Rival Ads

AppSumo

Customers appreciate Rival Ads for its efficient competitor ad tracking across Meta, Google, TikTok, and LinkedIn. They praise its quick setup, responsive support, and valuable insights.

Customers appreciate Rival Ads for its efficient competitor ad tracking across Meta, Google, TikTok, and LinkedIn. They praise its quick setup, responsive support, and valuable insights. Some users have noted minor quirks in localized copy and limited availability of certain ad metrics. Overall, Rival Ads is hitting the mark with a 4.9 rating and positive feedback. With a 60-day money-back guarantee, it's worth giving it a try for those in need of competitor ad tracking.

Visit product View on AppSumoJul 20, 2026

Share card

Actual performance

25reviews

Did not reach leaderboard

Traction signals

Rating5.0 / 5

Purchases139

Launch Intel predictions

Analyze your own launch →

Product HuntOn track for Day 1 leaderboard · Strong signals: google, user · Missing: mac, agents, macos

73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: efficient, users · Missing: plus, platform, intuitive

71%71% predicted probability of success on AppSumo, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

30%30% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: google, users · Missing: mobile apps, ios, personal

26%26% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

22%22% predicted probability of success on Hacker News, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

16%16% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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