Source Medium MTA Advanced Documentation

Overview

Our Multi-Touch Attribution (MTA) model provides a comprehensive view of how various marketing efforts contribute to customer purchases. By unifying data from multiple sources of first party data you already own, we construct detailed purchase journeys that help you understand the impact of different marketing channels, landing pages, and ad creatives.

Why is this a superior approach to MTA modeling?

No New Pixels: use the first party funnel data you already have
Total Transparency: direct access to all underlying data that make up the model in BigQuery
Accurate Revenue Impact: use accurate net revenue, gross revenue, or profit figures
All the Numbers Add Up: everything adds up to the correct number, no “magic”

Architecture

Data Sources

We integrate first-party purchase funnel data from:

Customer Acquisition Platforms (CAPI): Elevar and Blotout
Analytics Platforms: Google Analytics 4 (GA4), Snowplow, and Heap
Mobile App Streams: Tapcart
Compatible MTA Vendors: Northbeam, TripleWhale, Rockerbox

Unified Purchase Journey Dataset

We standardize and unify funnel event streams from these sources into a single schema. By organizing events into purchase journeys and assessing their quality, we create a comprehensive dataset:

Highest Quality Journeys: We select the best purchase journey from each data source, referred to as the “Source System”.
Quality Assessment: The quality of a purchase journey is judged by the number of valid touch points it contains.

Data Quality Assurance

We prioritize data accuracy and integrity through:

Event Deduplication: Eliminating duplicate events to prevent skewed attribution.
Consistent Classification: Applying rigorous rules for channel and touch point classification.
Validation Testing: Implementing tests to ensure correct purchase associations and revenue calculations.

Key Concepts

Purchase Journey

A purchase journey represents the sequence of events a customer goes through before making a purchase. This includes events like:

Collection & Product Page Views
Add to Cart
Lead Capture
Checkout Steps
Purchase

These events are standardized to the GA4 E-Commerce Event Schema, such as view_item, add_to_cart, purchase, etc.

Touch Points

Touch points are individual events within a purchase journey that carry key metadata:

Timestamp: When the event occurred.
UTM Parameters: Campaign tracking codes.
Landing Page URL: The initial page the customer landed on.
Ad Creative ID: Identifier for the ad that led the customer to the site.

We extract three critical pieces of metadata from touch points:

Landing Page
UTM Parameters
Ad Creative ID

Valid Touch Points

A touch point is considered valid if it contains at least one of the following:

Valid Landing Page: Any URL that is not a checkout or order confirmation page.
Valid Ad Creative ID: A numerical ID with at least four digits, extracted from URL parameters.
Mappable Marketing Channel: A marketing channel that can be identified through our channel mapping logic.

We do not consider Google branded campaigns as valid marketing channels for attribution purposes. Currently, the UTM values must contain “google” and “brand” in order for it to be considered branded search. In the future, we’ll connect to the actual underlying ad unit to better identify branded searches.

Lookback Window

We currently will associate a touch point with a purchase with a max of 120 days lookback window. In other words, the preceding touch point must happen less than 120 days from the purchase.

Dimensions

In our model, a dimension refers to a specific aspect we attribute conversions to:

Marketing Channels
Ad Creatives
Landing Pages

A purchase journey can be attributable to one or more dimensions if it contains at least one valid touch point for that dimension.

Distinct Dimension Values

We assess the complexity of a purchase journey by counting the distinct values within each dimension, which is not necessarily equivalent to the number of touch points:

Example: A customer visits the same landing page 3 separate times on 3 separate dates before making a purchase. This purchase journey contains 3 landing page touch points, but only 1 Distinct Dimension Value for the landing page model dimension, as the landing pages were not unique.

In our default reporting template, you can set a minimum number of distinct values required for a journey to be included in MTA analysis. By default, this is set to 2.

