmenting and Contacting our Customers
Hacker News11Ou
Our first three featured customers57%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Our first three featured customers
Hacker News3
Overlead75%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Find customers who are literally asking for your product
Product Hunt+146MarketingAu
Auto-ship product to your customers49%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Auto-ship product to your customers
Hacker News5Sc
Scholtz – Find customers and users that want your product51%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Scholtz – Find customers and users that want your product
Hacker News5