HackerTube.dev

Parisian Love

Google · 8.7M views · 2009-11-19 · 0:52

Open on YouTube ↗

If playback is restricted in your region, try opening the original video. Use Mark watched to add it to your marked videos.

Overview

Google's 2009 'Search Stories' ad, shown in the Super Bowl, tells the story of an American who falls in love in Paris entirely through search queries.

On Hacker News

HN cites it as a model of emotional product storytelling and as inspiration for visualizing one's own search history as a narrative.

Recommended by 1 HN people across 1 months · HN mentions 2010–2025

Topics

Search by tags

What people said

vardump · 2022-10 · Recommendation

Hmm... something like this? Maybe this video left out the brainwashing part. [VIDEO] (Google's famous ad, "Parisian Love", worth watching for those who haven't seen it yet)

Read the original HN comment ↗
nostrademons · 2025-05 · Reference

Quality can lead to sales - this was the premise behind the original Google (they never spent a dime on advertising their own product until the Parisian Love commercial [1] came out in 2009, a decade after founding), and a few other tech-heavy startups like Netscape or Stripe. Microsoft certainly didn't spend a billion $ marketing Altair Basic. The key point to understand is the only effort that matters is that which makes the sale . Business is a series of transactions , and each individual transaction is binary: it either happens or it doesn't. Sometimes, you can make the sale by having a product which is so much better than alternatives that it's a complete no-brainer to use it, and then makes people so excited that they tell all their friends. Sometimes you make the sale by reaching out seven times to a prospect that's initially cold but warms up in the face of your persistence. Sometimes, you make the sale by associating your product with other experiences that your customers want to have, like showing a pretty woman drinking your beer on a beach. Sometimes, you make the sale by offering your product 80% off to people who will switch from competitors and then jacking up the price once they've become dependent on it. You should know which category your product fits into, and how and why customers will buy it, because that's the only way you can make smart decisions about how to allocate your resources. Investing in engineering quality is pointless if there is no headroom

Read the original HN comment ↗
fallenatreus · 2019-05 · Reference

This looks like fb comment page than a hacker news one. So I just downloaded all of my search activity into nice JSON files, and now I am going to apply a bit of data science and magic stuff using Python and Node.JS and I will have a nice profile of myself for the past 11 years: 1) What kind of questions did I ask google? (beginning with how, when, where etc) - how do these questions connect to my personal and career development? 2) Apply some sentiment analysis using sentiment.js on the search terms and find out if the particular search term was positive, negative or neutral (this will give me a nice overview of my mood for each day I searched for the last one decade) 3) do some filtering and data massaging to pick out the exceptions (ok just a cursory look at the file helped me discovered a forgotten website project I did 10 years ago as part of college summer project - a work I am extremely proud of during that time and even now.) - which days, months, the year I searched the most (or what events triggered the searches - a breakup, a job search or a side project) 4) discover interesting patterns of my search profile (what I searched most during college time, after college when searching for a job, before marriage, after marriage etc.) 5) make a video on the lines of [VIDEO] so many f cking brilliant things I can do with my data. But what? Google has a copy of the same too? baby cry * What am gonna do? In a larger scheme of things, my data profile is just too insignificant.

Read the original HN comment ↗

Sources

Video by Google, played through YouTube.

Data and selection notes

HN summaries and labels may contain AI errors. HN dates refer to discussions, not video publication.

HN Library: HackerTube export (2026-10-31) and open-index/hacker-news. Upstream summaries and classifications may contain AI errors.

Views, duration and playback status refresh separately from the published catalog. When an update fails, existing data remains visible.

Selection checks
  • SaaS & AI candidates: Topic keywords do not match
  • Editor-approved picks: Editorial review is required

Rule matches identify candidates. They do not guarantee video quality or financial results.

About the library and its sources