No-Code Segment for Ecommerce

Hacker News

Hi all, Wanted to share what we have been working on the past few months :). We are building a Segment alternative for Ecommerce because we believe that current SaaS-focused CDPs do not serve this market well at the moment for a couple of reasons: 1. In SaaS you presume identification at every session, in ecom a lot of the interesting tracking is before conversion (= identification). That is why we put a ton more effort in stitching unidentified sessions with identified sessions. 2. One of the most annoying things about CDPs is how long they take to set up (= a whole spec needs to be established beforehand). In Ecom, the spec of interesting events is largely predetermined (Product View, Add To Cart, etc.) which means we can actually get away with a completely *one-click install*. Time to value is drastically reduced. 3. Conversion tracking (= purchase events) and attribution are extremely important in ecom, as most of these brands are heavily dependent on ads for demand. We track all of these events server-side (e.g. Shopify API）to make sure that 100% of these events arrive (whereas client-side tracking only does so up until 80% in most cases). I welcome all pieces of feedback!

Visit product View on Hacker NewsDec 13, 2022

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Indie HackersFits the IH revenue-focused audience · 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: code · Missing: mac, agents, macos

65%65% 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, io · Missing: https docs, excited, just released

47%47% 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: month, way · Missing: mobile apps, ios, personal

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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

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

14%14% predicted probability of success on AppSumo, 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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