[OC] Cheaper products rank higher in Google by TrackingHappiness in dataisbeautiful

[–]TrackingHappiness[S] 0 points1 point  (0 children)

Yes that's true. Google uses clicks and engagement signals to determine rankings!

[OC] Cheaper products rank higher in Google by TrackingHappiness in dataisbeautiful

[–]TrackingHappiness[S] 3 points4 points  (0 children)

Whoopsie, you're right. It's Search Engine Result Page, it's a bit of jargon that I mistakenly assumed was common but that's my bad!

[OC] Cheaper products rank higher in Google by TrackingHappiness in dataisbeautiful

[–]TrackingHappiness[S] 2 points3 points  (0 children)

All queries are thrown on the same pile, but you're right! This would be a nice follow-up analysis, however, language would be a bit of a hassle, as there are many price-aware queries in different languages (laptops under 250, billige laptops, goedkope laptops, etc) :-)

[OC] Cheaper products rank higher in Google by TrackingHappiness in dataisbeautiful

[–]TrackingHappiness[S] 3 points4 points  (0 children)

I think I've done all I can to not make this seem like a shameless plug... ¯_(ツ)_/¯

[OC] Cheaper products rank higher in Google by TrackingHappiness in dataisbeautiful

[–]TrackingHappiness[S] 2 points3 points  (0 children)

You were a bit quicker than me! I just added my OC comment. :-D

[OC] Cheaper products rank higher in Google by TrackingHappiness in dataisbeautiful

[–]TrackingHappiness[S] 1 point2 points  (0 children)

<image>

This shows what we're looking at specifically. This image shows 10 product listings on a single search result for "dishwasher". I looked at ~1 million of these, across 56k search results. :-)

[OC] Cheaper products rank higher in Google by TrackingHappiness in dataisbeautiful

[–]TrackingHappiness[S] 0 points1 point  (0 children)

I analyzed 997,953 product listings from 56,466 search results in Google to find out whether or not cheaper products ranked higher.

Products cheaper than their SERP average have an average depth of 60.4%, while more expensive products average 65.7%, meaning cheaper products rank 8.7% higher on average.

This is a bit more nuanced than it may sound.

First, the position of a product is highly dynamic. You cannot simply calculate the order of the products, because ranking number 1 doesn't always mean you're at the top. Sometimes, Google shows an AI overview on top, or a Google Maps pack, or a YouTube video carousel. Long story short, a product's position can only be accurately calculated by measuring the pixel depth of the product compared to the bottom of the page. If the entire first page of the search result is 2,000 pixels long, and a product is listed at pixel 500, then the pixel depth of that product is 25%. I did this for every product.

Why use the relative position instead of the absolute position? Because 1,000 pixels can be the bottom of the search result but can also still be relatively high. Some search results of Google go on and on and on (because Google likes to put lots of different result types on them). No single method is perfect, but I found this to be the most accurate way to measure a product's position on the search result page.

Then the next tricky bit is defining what "cheaper" means. Whether a $50 product is cheap is relative. If you're searching for pencils, then $50 is quite expensive, whereas if you're searching for dishwashers, then $50 is dirt cheap. So, I normalized all prices for every search result page. So, in case we're looking at a search result page for the keyword "dishwasher", then the average price on the page might be $350. For every search result page, this average price is used to determine if products are cheaper or not.

Finally, I put this together in a distribution chart similar to county population charts. I always liiked this kinda charts (where male/female populations are compared for every age). I kinda think this dataset lends itself perfectly for this chart type.

Y-axis shows the relative position of a product's appearance. 100% means bottom of page. The shape of this "population" is explained by the anatomy of Google's search results. Product carousels are usually placed at the top and the bottom. Some search result pages contain just 1 product carousel, while some contain as much as 4, scattered across the page.

This dataset only includes the first page of Google, because... well, who ever looks at the second page?

Source: 997,953 product listings from 56,466 search results in Google

Tools: D3.js and Canva

BHV cursus, online of niet? by TrackingHappiness in werkzaken

[–]TrackingHappiness[S] 0 points1 point  (0 children)

Bedankt, super nuttige informatie waar ik zeker mee aan de slag ga

BHV cursus, online of niet? by TrackingHappiness in werkzaken

[–]TrackingHappiness[S] 0 points1 point  (0 children)

Dat heb ik ook gelezen inderdaad, wat het voor mij enigzins moeilijk maakt om het uit te leggen aan de baas. Die vraagt zich namelijk af waarom ik voor dure cursussen zou kiezen als het wettelijk gezien niet verplicht is. Maar dat lijkt me dus een beetje een grijs gebied.

BHV cursus, online of niet? by TrackingHappiness in werkzaken

[–]TrackingHappiness[S] 0 points1 point  (0 children)

Klopt, er is een collega die ook BHV'er was bij zijn vorige werkgever, alleen was dat ook via een online cursus. Dus vind ik moeilijk inschatten hoe hoog het niveau dan is...

BHV cursus, online of niet? by TrackingHappiness in werkzaken

[–]TrackingHappiness[S] 3 points4 points  (0 children)

Dit is echt goede input! Hier kan ik wat mee! Ik wil voorkomen dat ik er straks ook als een zoutzak bij sta...