Attribution

Channel Classification Overview

We have a default channel mapping logic that assigns marketing channels based on UTM parameters, referrer domains, and other common URL parameters. This logic is defined in our sm_event_marketing_channel_grouping macro and will be customizable in future releases. Our model automatically classifies traffic into the following channels: Paid Channels

Meta: Facebook and Instagram, identified through UTM sources like facebook, fb, and referrer domains.
Google Ads:
Non-Brand Search: Campaigns not containing branded terms.
Brand Search: Campaigns containing branded terms but are not considered for attribution.
TikTok
Snapchat
X Ads (Twitter)
Pinterest
AppLovin

Owned Channels

Email: Identified via UTM sources/mediums like email, klaviyo, and referrer domains.
SMS
Push Notifications
Direct Mail
Shop App

Earned/Other Channels

Affiliate
Influencer
Loyalty Program
Organic Search: Traffic from search engines like Google, Bing, DuckDuckGo.
Organic Social
Press/Media Coverage
Blog
Customer Service
QR Code Scans
Marketplace

Channel Mapping Examples

Email:
- UTM Source/Medium contains patterns like email, klaviyo.
- Referrer domain matches known email providers.
Meta:
- UTM Source is facebook, fb, or ig.
- Referrer domain is facebook.com or instagram.com.
SMS:
- UTM Medium contains sms.
- No referrer domain, and UTM Source is null.

Channel Classification Detailed Logic

The above is a simplified overview. Our channel mapping system automatically classifies traffic sources using UTM parameters and referrer information. Below is the current hierarchy of channel attribution rules. Channel Mapping Rules (in order of precedence)

Reddit

    Matches ANY of:
    - source: reddit
    - referrer: reddit.com
    Channel: "reddit"

Podcast/Newsletter

- medium contains: podcast, newsletter
Channel: "podcast_newsletter"

YouTube

Matches ANY of:
- source: yt, youtube
- medium: yt, youtube
- campaign contains: youtube
- referrer: youtube.com
Channel: "youtube"

Google Search (Brand/Non-Brand)

When source = 'google':
Brand Search if:
- campaign_tactic = 'brand' OR
- campaign contains 'brand' (but not 'non-brand') OR
- medium = 'organic'
Channel: "brand_search"

Otherwise:
Channel: "non_brand_search"

AppLovin

Matches ANY of:
- source: applovin
- medium contains: applovin
Channel: "applovin"

Direct Mail

Matches ANY of:
- source contains: postpilot
- medium contains: direct[-_]?mail
Channel: "direct_mail"

Social Media Platforms

TikTok:
- source: tiktok
- referrer: tiktok.com
Channel: "tiktok"

Snapchat:
- source: snapchat
- medium: snapchat
- referrer: snapchat.com
Channel: "snapchat"

Influencers:
- source: refersion, grin
Channel: "influencers"

X Ads:
- source: twitter, x, x_ads
Channel: "x_ads"

Pinterest:
- source/medium: pinterest
- referrer: pinterest.com
Channel: "pinterest"

Meta (Facebook/Instagram):
- source/medium matches meta pattern
- meta_ad_id present
Channel: "meta"

Loyalty & Rewards

Matches ANY of:
- source: smile_rewards, friendbuy, referralcandy
- campaign: smileio_referrals, referral, loox
- referrer: referralcandy, stamped, loox-referrals
Channel: "loyalty_program"

SMS

Matches ANY of:
- source contains: postscript, attentive, sms, attv, mms, para
- medium contains: postscript, attentive, attv, sms, mms
- utm_id contains: sms
Channel: "sms"

Press/Media

- source or referrer matches press sources list
Channel: "press"

Email

Matches ANY of:
- source matches email pattern
- medium matches email pattern
- medium in email mediums list
- campaign matches email pattern
- campaign in: campaign, flow
- referrer in email referrers list
Channel: "email"
12. Other Channels
Organic Social:
- referrer in: facebook, instagram, tiktok, snapchat, pinterest, youtube, linktr, messenger, manychat
- source in: linktree, beacon
- medium: social
Channel: "organic_social"

Taboola:
- source/medium/referrer contains: taboola
Channel: "taboola"

AI Search:
- referrer: chatgpt, perplexity
- source contains: chatgpt, perplexity
Channel: "ai_search"

Organic Search:
- referrer matches organic search domains
Channel: "organic_search"

