> For the complete documentation index, see [llms.txt](https://docs.millimetric.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.millimetric.ai/core-concepts/attribution.md).

# Attribution & the classifier

Every event runs through a **pure, ordered classifier** that maps `(url, referrer, request_host)` into a normalised `(source, medium, campaign, confidence, rule_id)` tuple. The classifier is deterministic, has no I/O, and runs server-side on every ingest.

## The output

```json
{
  "source": "facebook",
  "medium": "paid",
  "campaign": "spring_launch",
  "source_confidence": "high",
  "source_rule_id": "fb_ad_redirect"
}
```

* `source` — the network or origin (`facebook`, `google`, `twitter`, `direct`, `internal`, etc.).
* `medium` — how the visit was driven (`paid`, `organic`, `social`, `email`, `referral`, `direct`).
* `confidence` — `high` / `medium` / `low`. A signal for downstream filters.
* `rule_id` — the exact rule that fired. Stored on the row so you can audit historical data and re-classify if the rules change.

## The rule cascade

First match wins. The full table:

| #  | Rule fires when…                                                                              | Result                                     | Confidence   |
| -- | --------------------------------------------------------------------------------------------- | ------------------------------------------ | ------------ |
| 1  | `gclid` in URL                                                                                | `google` / `paid`                          | high         |
| 2  | `msclkid`                                                                                     | `bing` / `paid`                            | high         |
| 3  | `ttclid`                                                                                      | `tiktok` / `paid`                          | high         |
| 4  | `li_fat_id`                                                                                   | `linkedin` / `paid`                        | high         |
| 5  | `fbclid` + referrer host is `l.facebook.com` / `lm.facebook.com`                              | `facebook` / `paid`                        | high         |
| 6  | `fbclid` + `utm_source` \~ facebook/instagram/meta                                            | `facebook` or `instagram` / `paid`         | high         |
| 7  | \`utm\_medium=cpc                                                                             | paid                                       | paid\_social |
| 8  | `utm_medium` ∈ paid set + `utm_source` \~ instagram                                           | `instagram` / `paid`                       | high         |
| 9  | `utm_medium` ∈ paid set + any `utm_source`                                                    | `<utm_source>` / `paid`                    | high         |
| 10 | `utm_medium` ∈ paid set, no `utm_source`                                                      | `unknown` / `paid`                         | medium       |
| 11 | `fbclid` alone, no other context                                                              | `facebook` / `paid`                        | **medium**   |
| 12 | `utm_source` explicit, no paid signal                                                         | `<utm_source>` / `<utm_medium ?? unknown>` | high         |
| 13 | Referrer host ∈ {facebook.com, m.facebook.com, l.facebook.com, …} without `fbclid`            | `facebook` / `social`                      | medium       |
| 14 | Referrer host ∈ {instagram.com, l.instagram.com}                                              | `instagram` / `social`                     | medium       |
| 15 | Referrer is a known social network (twitter, x, linkedin, reddit, tiktok, youtube, pinterest) | `<network>` / `social`                     | medium       |
| 16 | Referrer is a known search engine (google.\*, bing.\*, duckduckgo, …)                         | `<engine>` / `organic`                     | medium       |
| 17 | Referrer is an email-client / ESP                                                             | `email` / `email`                          | medium       |
| 18 | Referrer host == page host                                                                    | `internal` / `direct`                      | high         |
| 19 | No referrer + no UTM + no click ID                                                            | `direct` / `direct`                        | high         |
| 20 | Anything else                                                                                 | `<host slug>` / `referral`                 | **low**      |

## The Facebook social-vs-paid resolution (the headline)

This is the case we obsess over because it's where most tools blur the line:

| You see                                     | Most analytics show | Millimetric shows              |
| ------------------------------------------- | ------------------- | ------------------------------ |
| `?fbclid=abc` arriving via `l.facebook.com` | Facebook (mixed)    | **facebook / paid** (high)     |
| `?utm_source=facebook&utm_medium=cpc`       | Facebook            | **facebook / paid** (high)     |
| `?fbclid=abc` from anywhere else            | Facebook (mixed)    | **facebook / paid** (medium)   |
| Referrer `facebook.com`, no `fbclid`        | Facebook            | **facebook / social** (medium) |
| Referrer `m.facebook.com`, no `fbclid`      | Facebook            | **facebook / social** (medium) |

Same for `instagram.com` ↔ `utm_source=instagram&utm_medium=paid_social`.

## Why `confidence`

Some rules are inherently softer than others. `gclid` is *only* emitted by Google Ads — high confidence. `fbclid` alone *usually* means a paid click but can leak onto organic shares — medium confidence. A novel referrer we've never seen — low confidence.

In dashboards or downstream pipelines, you can filter on `source_confidence` to exclude noisy attribution when you want clean revenue-attribution numbers.

## Why `rule_id`

Stored on every event row. If the rules change in v2 and you want to re-attribute historical data, you can identify exactly which rule fired on each row and apply targeted updates instead of re-classifying everything.

## What it does *not* do

* **No fingerprinting.** No IP-based linkage, no canvas, no fonts. Just `(url, referrer)`.
* **No probabilistic attribution.** The classifier is rule-based and deterministic.
* **No machine learning.** Rule data lives in `packages/classifier/src/rules.ts` and is unit-tested against \~31 scenarios in `classify.test.ts`.

## Per-event vs. per-session attribution

The classifier evaluates *each event individually*. So a backend `signup` event POSTed without `url`/`referrer` will classify as `direct` — that's per-event truth.

For "where did this visitor enter from", query the **`sessions_v`** view instead. It captures the first-touch source/medium per `(anonymous_id, 30-min window)` and is the right answer for revenue attribution.

```sql
SELECT entry_source, entry_medium, count() AS sessions
FROM sessions_v
GROUP BY entry_source, entry_medium
ORDER BY sessions DESC;
```


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