A revolutionary way to strategize emails

Hacker News

Products tailored to an individual's preferences are the ultimate way of indicating that he or she has a high standard of living. Most corporations slowly dwindle away by not taking into account their customers' changing preferences, which we all know can change day by day. However, we aim to target the base preference that an individual will still buy, regardless of how often their preferences change. As weeks and months go by, even the base preference changes. This is what we are trying to address with bespoke. We know we can gather a lot of information from our customers using all the tools available, but what would be the baseline product that you need to launch, manufacture efficiently, and make a profit from? It's difficult to discern things from a sea of data, but we aim to make it easy and decipher the exact data. Read more on how it changes for DTC: https://bespoke.bespoke.surf/p/why-email-strategies-need-to-...

Visit product View on Hacker NewsMay 9, 2023

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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

73%73% 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.

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

49%49% 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

48%48% 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 HuntUnlikely to reach the leaderboard · Strong signals: email, using · Missing: mac, agents, macos

46%46% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

18%18% 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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