Skip to main content
Attribution model analysis is essential for media, creative, and budget decisions. Below are complete tables and examples maintaining the original logic, now including: Linear, U-Shaped, and Last Click Non Direct & Organic.

How to use models in campaign analysis? (guide table)

QuestionIdeal Attribution Model
Which channels attract new customers?First Click
Which channels are responsible for final conversion?Last Click
Which paid channels close sales?Last Click Paid
What is the last click ignoring Direct/Organic?Last Click Non Direct & Organic
Which channels contribute at some point in the journey?Assisted
How to distribute credit equally?Linear
How to balance acquisition (start) and closing (end)?U-Shaped
Which channels really make a difference across the journey?Markov

When to use each model (quick best practices)

Last Click Paid

Use for: measuring direct performance of paid campaigns; evaluating bottom-of-funnel ads; prioritizing channels with financial return without organic/direct bias.
Essential metrics: Attributed Sales/Revenue, ROAS, ROI, CPA.

Last Click Non Direct & Organic

Use for: reducing noise from Direct/Organic visits in final moments; highlighting the last “active” effort from media/channel.
Essential metrics: Attributed Sales/Revenue, ROAS, non-organic session conversion rate.

Linear

Use for: measuring collaboration between channels in multi-touch strategies; comparing “share” of influence between touchpoints.
Essential metrics: Linear Revenue, % participation per channel, average journeys per conversion.

U-Shaped

Use for: balancing acquisition and closing, valuing awareness + conversion; useful in long funnels with multiple touchpoints.
Essential metrics: First/Last Revenue (weighted), middle-of-funnel Revenue, CAC/CPA per stage.

Markov

Use for: understanding real impact of each channel across the entire journey; justifying awareness investments; complementing First/Last analyses.
Best practices: combine with First/Last for balanced view; use to explain discrepancies between models.

Practical examples with comparative tables

The following numbers are illustrative to demonstrate readings and decisions.

1) Top of Funnel attracting new customers

Scenario: Beauty e-commerce seeking awareness and qualified traffic.
Strategy: Instagram/TikTok for top; Google for active demand; Facebook Remarketing.
ChannelFirst Click RevenueLast Click RevenueLast Click PaidLast Click Non Direct & OrganicAssisted RevenueLinear RevenueU-Shaped Revenue
Instagram Ads12,0002,5002,5002,50015,0006,0008,000
TikTok Ads8,0001,8001,8001,80010,0004,5005,200
Google Ads2,0009,5009,5009,50011,5006,5006,800
Facebook Remarketing1,50010,00010,0009,00011,5006,0007,000
Reading:
  • First Click: Instagram/TikTok lead acquisition.
  • Last/Last Paid/Non Direct & Organic: Google + Remarketing close.
  • Assisted/Linear/U-Shaped: Instagram/TikTok influence the journey; U-Shaped highlights the importance of first and last touch.
Actions: increase budget on Instagram/TikTok (acquisition); maintain/optimize Google and Remarketing (closing); test top-of-funnel creatives to increase final conversion rate.

2) High investment with low perceived return

Scenario: Electronics store invests $50,000/month in Google, but revenue doesn’t keep up.
Objective: Improve ROAS/ROI, reallocate budgets according to real contribution.
ChannelFirst Click RevenueLast Click RevenueLast Click PaidLast Click Non Direct & OrganicAssisted RevenueLinear RevenueU-Shaped RevenueROAS
Google Ads15,0007,0007,0007,50018,0009,0009,2001.4
Facebook Ads8,00018,00018,00017,50022,00011,50012,0003.5
E-mail Marketing2,00012,0000014,0006,5007,0005.0
Reading:
  • Google strong in First, weak in Last/Last Paid → generates traffic but doesn’t close.
  • Facebook closes more and has higher ROAS.
  • E-mail converts, but doesn’t appear in Last Paid (not paid media).
Actions: reduce part of Google budget and reallocate to Facebook; improve targeting/keywords (bottom of funnel); create paid remarketing to capture traffic generated by Google.

3) Campaign performing well and ready to scale

Scenario: Edtech launched new course; Facebook and Google performing.
Objective: Identify scale potential while maintaining efficiency.
ChannelFirst Click RevenueLast Click RevenueLast Click PaidLast Click Non Direct & OrganicAssisted RevenueLinear RevenueU-Shaped RevenueROAS
Facebook Ads20,00010,00010,0009,50025,00012,50013,5005.0
Google Ads5,00015,00015,00015,00018,0009,0009,8007.0
YouTube Ads12,0008,0008,0007,50016,0009,0009,2006.0
Reading:
  • Facebook drives top and support (Assisted/Linear); Google closes sales (Last/Last Paid/Non Direct & Organic).
  • YouTube contributes, but with higher CPA.
Actions: increase budget on Facebook (acquisition); scale Google with bottom-of-funnel campaigns; optimize YouTube creatives to reduce CPA.

Additional cross-analysis tips

  • Compare First vs Last/Last Paid/Non Direct & Organic to separate attraction from closing.
  • Use Linear and Assisted to measure journey collaboration.
  • Use U-Shaped when you want to strategically reflect the weight of discovery and conversion.
  • Bring Markov to justify investments and explain discrepancies between rule-based models (First/Last/Linear/U-Shaped) and real impact.

Final Summary (checklist)

  • First Click → new customer acquisition.
  • Last Click → final conversions.
  • Last Click Paid → direct performance of paid media.
  • Last Click Non Direct & Organic → removes Direct/Organic noise at last touch.
  • Assisted → overall channel contribution.
  • Linear → balanced collaboration.
  • U-Shaped → balances start and end of journey.
  • Markov → real impact across the entire journey.