Facebook Ads Targeting 101

Hacker News

Hey Guys, How is WFH doing to everyone? I have been working on Facebook ads for six years now & the last two years have been running my own performance marketing agency. I have started investing some time everyday in creating these courses & have launched the first one. Do check it out. Investing in Facebook ads can be tricky & it can wipe out the ad budgets if not done right. If Facebook ads are the cake, targeting is the cream of that cake. This ppt+video webinar will teach how to use the right targeting, creating audiences, case studies & best practices. Link: https://gumroad.com/l/aZmeh Have a discount code for the first 50 buyers. Code: welcome20 Would love to hear feedback :) Cheers!

Visit product View on Hacker NewsMay 21, 2020

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting

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AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

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Product HuntUnlikely to reach the leaderboard · Strong signals: code · Missing: mac, agents, macos

38%38% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide · Missing: https docs, excited, just released

36%36% 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: video · 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 · Missing: arr, mrr, revenue

23%23% 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

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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