Multi Touch Attribution

See What Your Channels Actually Contribute

Illustration of six rule-based attribution models, including last interaction, first interaction, linear, position based, time decay, and customised, each assigning credit differently across the same customer journey.

When performance relies on rule-based attribution, credit is assigned according to what model “feels right”, introducing human bias that blinds you to what truly builds demand.

Data-driven attribution helps with assigning credit across the full customer journey, giving you a more reliable base to make tactical decisions.


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What you’ll gain

A data-driven attribution model gives you a single, independent view of channel contribution, so you can base your budget decisions on what the data supports.


Get a reliable channel picture

Bar chart comparing channel credit under first touch, last touch, and data-driven MTA across five channels, showing how each model tells a different story about the same performance.

Rule-based attribution models apply fixed weights to the customer journey that have no grounding in how customers actually behave.

Data-driven attribution reads the full journey and distributes credit accordingly, so channels that build demand get recognised alongside the ones that convert it.


Turn attribution into budget decisions

A spend share versus effect share chart is the most immediate way to put the results of your attribution model to work.

Move budget away from channels taking more than they are giving back and towards the ones returning more than they are getting.

Bar chart comparing spend share and effect share across five channels with ROAS overlaid, highlighting where budget exceeds returns and where returns exceed budget.


When MTA is useful

Multi-touch attribution works best for digital-first or digital-only businesses that have been advertising for 2 years or less, have robust server-side tracking, and run minimal or no offline, influencer, or non-digital promotional activities.

Use multi-touch attribution when you need a unified, platform-agnostic view of your digital channels. Common situations include:

  • Inflated Platform Data - Your ad platform dashboards clock more conversions or revenue than your actual backend shows.

  • The Overlap Dilemma - You are running a mix of digital channels and cannot confidently pinpoint which mix is driving results.

  • The Bottom-Funnel Trap - Budget flows to bottom-funnel channels because they claim all the credit, leaving your upper-funnel contribution unclear.

However, MTA focuses primarily on digital interactions and struggles to account for macro factors such as price reductions, promotions, seasonality or offline advertising.

If your marketing mix is becoming more complex as your business scales, it may be time to transition to marketing mix modelling.


How I work

Data Audit & Integration

  • Map tracking across UTM, events, and conversions

  • Confirm data linkability via persistent ID

  • Agree look-back window

Attribution Model Build

  • Configure data-driven attribution model

  • Compare outputs & validate against actual revenue

  • Select model that best fits data & business question

Analysis & Sign-Off

  • Profile channel contribution and efficiency gaps

  • Agree channel view & budget allocation implications

Deploy & Recommendations

  • Deliver channel investment recommendations

  • Agree refresh cadence

Each solution is tailored to your tracking setup, data maturity, and the channel mix to measure.


FAQ

Each ad platform attributes conversions using its own methodology and default settings, which are not designed to produce a consistent view across all channels.

A data-driven MTA model built on your first-party data applies a single methodology across every channel and is not influenced by how any individual platform chooses to count.

Three core inputs: granular clickstream and event data capturing each user’s interactions and the source they came from; cost and impression data at campaign level from your advertising platforms; and clear conversion outcome data such as orders, form submissions, or CRM deal records.

All three need to be linkable through a persistent identifier, supported by your server-side tracking setup.

Yes, but less so if you have server-side tracking in place, which safely preserves first-party cookies for short conversion windows (under a few days). For longer customer journeys, browser privacy restrictions will still cause fragmentation.

Rather than guessing or over-crediting the last click, our model scopes the look-back window to what your first-party data can reliably prove.

Not to start, but optimized tracking yields optimised results.

If you already have server-side tracking, you are in a great position. If not, our initial data audit will map out your current gaps, helping you understand exactly what your data can and cannot account for before the model goes live.

Typically six to twelve weeks.

For digital-only brands, the timeline depends almost entirely on how cleanly server-side and platform data sources are integrated.

They are designed to complement each other at different stages of growth.

MTA is your bottom-up, tactical tool for the first 1–2 years, giving you daily, digital-only channel guidance. MMM is a top-down, aggregate model that you graduate to as your mix introduces offline media, site-wide promotions, and macro seasonality.

No. Attribution is conducted using open-source frameworks whenever feasible, keeping work portable, transparent and easy to maintain & scale. This means no vendor lock-in, transparent code, and extensibility as your internal capability grows.


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