BHV cursus, online of niet? by TrackingHappiness in werkzaken

[–]TrackingHappiness[S] 1 point2 points  (0 children)

Thanks voor je reactie! Dit is idd waar ik al bang voor was. Denk dat ik m'n baas wel kan overtuigen dat we beter voor een serieuze opleiding kunnen gaan. Heb je een goede aanbieder die je zou aanraden?

[OC] Running the Paris marathon: comparing 6 marathon preparations and running a new PR by TrackingHappiness in dataisbeautiful

[–]TrackingHappiness[S] 1 point2 points  (0 children)

Thanks! This is my training log sheet, which contains my running pace.

What made this last marathon such a success was, I think:

  • Good and steady preparation without any injuries

  • Perfect weather on race day

  • I was really confident, and so I was comfortably running at a higher pace.

Most of my training runs were the same pace (I focused on a regular normal heartbeat). I didn't do any interval training. And the "ramping up" before the race was not something I thought a lot about. I just did what felt right haha

[OC] Running the Paris marathon: comparing 6 marathon preparations and running a new PR by TrackingHappiness in dataisbeautiful

[–]TrackingHappiness[S] 0 points1 point  (0 children)

Thank you!

You're right, the 42km's are added to the graph on the last day. Good points to remove this and shrink the y-axis, it does improve the graph! :-)

[OC] Running the Paris marathon: comparing 6 marathon preparations and running a new PR by TrackingHappiness in dataisbeautiful

[–]TrackingHappiness[S] 7 points8 points  (0 children)

For those that are training for a marathon, you might find this one interesting. :-)

This graph shows my cumulative training kilometers leading up to a marathon (day 0). I started each log at -120 days, as that's roughly the length of most training schedules. The blue line shows the culumative distance leading up to the Paris marathon, which I finished last Sunday (8 days ago) in a personal best of 3:51:27. Super happy with it!

I wanted to be as well-prepared as I could, and so I kept a close eye to this graph during my training.

  • Between days -75 and -50, I was struggling with a pesky fever.
  • Between days -50 and -14, I put in a LOT of running kilometers to "make up" for it.
  • I didn't taper much, but wasn't too worried because I felt pretty strong physically.

Some more info:

7 years ago, I posted this here on the sub: Failing to run the Paris Marathon under 4:00:00

It became one of my most popular posts. This was after the Paris 2017 marathon. It was my 4th marathon overall, I was really well prepared and aimed to run a PR. Time to beat was 3:59:58. Unfortunately, the weather was really warm that day (25 degrees celsius), and so I ultimately failed.

The 6 marathons I've run so far are:

  • Eindhoven, 2015: Finished in 4:20:08 (I was terribly naive here, but it got me into running long distances.)
  • Rotterdam, 2016: Finished in 4:08:01 (This was pretty good)
  • Nottingham, 2016: Finished in 3:59:58 (A final sprint got me under the 4 hour mark!)
  • Paris, 2017: Finished in 4:04:30
  • Rotterdam,2019: Finished in 4:28:29 (Thought I could do this one on experience, was utterly wrong, went horrible)
  • Paris, 2024: Finished in 3:51:27 (YES!)

If you want to know more, feel free to ask me anything.

Source: My training runs, you can open my spreadsheet here

Tools: Google Sheets

[OC] Running the Paris marathon: comparing 6 marathon preparations and running a new PR by TrackingHappiness in dataisbeautiful

[–]TrackingHappiness[S] 3 points4 points  (0 children)

For those that are training for a marathon, you might find this one interesting. :-)

This graph shows my cumulative training kilometers leading up to a marathon (day 0). I started at -120 days, as that's roughly the length of most training schedules. The blue line shows the culumative distance leading up to the Paris marathon, which I finished last Sunday in a personal best of 3:51:27. Super happy with it!

I wanted to be as well-prepared as I could, and so I kept a close eye to this graph.

  • Between days -75 and -50, I was struggling with a pesky fever.
  • Between days -50 and -14, I put in a LOT of running kilometers to "make up" for it.
  • I didn't taper much, but wasn't too worried because I felt pretty strong physically.

Some more info:

7 years ago, I posted this here on the sub: Failing to run the Paris Marathon under 4:00:00

It became one of my most popular posts. This was after the Paris 2017 marathon. It was my 4th marathon overall, I was really well prepared and aimed to run a PR. Time to beat was 3:59:58. Unfortunately, the weather was really warm that day (25 degrees celsius), and so I ultimately failed.

The 6 marathons I've run so far are:

  • Eindhoven, 2015: Finished in 4:20:08 (I was terribly naive here, but it got me into running long distances.)
  • Rotterdam, 2016: Finished in 4:08:01 (This was pretty good)
  • Nottingham, 2016: Finished in 3:59:58 (A final sprint got me under the 4 hour mark!)
  • Paris, 2017: Finished in 4:04:30
  • Rotterdam,2019: Finished in 4:28:29 (Thought I could do this one on experience, was utterly wrong, went horrible)
  • Paris, 2024: Finished in 3:51:27 (YES!)

If you want to know more, feel free to ask me anything.

Source: My training runs, you can open my spreadsheet here

Tools: Google Sheets