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Data & Analytics

Attribution Model Challenge

Know what each channel truly delivers

Challenge your attribution model, quantify its biases channel by channel and adopt a target model and incrementality tests to allocate your media budgets on a solid basis.

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They trust us+70 clients

  • L'Oréal
  • Canal+
  • Sephora
  • Chaumet
  • Renault Group
  • Salomon
  • TAG Heuer
  • Etam
  • Shiseido
  • Yves Rocher
  • Byredo
  • Jardiland
  • Hiscox
  • Fursac
  • Cegid
  • Bricomarché
  • Gamm vert
  • agnès b.
  • A.P.C.
  • Monte-Carlo Société des Bains de Mer
  • RAJA
  • Mouratoglou Academy
  • Alptis
  • OGF
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The need

What you tell us

« My attribution (last click, data-driven) guides my media budgets, but I don't know if it's accurate. I want to challenge the model, measure its biases and make decisions on solid ground. »
  • « I don't know what each channel really brings in »
  • « My attribution favors one channel at the expense of others »
  • « I want to allocate my budgets on a solid basis »

Our response

  • Mapping of the model and journeys
  • Highlighting biases and discrepancies
  • Recommendation: target model and incrementality tests

The principle

Knowing what each channel truly delivers

Illustration of the principle, without client data.

Last click overestimates some channels and erases others: the model challenge highlights these biases, channel by channel.

  • Last click
  • Model challenged

Method

Our method in 5 steps

Our stance

A challenged model

An attribution model relies on conventions. We challenge them before they dictate your budgets.

  • Bias
  • Comparison
  • Incrementality
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  1. Step 01 : Scoping

    Review of the current model and attribution windows, the channels and budgets involved, data access (GA4, ad platforms) and your decision-making questions.

    • Current model and attribution windows
    • Channels and budgets concerned
    • Data access (GA4, ad platforms)
    • Priority arbitration questions

  2. Step 02 : Mapping

    Analysis of actual multi-touch journeys and conversion paths by segment, identification of untracked touchpoints and data quality control.

    • Real multi-touch journeys
    • Conversion paths by segment
    • Untracked touchpoints
    • Input data quality

  3. Step 03 : Comparison

    Comparison of models (last click, data-driven, by position) with quantified gaps by channel, sensitivity to attribution windows and documentation of edge cases.

    • Models compared: last click, data-driven, position-based
    • Quantified gaps by channel
    • Sensitivity to attribution windows
    • Documented edge cases

  4. Step 04 : Biases & impacts

    Quantification of over-crediting and under-crediting for each channel, budget impact of biases and prioritization of corrections.

    • Over-crediting and under-crediting by channel
    • Budget impact of biases
    • Risks of incorrect arbitration
    • Prioritization of fixes

  5. Step 05 : Recommendations & tests

    Recommendation of a target model, incrementality testing plan (geo-tests, holdouts) and measurement governance.

    • Recommended target model
    • Incrementality testing plan
    • Geo-test and holdout protocols
    • Measurement governance

Deliverables

What you get

  • Diagnosis of the current model

    Strengths, blind spots, risks.

  • Quantified comparison of models

    Discrepancies by channel.

  • Bias & budget impacts

    What the model over-credits or under-credits.

  • Incrementality testing plan

    Prioritized protocols.

Who is it for?

Who is this offer for?

Tools used

  • GA4
  • BigQuery
  • Piano
  • Looker
  • Meta
  • Google Ads
  • Multi-channel advertiser with significant media budgets
  • Mature marketing data (GA4, ad platforms)
  • Sponsor: Head of Acquisition, Head of Data, CMO

To go further

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