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AdoPilot – Your Facebook ads on autopilot (text post)

Hacker News

AdoPilot – Your Facebook ads on autopilot (text post)

Hi everyone, Review here: http://www.adopilot.info Like any other field I believe that the numbers game of Facebook advertising is best left to algorithms. Yet very few people rely on Ad Automation for managing budgets, ROI, ROAS etc. In the dropshipping industry, people often use intuition to judge how much an ad should be scaled to. So, beginners often scale an ad by 10x, and see their CPC skyrocket by 5-10x. Frightened, they cut back their ad spend and conclude that FB ad spend doesn't scale. A systematic way of increasing ad spends by 10-15% per day while weeding out poor geographies and interests can maintain a user's ROI. We are building a platform that will scale your ad budgets daily, maintain a minum bid, and expand to new market where ROI threshold is upheld. A human should do what humans do best - creative work. Site: http://www.adopilot.info

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Actual performance

5points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
75%75% 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.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
67%67% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: user, new · Missing: mac, agents, macos
61%61% 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 · Missing: plus, intuitive, reviews
40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
25%25% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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