How attribution approaches compare
There are four broad ways to answer “which ad produced that sale”. Each is genuinely good at something, and each fails in a way the others do not.
Overview
Categories, not products
This page compares approaches — the native pixel inside an ad platform, attribution reconstructed by hand in a spreadsheet, enterprise marketing-mix modelling, and a dedicated first-party attribution layer like Hyros. It does not name, rank or make claims about any specific vendor's product.
That is deliberate. Individual tools change every quarter, but the structural trade-offs of each category do not: what data it can see, how long it remembers, how much volume it needs, and how much manual work it costs you every week.
Read the table as a description of typical behaviour for each category rather than as a scorecard. Most mature teams end up running two of these at once — a pixel for platform optimisation, and something else for the numbers they actually decide on.
Side by side
Side by side
The seven questions that decide which approach fits, answered per category.
| What you need | Hyros | Native platform pixels | Spreadsheet attribution | Enterprise MMM suites |
|---|---|---|---|---|
| Cross-channel view | One record covering every channel you run | Each platform sees only the traffic it sent | Whatever you paste in, as often as you paste it | Channel-level totals, modelled rather than joined |
| Journey-level detail | Individual touch sequences per profile | Conversions inside one platform's window | Usually aggregated — per-journey detail is impractical by hand | Aggregate by design; no individual journeys |
| Long consideration cycles | Attribution survives weeks or months after the click | Credit expires with the attribution window | Only if you keep the raw exports long enough | Handles long cycles well at the channel level |
| Offline and CRM outcomes | Closed deals and revenue post back to the original touch | Needs manual upload and often loses the join | Possible, but the join is rebuilt by hand each time | Included as an input series, never per lead |
| Feeding the ad platforms | Cleaned conversions pushed back for optimisation | Native — this is exactly what pixels are for | No path back into the platforms | Informs budget, not real-time bidding |
| Data volume required | Works from your first conversions | Works immediately, within its own limits | Any volume, but the effort scales with it | Needs years of history and substantial spend |
| Ongoing manual effort | Set up once, then automatic | Low, aside from periodic re-tagging | High and recurring — every report is rebuilt | Specialist time to build, run and interpret |
No category wins every row, and the right answer changes as a business grows. The rows that matter most are the ones describing the gap between your click and your money.
Category archetypes
What each approach is actually good at
Nothing here is a bad choice in general. Each one is a bad choice for particular questions.
Native platform pixels
The measurement built into each ad platform, reporting the conversions it observed on the traffic it sent.
Best at: optimising bids inside a single platform, in real time.
Weakest at: anything involving another channel, a second device, or a conversion that lands after the window closes.
Spreadsheet attribution
Platform exports, store reports and CRM extracts reconciled by hand on a recurring schedule.
Best at: flexibility and full transparency — you can see exactly how every number was produced.
Weakest at: repeatability. The join is rebuilt by a person each time, so it drifts, breaks quietly, and rarely survives the analyst who built it.
Enterprise marketing-mix modelling
Statistical models that infer each channel's contribution from aggregate spend and outcome series over long periods.
Best at: strategic budget allocation across channels, including offline media, without needing user-level data.
Weakest at: telling you anything about a specific campaign this week. It needs years of history and substantial spend before its estimates settle.
Choosing
Which one should you reach for?
You are optimising a single platform today
Stay with the native pixel and make sure it receives good server-side events. Nothing else reacts fast enough to steer bidding inside a platform.
You run several channels and a delayed sale
This is where a dedicated attribution layer pays for itself: one identity per person, one record across channels, and credit that survives the gap between click and money.
You spend at national scale across online and offline media
Marketing-mix modelling is the right instrument for strategic allocation — usually alongside, not instead of, journey-level attribution for the digital half.
FAQ
Choosing between approaches
No, and most teams should not. Platform pixels stay in place because they drive optimisation; a dedicated attribution layer becomes the record you make decisions from. The real mistake is running two sources and never deciding which one wins an argument.
For a single channel and a same-session purchase, honestly yes. It stops being enough the moment you add a second channel, a second device, or a delay between click and money — because at that point you are rebuilding an identity join by hand every week.
Because tool-by-tool claims go stale fast and rarely survive contact with your own funnel. Categories are stable: what each approach can see, how long it remembers, and how much manual work it costs. Once you know which category fits, evaluating specific tools inside it is a much shorter exercise.
Keep reading
Keep exploring the attribution stack
What is ad attribution?
Attribution is the practice of deciding which marketing touch gets credit for a conversion. Everything difficult about it follows from the fact that there is usually more than one honest answer.
Read moreAd attribution for call-based and high-ticket funnels
When the sale happens on a call, the conversion your ad platform sees is a form fill. Everything that decides profitability happens after the pixel stops watching.
Read moreAd attribution for info products and online education
Free lead magnets are easy to buy and easy to misread. Attribution built for info funnels scores campaigns on the students who enrol, not the emails you collected.
Read moreStart with the approach that matches your funnel.
Run the free analyzer to see which gaps apply to you, then continue on our partner platform when you want a full attribution stack behind it.
Hyros is an independent attribution explainer and demo site. The analyzer writes a live read-out of your described funnel; the full report step is illustrative and is not connected to your ad accounts.