polodidatticofama.it SEO & development

Website, content and SEO strategy for a university guidance centre. The first thing I did, on 27 January 2026, was install GA4 and submit the sitemap to Search Console the same day: first you put the measure in place, then you work. Everything that follows is measured from there.

Polo Fama asked me to rebuild their website from the ground up. I started by taking stock of the pages to know what was already there, sorted them into ten topic clusters, and between February and August 2026 published nineteen new articles. I chose to write the articles in HTML, so that I decide myself how the structured data goes in rather than leaving it to the page builder. Along the way the site stopped being only pages to read and became an interactive ecosystem, with several guidance tests, a tuition fee simulator, a direct link into the CRM FAMA for anyone filling in a form or signing up to the newsletter, and a good many other tools you can see at polodidatticofama.it.

real Search Console data, web search · same month, one year apart

What the numbers say, and what they do not

The comparison above puts July 2025 against July 2026, and I chose it because it is the one that flatters me least. July 2025 had been the best month of that year, so the comparison starts from the highest point available and the growth that comes out of it is four times over. Had I picked April, which went badly in 2025, exactly the same growth would have looked eight times over. Same work, double the number, changing only the month you start from: which is why a multiplier on its own means nothing unless you know where it starts.

Then two things that case studies usually leave out. The first: the site now appears far higher up, with average position moving from 13.4 to 6.8, that is from the second page of results to the first. Over the same period, though, the share of people who click when they see it went down, from 1.63% to 1.42%.

There are at least three plausible causes, and the available data cannot separate them. The results page is more crowded than it was a year ago: above the organic links there can be paid ads and AI-generated panels, and in both cases the searcher finds an answer before reaching the site. There is also a composition effect, because as the site grew it started appearing on many more informational searches, where people read rather than click. And there is a measurement artefact: an improving average position can hide a tail of appearances in ninth and tenth place, where clicks are close to zero by nature. Anyone claiming to know which of the three weighs most is guessing. What you can do is say so, and keep measuring.

The second: part of the growth is the calendar and not merit. The biggest month of the year is March, and March falls inside the window when teachers submit their applications for those same lists. That demand would have existed anyway, with me or without me.

What the calendar does not explain, though, is the rest of the year. The pages on the site fall into two groups: those that live on deadlines, such as calls, ranking lists and teaching qualifications, and those that have none, such as what a degree costs or which course to choose. If the growth had been seasonal alone, the first group would have grown most. The opposite happened: pages with no deadline grew 5.1 times over, those with one 4.8.

Two articles, two different mechanics

On 24 February 2026, one day after applications opened for the Italian supply teaching lists, I published a guide to the IT certifications that earn points on them. In five days: 29,823 impressions, 976 clicks, average position 5.8. The credit here is not the volume, which the deadline would have brought to somebody regardless. It is that on that day the page was already online.

On 24 July 2026 I published an article on the reform of the biologist profession, and something different happened. That article got into Google Discover, where nobody is searching for anything and the content is put in front of people as they scroll: 1,997 clicks in four days, with a click-through rate of 8.9%, six times the one from search. It has happened once in sixteen months of data, so I do not call it a channel I have a hold on. The interesting part comes afterwards: in search, rather than fading, that article kept gaining positions for three weeks. The first piece catches a deadline, the second builds a position.

How I decide what to write

Not from a list of headlines, from a calendar of audiences. Each month has one: the person checking the cost before enrolling, the one who has to qualify to teach, the one who works and goes back to studying at forty. That is what the ten clusters are for, and taking stock of the 340 pages is what tells me what already exists before adding anything.

There is also a rule about sources, and it is the part of the work that shows least. On 3 August 2026 I spent a day checking the figures that were about to go into the articles, and threw four of them out: employment data from a consortium that none of the three universities belongs to, an American study from 2009 quoted as current, a regulation that does not exist, and a course attributed to the wrong university. Nobody would have noticed. It is why every figure in those articles has a date next to it.

The tool I run the plan with

The plan does not live in a spreadsheet. I built Marketing Hub, a Flask application with a SQLite database: every article moves through seven stages, from idea to monitoring, and next to each one sit its own performance figures, read from Search Console and GA4. In the native interfaces those two things live in different products and never meet. Here the performance of a query and the working state of the article chasing it appear on the same screen.