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Take 2 with Pathview’s cookieless content attribution

Hacker News

Take 2 with Pathview’s cookieless content attribution

Afternoon HN. I’ve been working to address the concerns previously voiced by fellow hackers. I underestimated the amount of passion some would have for data privacy. Thanks again to everyone who donated a few minutes to educate me. Pathview no longer uses a salted and hashed IP to count unique visitors. I’ve removed the metric entirely and replaced it with “unique sessions” which is more congruent with my intention. I would not have arrived at this solution without HN. Originally, I was returning a .gif in response to the JavaScript trigger, which now returns a timestamp. The conversion report now highlights first touch, last touch, and multi-touch content paths. Pathview content attribution is a (single channel) platform for conversion optimization. I now realize conversion attribution was too broad a term for this application. Users can implement a Do Not Track mechanism should they need such functionality. I will not make GDPR claims until I’ve consulted with a lawyer. Though, my intention is to be just that. The underlying capture mechanism leverages 160-characters of JavaScript to capture two pieces of information: the current page and the referring page. The script then returns a time-stamped response. Post-response, I store three pieces of data derived from the visit: the base domain, current page, and referring page. My general approach to post-processing represents an iteration and improvement over old-school log analytics. There are two main differences: I do not capture or store the visiting IP address and Pathview is a cloud solution rather than a self-hosted application. Also, disabling JavaScript prevents the visit from being recorded. In time, some ad blockers will impact visit captures too. I still have content to write and a short to-do list before I start beta testing. I know I need to explain what I do and how I do it to build trust. Does anyone have questions, concerns, or recommendations? I would welcome any insight. I’m prepared to listen and adapt as necessary. Note: I turned the Pathview script off for this post. It will not capture your visit. Original Post: https://news.ycombinator.com/item?id=32480811

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
84%84% 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: user, new, single · Missing: mac, agents, macos
62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
60%60% 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: users · Missing: mobile apps, ios, personal
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, host, users · Missing: plus, intuitive, reviews
32%32% 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 · Missing: mrr, revenue, profit
16%16% 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.

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