Streamline Your Funnel Analytics with Dynamic UTM Tags and JavaScript

Hacker News

I built a JavaScript snippet for your website to solve the problem below. While UTMs are ubiquitous in analytics, the best practice requires constant effort to ensure external links are tagged. The reality is that only a few links contain UTM tags. Links that do have UTMs are often only digital marketing campaigns, generated through manual tagging with the help of a campaign tracking URL builder. This creates a challenge when you want to create standard reports to compare the performance across all marketing channels. This is even more important when your analytics stack extends beyond the Google ecosystem.

Visit product View on Hacker NewsMar 15, 2022

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

87%87% 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: google · Missing: mac, agents, macos

54%54% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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

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

32%32% predicted probability of success on Hacker News, 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

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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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