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Li Tan
Essay 2 min read
· 2 min read · Attribution · Marketing Analytics · Measurement

Between MTA and LTA

The attribution debate never ends. Here is how I think about it.

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The attribution debate never ends. Here is how I think about it.

The core problem

A user sees a Facebook ad, clicks a Google ad, reads a blog post, then converts. Who gets credit?

  • Last-Touch (LTA). Google gets 100%.
  • First-Touch (FTA). Facebook gets 100%.
  • Multi-Touch (MTA). Some weighted split.

None of these is “correct.” They are all models with different assumptions.

Why LTA sticks around

LTA gets called simplistic a lot. It has real advantages too:

  1. Simple. Easy to explain, easy to build.
  2. Actionable. Clear signal for what to optimize.
  3. Conservative. Tends to favor the lower-funnel, high-intent channels.

For businesses with short consideration cycles, LTA is usually enough. I have seen teams make this too complicated when LTA would have done the job.

When MTA helps

MTA is more useful when:

  1. The consideration cycle is long (B2B, big-ticket consumer).
  2. You invest heavily upper-funnel (brand, content).
  3. Customer journeys are complex — multiple devices, channels, touchpoints.

MTA tries to credit each touchpoint based on its contribution to the conversion.

The dirty secret

Here is the thing: MTA does not measure incrementality.

MTA answers: “what touchpoints appeared in the journeys that converted?”

It does not answer: “what would have happened without those touchpoints?”

A user who was going to convert anyway still has touchpoints. MTA credits them regardless. That is why I always want to calibrate MTA with an actual experiment.

A better framework

Instead of arguing about attribution models, ask:

  1. What decision am I making?

    • Reallocating budget across channels → you need incrementality testing.
    • Optimizing within a channel → platform attribution is probably fine.
    • Understanding journeys → path analysis.

  2. How accurate do I need it?

    • Directionally correct → LTA is often enough.
    • Precise → you need experiments.

  3. What can I actually test?

    • Run holdout experiments where you can.
    • Use geo tests for channels that cannot be randomized at the user level.

Where I land

Attribution models are good for monitoring and directional optimization. For budget allocation, they should be calibrated against experimental evidence.

The best measurement stack I have seen is a combination:

  • Attribution for day-to-day monitoring
  • Incrementality tests for calibration
  • MMM for overall budget allocation

No single method gives you truth. Triangulating across methods gets you closer.

See it for yourself

Build a customer journey below and watch how five different attribution models split a single conversion. Same data, radically different stories — which is exactly why debating models without defining the decision is a waste of breath.

Interactive · MTA vs. LTA

Who gets the credit?

Build a customer journey. Compare how five attribution models split a single conversion.