Position-Based Attribution: Credit First and Last Touches

What Position-Based Attribution Measures

Position-based attribution answers: "How do I credit both the channel that introduced this customer AND the channel that converted them?"

It's a compromise between first-touch (introducers) and last-touch (closers):

The LinkedIn ad that sourced the opportunity gets 40%. The demo request that triggered conversion gets 40%. The nurturing content in between splits the remaining 20%.

Why Position Matters

Different touchpoint positions serve different strategic functions:

Position-based acknowledges that beginning and end are often strategically distinct from the middle—while still crediting the journey.

When Position-Based Is the Right Choice

1. B2B Lead Generation

B2B funnels often have clear "sourcing" (first touch) and "closing" (last touch) moments that different teams own:

This satisfies both demand gen teams (who want sourcing credit) and performance teams (who want conversion credit).

2. Long Sales Cycles with Distinct Stages

When journeys span months with clear phase transitions:

• Awareness phase: First touch starts the journey
• Consideration phase: Content and nurture (middle touches)
• Decision phase: Last touch triggers purchase

Position-based matches this mental model without over-complicating attribution.

3. Shared Credit Across Teams

When marketing and sales share pipeline responsibility:

Position-based creates natural accountability without fighting over credit.

4. When You Value Introduction AND Conversion

If you believe two moments matter most—discovering the brand and deciding to buy—position-based captures both without requiring data-driven complexity.

When Position-Based Is the Wrong Choice

1. Content-Heavy Nurture Journeys

If your middle-funnel content—whitepapers, webinars, case studies—genuinely drives purchase decisions, position-based under-credits them:

If each content piece builds conviction, they deserve more than a 20% split.

Consider: Linear attribution or custom weights.

2. Single-Session Conversions

If first touch = last touch (same session purchase), position-based adds no value:

For e-commerce impulse buys, simpler models work fine.

3. When You Have High-Volume Data

With 5,000+ monthly conversions, data-driven models can learn actual touchpoint importance rather than assuming 40-20-40. Why guess when you can measure?

Position-Based Variations

Standard U-Shaped (40-20-40)

The classic split:
- 40% to first touch
- 20% split among middle touches
- 40% to last touch

Best for: Standard B2B, clear intro/close distinction.

W-Shaped (30-30-30-10)

Adds a third key position—typically "lead creation" (form fill, signup, MQL):

Best for: B2B with distinct lead capture moment, marketing → sales handoff.

Custom Position-Based

Adjust percentages to match your funnel:

The 40-20-40 split is arbitrary—customize if you have reason to.

How Position-Based Works

The Math (U-Shaped)

If touchpoints = [first, middle1, middle2, ..., last]:

first_touch_credit = 0.40
last_touch_credit = 0.40
middle_credit_per_touch = 0.20 / (number_of_middle_touches)

Special cases:
- 1 touchpoint: Gets 100%
- 2 touchpoints: Each gets 50%

Implementation

class PositionBasedAttribution
  def initialize(first_weight: 0.4, last_weight: 0.4, lookback_days: 90)
    @first_weight = first_weight
    @last_weight = last_weight
    @middle_weight = 1.0 - first_weight - last_weight
    @lookback_days = lookback_days
  end

  def attribute(conversion)
    touchpoints = conversion.user.touchpoints
      .where("occurred_at >= ?", conversion.occurred_at - @lookback_days.days)
      .where("occurred_at <= ?", conversion.occurred_at)
      .order(:occurred_at)
      .to_a

    return [] if touchpoints.empty?

    case touchpoints.size
    when 1
      # Single touch gets 100%
      [build_result(touchpoints.first, 1.0)]
    when 2
      # Two touches split 50/50
      [
        build_result(touchpoints.first, 0.5),
        build_result(touchpoints.last, 0.5)
      ]
    else
      # First: @first_weight, Last: @last_weight, Middle: split @middle_weight
      middle_touches = touchpoints[1..-2]
      middle_credit = @middle_weight / middle_touches.size

      results = [build_result(touchpoints.first, @first_weight)]
      middle_touches.each { |tp| results << build_result(tp, middle_credit) }
      results << build_result(touchpoints.last, @last_weight)
      results
    end
  end

  private

  def build_result(touchpoint, credit)
    {
      channel: touchpoint.channel,
      source: touchpoint.source,
      medium: touchpoint.medium,
      campaign: touchpoint.campaign,
      credit: credit,
      touchpoint_at: touchpoint.occurred_at
    }
  end
end

