Upsell Plugin

AppSumo

Customers appreciate Upsell Plugin for its quick and easy setup, seamless integration with WordPress page builders, and responsive customer support. Some users wish for broader payment method options and more customizable styling. Despite minor drawbacks, the overall sentiment is positive, with users recommending it as a great alternative to WooCommerce. With a 4.2 rating and a 90-day money-back guarantee, it's worth giving Upsell Plugin a try.

Visit product View on AppSumoOct 7, 2024

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Actual performance

6reviews

Did not reach leaderboard

Launch Intel predictions

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

74%74% 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.

AppSumoMay struggle as an AppSumo deal · Strong signals: builder, users · Missing: plus, platform, intuitive

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

40%40% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: 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: io · Missing: https docs, excited, just released

19%19% predicted probability of success on Hacker News, based on ML models trained on real launch data.

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

17%17% 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

6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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