My Link Portfolio Structure: Categories, Slugs, and Naming

Quick Answer

My portfolio is 16 tracked links across 9 categories, carrying 1,754 recorded clicks. The structure is deliberately boring: one link per offer, categorised by theme, with readable slugs. The interesting part is not the structure — it is what the category rollup revealed about where the audience actually was.

These are click counts from my own dashboard, not earnings. Clicks are not income. See the earnings disclaimer.

What This Means (Definition)

A link portfolio is the full set of tracked links you maintain, viewed as one thing rather than as scattered individual URLs. Treating it as a portfolio is what makes questions like "which theme is growing" answerable at all.

Sixteen links is small, and that is the point. A small, well-organised portfolio produces readable data. A large, unstructured one produces a spreadsheet nobody opens. The number of links is not the measure of anything useful.

The Step-by-Step Framework

1. One link per offer, not per placement. Placement is captured by campaign tags. This keeps sixteen links at sixteen rather than sprawling into hundreds.
2. Categories by theme. Mine are things like travel, events, crypto, merch, gig economy — the way I actually think about them, not by platform.
3. Slugs match the brand or offer name. Readable, lowercase, hyphenated, and stable. I have never changed one.
4. Never reuse a slug. Old mentions keep sending traffic for years, and re-pointing a slug at something unrelated betrays that traffic.
5. Read at category level first. Individual link numbers are usually too small to mean much. The rollup is where the signal lives.

Common Mistakes to Avoid

• Optimising for link count. More links is not more progress. It is usually just more mess.
• Categorising by platform. That is what campaign tags do. Doubling up wastes the category axis.
• Letting the uncategorised bucket grow. Anything uncategorised is effectively invisible at review time.
• Restructuring frequently. Trend data requires a stable taxonomy. Changing it resets your history.
• Reading clicks as revenue. They measure interest, nothing more.

How to Implement This Today

List every affiliate link you use and count them. If the number is above about thirty and you have no categories, that is the problem to fix before anything else. Assign five or six themes and put every link in exactly one.

Then read the rollup and compare it to what you expected. In my case the largest single category accounted for roughly a third of all clicks — and it was a category I had written almost nothing about, while the topic the entire site was built around sat far below it. That gap was the most useful thing the portfolio ever told me.

What the Distribution Actually Revealed

The structure is unremarkable. What made it worth building was that it made a mismatch visible that I would otherwise have kept not noticing.

The site those links sat alongside was, for a long time, entirely about one topic — creator software and AI tooling. Every article, every landing page, every pillar pointed the same direction. The click data said something different: the largest single category was travel, at roughly a third of all recorded clicks, and the category the whole site was built around sat well below it. Events and crypto also out-clicked it.

The honest reading is more nuanced than "write about travel". Those clicks came largely from social distribution rather than from the site, so what the data proves is that the demand existed in the audience, not that the site had earned it. But that is still the useful finding: there was appetite in categories with no owned content at all, while the fully built-out category was the smaller one.

The other thing it surfaced was a plain operational failure. The highest-traffic destination in the portfolio was, for a long time, a storefront URL carrying no affiliate attribution whatsoever — hundreds of clicks leaving unattributed. Nothing surfaces that except a deliberate audit, which is the strongest argument for doing one.

The Bigger Picture

The purpose of structure is not tidiness. It is making it possible to be surprised by your own data, because unstructured data cannot surprise you — it just looks like noise and gets ignored.

Almost everyone who tracks properly for the first time discovers their audience wanted something other than what they were producing. Finding that out early is worth far more than any individual optimisation.

- Jeff