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Rooms get planned first.
Last-click sees the last ad.

For home & living brands on GA4, on any store platform. Upload your GA4 export and see what Pinterest, Google Shopping, Meta and every other channel caused, next to what last-click gave it, in 1–2 minutes.

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Full refund within 30 days, no questions asked. You keep the read. Prices exclude VAT.

One path, illustrated
  1. Pins a room idea on PinterestLast-click credit: 0%
  2. Compares products on Google ShoppingLast-click credit: 0%
  3. Reads a styling guide on your siteLast-click credit: 0%
  4. Clicks a Meta ad and buysLast-click credit: 100%

Last-click pays the final step and nothing else. The causal read asks what each step caused.

Why home & living attribution breaks. The plan starts long before the purchase.

Furniture and decor are planned: a room board, measurements, comparisons, a second look at the price. The channels that start that plan rarely get the last click. The ad at the end does, and last-click hands it the whole sale.

The bigger the purchase, the longer the plan and the more channels it passes through, and the more a last-click report leaves out.

  • Inspiration is not a click

    A pinned room or a saved product starts a purchase that can end much later, on another channel. Last-click only sees the end.

  • Considered buys touch every channel

    A lamp or a sofa gets researched on Pinterest, Instagram, Google Shopping and review sites before it is bought. Every platform claims the sale; none of them sees the whole path.

  • Seasons shift demand

    Renovation and gifting seasons change which channels matter. A credit rule set in the quiet months misreads the busy ones.

The causal read asks a different question. What would sales have been without it?

Last-click asks who touched the sale last. The causal read estimates, from the GA4 history you already have, what sales would have been without each channel: incremental sales are sales with the channel minus sales without it.

The math, as an illustration:

  1. Pinterest costs €6,000 a month. Last-click credits it with €4,800 in sales: 0.8x, the weakest line on the report.
  2. Switch it off and total sales fall by €18,000 a month, because the Google Shopping and Meta sales it started stop too.
  3. So Pinterest caused €18,000 on €6,000 of spend: 3.0x.

Last-click said cut it. The counterfactual says it starts the plans your other channels close. When a channel's sales would have happened anyway, the read says that too.

Illustrative round numbers, not a customer result. Your own read shows your channels.

What it answers for home & living brands

  • Is Pinterest starting purchases that Google Shopping and Meta finish?
  • Do paid social and shopping cause the sales they close, or close sales that were already planned?
  • Where should the budget go before the busy season?

Every channel gets a data-health score from 0 to 100 and a next step. A channel under 1% of credit on both methods is called too small to act on, not guessed at. How the read works.

This is what €99 buys. Your read, channel by channel.

Direct split back to the channels that sent those buyers. Last-click next to the engine. A data-health score and a next step for every channel.

31%

of last-click credit sat on Direct.

Direct is not a channel. The read splits it back.

Illustrative sample

Where Direct’s credit goes: Organic Search +9, Email +8, Paid Search +6, Paid Social +4, Cross-network +4

Last-click, Direct left outEngine, after the splitData health, 0 to 100
  1. Paid SearchAgrees
    Last-click, Direct left out: 34%
    Engine, after the split: 31%

    Data health 96

    Within 25% of last-click. Hold.

  2. Paid SocialEngine lower
    Last-click, Direct left out: 22%
    Engine, after the split: 14%

    Data health 93

    Cut spend a little in one region. Compare revenue after 14 days.

  3. Organic SearchAgrees
    Last-click, Direct left out: 18%
    Engine, after the split: 21%

    Data health 97

    Within 25% of last-click. Hold.

  4. EmailEngine higher
    Last-click, Direct left out: 11%
    Engine, after the split: 19%

    Data health 91

    Put a little more effort here. Judge it after 21 days.

  5. Cross-networkEngine higher
    Last-click, Direct left out: 9%
    Engine, after the split: 12.4%

    Data health 81

    Raise spend a little in one region. Compare revenue after 21 days. Data health under 90: move in steps.

  6. UnassignedFix tagging
    Last-click, Direct left out: 5.4%
    Engine, after the split: 2.1%

    Data health n/a

    No tracked source. Tag every link with a UTM source and medium, then run a new read.

  7. ReferralToo small
    Last-click, Direct left out: 0.6%
    Engine, after the split: 0.5%

    Data health 88

    Under 1% on both methods. Too small to act on.

Round numbers for illustration, not a customer’s read. Your read shows your own channels from your GA4 export, with your common paths to purchase and receipts that check every number against the file.

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Named results. Published with each brand's sign-off.

Three Dutch fashion brands so far. The read works the same way on any GA4 export, whatever you sell.

Read all five case studies

Home & living attribution, question by question.

  • Why does Pinterest show a lower ROAS than Meta for my home & living brand?
    Because Pinterest usually works at the start of a home purchase and Meta often at the end. A pin rarely produces the last click, so last-click hands the sale to the ad that closed it. A causal read estimates what sales would have been without Pinterest, sales with the channel minus sales without it, and sets that number next to what last-click gave it.
  • How do I know if cutting Pinterest will hurt my home & living sales or save money?
    Run a causal read before you cut. It estimates what each channel caused from your GA4 history, so you see whether Pinterest starts plans that Google Shopping and Meta close later, or collects credit for sales that would happen anyway. Every channel gets a data-health score from 0 to 100, and a channel under 1% on both methods is called too small to act on, not guessed at.
  • What is the difference between correlation and causality in home & living marketing attribution?
    Correlation is what last-click measures: which channel was present when the order came in. Causality is whether the order would have come in without it. A Meta ad can close many planned purchases and cause few; a Pinterest board can start many and close none. Causal attribution credits each with what it caused.
  • Can I see which channels drive sales for my Shopify home & living brand with GA4 data?
    Yes. The read runs on a GA4 export, so it works for a Shopify home & living brand and for any other store platform GA4 tracks. Upload a GA4 export (12 months or more reads best) and get a per-channel causal read in 1–2 minutes. No app to install, no pixel, no code.
  • How does seasonal demand affect attribution for home & living brands?
    Seasons change which channels start purchases and which close them, so a fixed credit rule is wrong in a different way each quarter. A causal read estimates channel effects on the window you upload. Run it before a busy season and again after, or keep it running on Pro, and the budget follows what each channel is causing now.

Keep reading. The terms and comparisons behind this page.

New to the method? Start with causal attribution or why last-click misleads.

Your platforms guess.
We run the math.

Upload a GA4 export and see what each channel caused, next to last-click, in 1–2 minutes. The read is yours to keep.

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The free finding runs in your browser. Your file never leaves it. The full read is €99, refundable within 30 days, no questions asked. Prices exclude VAT.