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Overview

Dema’s attribution engine ensures that each conversion (order) is assigned to the right marketing source. By mapping UTM parameters, user sessions, and integrated cost data, Dema helps you see which channels and which campaigns drive profitable conversions.
Attribution is crucial for identifying where to allocate your marketing budget, comparing performance across channels, and measuring return on ad spend (ROAS) at a more granular level.

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Models we support

Dema offers four primary attribution models. You can select one that aligns with your business logic or compare models side by side for a complete view of channel impact.

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1. Last-click

  • How it works The last marketing channel a user interacted with before placing an order receives 100% credit.
  • Pros & cons
    • Pros: straightforward and aligns with many standard analytics platforms.
    • Cons: may undervalue earlier touchpoints (awareness campaigns).

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2. Linear

  • How it works All channels in the user’s path to purchase share conversion credit equally.
  • Pros & cons
    • Pros: recognizes every touchpoint’s contribution.
    • Cons: can over-credit minor interactions that barely influenced the final purchase.

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3. ML-based multi-touch (MTA)

  • How it works Our algorithmic model evaluates each user’s full journey and learns how much each touchpoint contributes to the conversion. Rather than assigning fixed percentages (e.g., 30% first-click), it adapts dynamically using historical data.
  • Pros & cons
    • Pros: captures complex journeys with more precision, often uncovering undervalued channels.
    • Cons: requires more data and can be less intuitive at a glance.

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4. Ad platform-reported

  • How it works Uses each ad platform’s native settings (Meta, Google Ads, TikTok, etc.). Their conversion windows (e.g., 7-day click, 1-day view) define attribution.
  • Pros & cons
    • Pros: matches what you see in ad platform dashboards.
    • Cons: can differ across platforms, making cross-channel comparisons less consistent.

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How Dema’s ML-Based Multi-Touch Attribution works

Dema’s ML-based Multi-Touch Attribution (MTA) goes beyond simple models like last-click or linear. It uses machine learning to evaluate the entire customer journey and assigns conversion credit based on the actual contribution of each touchpoint. This approach gives you a more accurate understanding of which channels and campaigns drive results.

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Key Features of ML-Based MTA

  1. Analyzes the full customer journey The model considers every interaction a customer has with your brand—clicks, views, and other engagements—and evaluates their impact on the conversion.
  2. Learns what drives conversions Instead of assigning fixed percentages to specific touchpoints, Dema’s algorithm uses historical data to learn how much influence each interaction had. It adapts as customer behavior changes over time.
  3. Context matters The model accounts for important factors like:
    • The order of interactions (e.g., first vs. last touch).
    • The time between interactions.
    • The type of channel (e.g., paid ads, organic search, email).
  4. Dynamic credit allocation Conversion credit is assigned based on the importance of each touchpoint. For example:
    • A paid social ad might generate interest and receive 30% credit.
    • A remarketing email might nurture the customer further and get 20%.
    • The final paid search click that led to the purchase might earn 50%.

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Choosing the right model

Each model offers a different perspective on which channel or campaign deserves conversion credit. Depending on your funnel length and goals, you may prefer a single model or use several at once.
1

Evaluate funnel complexity

If customers buy soon after clicking an ad, last-click may suffice. For longer journeys, ML-based MTA often reveals hidden touchpoint value.
2

Compare models

Running multiple models shows how credit shifts. Channels that seem weak under last-click may appear stronger in linear or ML-based MTA.
3

Align with stakeholders

Some teams or agencies rely on ad platform-reported conversions. Decide which model is your single source of truth for ROI.

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How it works in Dema

  1. UTM & session tracking
    • Dema uses utm_source, utm_medium, utm_campaign, etc., at session start.
    • These persist until the user either checks out or the session expires.
  2. Order association
    • When a user converts, Dema identifies the relevant session(s) and divides credit based on your chosen model.
  3. Real-time updates
    • Dema processes data in near real-time, so you can see updates within minutes.
    • Overnight jobs reconcile missed events or offline data to ensure accuracy.
  4. Adjusting models
    • You can switch models in the reporting interface.
    • Historical data is recalculated, letting you compare how each model allocates credit over time.
Conversion window
By default, Dema uses a 30-day click window for multi-touch attribution.

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Key takeaways

  1. No single model works for everyone—match your choice to funnel complexity.
  2. ML-based MTA can reveal hidden value in mid or early funnel, but it’s more complex.
  3. Ad platform data may differ from Dema’s logic; use both for a broader picture.