W-Shaped Implementation

W-shaped requires defining the "lead creation" touchpoint:

class WShapedAttribution
  def initialize(first_weight: 0.3, lead_weight: 0.3, last_weight: 0.3, lookback_days: 90)
    @first_weight = first_weight
    @lead_weight = lead_weight
    @last_weight = last_weight
    @other_weight = 1.0 - first_weight - lead_weight - last_weight
    @lookback_days = lookback_days
  end

  def attribute(conversion)
    touchpoints = conversion.user.touchpoints
      .where("occurred_at >= ?", conversion.occurred_at - @lookback_days.days)
      .where("occurred_at <= ?", conversion.occurred_at)
      .order(:occurred_at)
      .to_a

    # Find the lead creation touchpoint (e.g., form fill)
    lead_touch = touchpoints.find { |tp| tp.is_lead_creation? }

    # Distribute credit based on position
    # ... (similar logic with three key positions)
  end
end

The tricky part: defining what counts as "lead creation." Usually it's:
- Form submission
- Demo request
- Free trial signup
- MQL qualification

Comparing Position-Based to Other Models

Position-Based vs Linear

When to prefer position-based: When you're confident first and last are most important.
When to prefer linear: When all touchpoints genuinely contribute equally.

Position-Based vs Time-Decay

When to prefer position-based: B2B where sourcing matters.
When to prefer time-decay: E-commerce where recency correlates with intent.

Position-Based vs First + Last Combined

Instead of position-based, some teams run first-touch and last-touch separately:

Both work—position-based is simpler for reporting, separate is better for deep analysis.

Common Position-Based Mistakes

Mistake 1: Using 40-20-40 Without Thinking

The 40-20-40 split is arbitrary. If your middle-funnel content drives conversion, 40-20-40 undervalues it.

Fix: Analyze whether middle touches correlate with higher conversion rates. If so, consider increasing middle weight.

Mistake 2: Not Handling Edge Cases

Two-touch journeys shouldn't give 80% to one path:

BAD: 2-touch journey
Ad → Purchase
40% + 40% = 80%? Where's the other 20%?

GOOD: 2-touch journey
Ad → Purchase
50% + 50% = 100%

Fix: Handle 1-touch and 2-touch journeys as special cases.

Mistake 3: Ignoring Journey Length Distribution

If most journeys have 2-3 touchpoints, position-based behaves almost like first-touch + last-touch. The "middle" barely exists.

Fix: Analyze journey length distribution. If most journeys are short, simpler models may suffice.

Mistake 4: W-Shaped Without Clear Lead Stage

W-shaped only works if you have a clear "lead creation" event. Without it, you're guessing which touchpoint to credit.

Fix: Only use W-shaped if you track explicit lead creation events (form fills, signups).

Position-Based and GA4

Google Analytics 4 removed position-based attribution in 2023. Your options:

1. Third-Party Attribution Tools

Use mbuzz or similar tools that support position-based with configurable weights.

2. Build in Your Data Warehouse

WITH touchpoint_positions AS (
  SELECT
    conversion_id,
    channel,
    touchpoint_time,
    conversion_value,
    ROW_NUMBER() OVER (PARTITION BY conversion_id ORDER BY touchpoint_time) as position,
    COUNT(*) OVER (PARTITION BY conversion_id) as total_touches
  FROM touchpoint_data
),
credited AS (
  SELECT
    *,
    CASE
      WHEN total_touches = 1 THEN 1.0
      WHEN total_touches = 2 THEN 0.5
      WHEN position = 1 THEN 0.4
      WHEN position = total_touches THEN 0.4
      ELSE 0.2 / (total_touches - 2)
    END as credit
  FROM touchpoint_positions
)
SELECT
  channel,
  SUM(credit * conversion_value) as attributed_revenue
FROM credited
GROUP BY channel
ORDER BY attributed_revenue DESC;

Implementing Position-Based in mbuzz

mbuzz uses AML (Attribution Modeling Language) — a small Ruby DSL — to define how credit is distributed. U-shaped is a three-line program; the weights are explicit arguments rather than hidden config.

