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What is the Markov Model?

The Markov Chains attribution model (or simply Markov) is an advanced way to understand the real value of each channel, campaign, or ad in conversion journeys. Unlike traditional models (First Click, Last Click, Linear, Time Decay, etc.), Markov doesn’t distribute credit based solely on fixed points in the journey, but on the importance each interaction has within all observed journeys. In other words: Markov doesn’t just look at “who brought” or “who converted”, but at the impact each interaction has when present and when absent.
In the Markov model, we don’t consider the complete journey, precisely to ensure greater quality and consistency in attribution. Today we work with a 7-day window, which best balances volume and data accuracy.Very long journeys tend to have noise and dilute the relevance of touchpoints closest to conversion, which can negatively impact this model’s analysis.

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How Markov is Calculated

The Markov model at Nemu follows the removal effect method:
  1. We map all journeys: both those that resulted in conversion and those that didn’t.
  2. We simulate removing a channel/campaign/ad:
    • We remove that point’s presence from the journeys.
    • We calculate what the conversion rates would be without that point.
  3. We compare with the original scenario:
    • If conversion drops, it means that channel played a fundamental role.
    • If there’s no impact, it means that channel had little or no role in conversion.
  4. We proportionally assign value: each channel’s share is based on how much it actually contributes to conversion as a whole.

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Practical Example

Imagine we have the following journeys:
  • User 1: Google → Meta → Conversion
  • User 2: Organic → Meta → Conversion
  • User 3: Google → Conversion
If we apply the removal effect to Meta:
  • Journeys that went through Meta now “lose” that step.
  • The result shows significant drop in total conversions.

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Conclusion

Meta played an essential role in the journey (even without always being the last click).

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Explaining to a Child

Think of putting together a puzzle. Each piece helps form the final picture. The Markov model tests what happens if you remove one piece: does the picture still appear or is it incomplete? This way we know which pieces are really important to complete the drawing.

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Advantages of the Markov Model

  • Measures real impact of each channel.
  • Considers the entire user path, not just beginning or end.
  • Helps identify top-of-funnel channels that contribute invisibly in other models.
Exclusivity: Very few platforms offer this level of analysis in an accessible way.