What is marketing attribution? And why does it matter for your store or restaurant?

Short answer: marketing attribution is knowing which ad or channel actually caused each sale. Without attribution, you know you spent on Snap, TikTok, and Google, and you know sales came in, but you cannot connect the two; so you split budget by feel. With attribution, every order is credited to its source, and you know exactly where to scale and where to stop.

An example that makes it concrete

A customer sees your TikTok ad, clicks your Google ad two days later, then orders 300 SAR from your website. Who gets the credit?

• Last-click attribution: Google, as the final interaction before purchase. The most common and simplest model.
• First-touch attribution: TikTok, because it introduced the customer to you.
• Distributed attribution: credit split across both.

There is no single “correct” model; what matters is measuring every channel the same way, from the same data source, so the comparison is fair.

Why is attribution especially hard in Saudi Arabia?

• Orders are scattered: an online store, POS in branches, and delivery apps; each lives in a separate system, and delivery apps hide customer data entirely.
• Tracking limits: iOS and ad blockers hide part of the customer journey from a traditional pixel.
• Many platforms: Snap, TikTok, Meta, Google, and X all perform here, and each attributes sales to itself.

The result: most merchants compare numbers that are not comparable. The fix is practical: unify orders in one place first, then connect them to campaigns with one consistent method. That is the core of what MIQAS does.

Common questions

Does attribution require a technical team? Historically yes; today no. Connecting your first channel in MIQAS takes about 15 minutes by pasting a snippet, with no code and no SQL.

What is a UTM? Small tags added to your ad links identifying the source and campaign; the simplest and most reliable attribution tool for direct orders.

Delivery orders have no links or UTMs. How are they attributed? By time and geography matching against the nearest campaigns; a useful approximation that reveals the trend instead of total blindness, and improves as your data grows.