Basic U-Shaped (40-20-40)

within_window 90.days
  apply 0.4 to touchpoints[0]
  apply 0.4 to touchpoints[-1]
  apply 0.2 to touchpoints[1..-2], distribute: :equal
end

40% to the first touchpoint, 40% to the last, 20% split equally across the middle. The distribute: :equal modifier handles the middle pool. Edge cases (1 or 2 touchpoints) collapse cleanly: a single touchpoint takes all the credit; two touchpoints split 50/50.

Custom weight distributions

Change the literal values to skew the model toward introduction, conversion, or nurture:

# Closer-heavy (performance focus)
within_window 90.days
  apply 0.3 to touchpoints[0]
  apply 0.5 to touchpoints[-1]
  apply 0.2 to touchpoints[1..-2], distribute: :equal
end

# Nurture-heavy (content-driven sales)
within_window 90.days
  apply 0.25 to touchpoints[0]
  apply 0.25 to touchpoints[-1]
  apply 0.5 to touchpoints[1..-2], distribute: :equal
end

W-Shaped (with a lead-creation position)

W-shaped extends the model with a third anchor point — typically the lead-creation event (demo request, free trial start, contact sales). Implementing it in AML uses the same conditional-logic pattern shown for custom models in the AML reference: select the lead-creation touchpoints by their channel or event tag, then apply a fourth weight band.

Tuning Position-Based for Your Business

Position-based is highly customizable. Here's how to tune the weights.

Weights by Business Type

In AML, the levers are the literals on each apply line and the within_window duration. Stretch the window to 180 days for enterprise cycles; tighten it to 30 days for product launches.

Seasonal and campaign adjustments

For a product launch view, increase first_weight to 0.50 (every introduction matters when the product is new) and shorten the window to 30 days. For an end-of-quarter closer view, push last_weight to 0.55 on a 45-day window. For a brand campaign view, run an intro-heavy variant (0.55 / 0.20 / 0.25) on a 60-day window. Run any of these in parallel with your standard U-shaped and compare the credit split.

Team-based views

Different teams need different perspectives. Run multiple AML programs side-by-side: a demand-gen view (first_weight: 0.60), a performance view (last_weight: 0.60), and a leadership view (balanced 40/20/40). The DSL is small enough that maintaining three variants is cheap, and showing all three side-by-side is exactly what makes the political conversation tractable.

Parameter Tuning Cheatsheet

Summary

Position-based attribution (U-shaped) gives 40% credit to first touch, 40% to last touch, and 20% to middle touches. It balances the need to credit both introduction and conversion while acknowledging the nurturing journey.

Use position-based when:
- B2B with clear sourcing and closing moments
- Multiple teams share pipeline responsibility
- You want both first-touch and last-touch credit in one model
- Long sales cycles with distinct funnel stages

Don't use position-based when:
- Middle-funnel content is critical to conversion
- Single-session conversions dominate
- You have high volume for data-driven models
- The 40-20-40 assumption doesn't match your reality

Best practice: Start with 40-20-40 as a baseline, then analyze whether your middle touches deserve more credit. Consider W-shaped for B2B with clear lead creation stages. Validate with incrementality tests.

Further Reading

On Attribution Models:
- First-Touch Attribution — Understanding introduction value
- Last-Touch Attribution — Understanding conversion value
- Linear Attribution — The neutral alternative
- How to Choose the Right Attribution Model — Decision framework

On B2B Attribution:
- B2B Attribution for Long Sales Cycles — Adapting attribution for B2B

Holly Henderson

Co-Founder, mbuzz

Holly Henderson is Co-Founder of mbuzz. With 10+ years in marketing including roles at Westpac, Avon, and Forebrite, she's obsessed with making measurement actually useful.

Harvard Extension School Forebrite Westpac Avon