XaaS Pricing – track 25k+ SaaS company pricing strategies in one place

Hacker News

Hi all - I'm completely new here! I come from a market analyst background - I've done large B2B tech pricing research and consulting projects for over 10 years. I wanted to figure out a way to scale what we do in those projects to a broader set of SaaS companies that can't pay the six-figure plus price points those projects often carry. Today is our first step on figuring that out - we've created XaaS Pricing, a tool that let's you analyze the pricing strategies of 25,000 SaaS vendors across 50+ metrics, on-demand, with real-time updates when pricing strategies change. I hope it's helpful and look forward to feedback and questions from the HN community!

Visit product View on Hacker NewsAug 15, 2022

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · 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.

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

64%64% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: 000, io · Missing: https docs, excited, just released

55%55% 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: way · Missing: mobile apps, ios, personal

33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

24%24% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

20%20% 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.

Incorrect prediction on native model

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