Employee Links:
- source: lululinktree
Channel: "employees"

Blog:
- source: blog
Channel: "blog"

Order Tracking:
- source/medium contains: malomo
Channel: "order_tracking"

Customer Service:
- referrer: gorgias
Channel: "customer_success"

QR Codes:
- referrer: qrcodes
- medium: qr
Channel: "qr_code"

Marketplaces:
- referrer in: amazon, costco, thredup
Channel: "marketplace"

Calendly:
- medium: calendly
Channel: "calendly"

Survey:
- referrer: typeform
Channel: "survey"

Default Classifications

No Parameters:
- All UTM parameters and referrer are null
Channel: "(none)"

Unmatched:
- Any traffic not matching above rules
Channel: "(other)"

Core Attribution Elements

UTM Parameters Standard URL parameters that help track traffic sources:

https://example.com?utm_source=facebook&utm_medium=paid_social
&utm_campaign=summer_sale

Common Parameters:

utm_source: Traffic origin (facebook, google, tiktok)
utm_medium: Marketing method (cpc, email, social)
utm_campaign: Campaign name
utm_content: Ad variant/creative ID
utm_term: Search keywords

Implementation:

Manually added to URLs
Built into ad platform URL builders
Automatically added by email/SMS platforms
Can be generated using URL builders like Google’s Campaign URL Builder

Referrer Domain The website that sent traffic to your site.

User clicks link on facebook.com → Your site
Referrer domain = facebook.com

Common Implementations:

Automatically captured by analytics platforms
Available in server logs
Accessible via JavaScript: document.referrer
Sometimes blocked by privacy settings or HTTPS restrictions

Ad Platform IDs Facebook/Meta Ad IDs

Common Parameters:
- fbclid      : Facebook Click ID
- fbadid      : Facebook Ad ID
- fb_adid     : Alternative Facebook Ad ID format

Implementation:

Automatically added by Facebook Ads
Found in Facebook Ads Manager
Can be extracted from dynamic URL parameters
Often used in conversion tracking
Google Ad IDs

Common Parameters:
- gclid       : Google Click ID
- gadid       : Google Ad ID
- gaid        : Google Analytics ID

Implementation:

Automatically added by Google Ads
Found in Google Ads interface
Used for conversion tracking
Important for Google Analytics integration

TikTok Ad IDs
Common Parameters:
- ttclid      : TikTok Click ID
- ttadid      : TikTok Ad ID

Implementation:

Automatically added by TikTok Ads
Found in TikTok Ads Manager
Used for conversion attribution

MTA Vendor Parameters

Vendor-specific IDs:
- nbt         : Northbeam tracking
- tw_adid     : Triple Whale Ad ID
- h_ad_id     : Hyros Ad ID

Implementation:

Added by respective attribution platforms
Usually requires platform-specific pixel installation
Often combined with platform native tracking

Example Full Attribution URL

https://example.com/product?
  utm_source=facebook&
  utm_medium=paid_social&
  utm_campaign=summer_sale&
  utm_content=video_ad_1&
  fbclid=abc123&
  fbadid=789xyz&
  nbt=nb_campaign_456

Common Implementation Methods

Direct URL Building

const baseUrl = 'https://example.com';
const params = new URLSearchParams(\{
    utm_source: 'facebook',
    utm_medium: 'paid_social',
    utm_campaign: 'summer_sale'
\});
const fullUrl = `${baseUrl}?${params.toString()}`;

Platform Auto-Tagging Most ad platforms offer automatic URL parameter addition:
- Facebook Ads Manager: Auto-adds fbclid, fbadid
- Google Ads: Auto-adds gclid
- TikTok Ads: Auto-adds ttclid
Marketing Tools Integration Email/SMS platforms automatically add tracking:

Klaviyo: 
utm_source=klaviyo&utm_medium=email

Attentive:
utm_source=attentive&utm_medium=sms

Attribution Parameter Best Practices

Consistent Naming:

✅ facebook, paid_social, summer_sale_2023
❌ FB, Paid Social, Summer Sale

Parameter Hierarchy:

Platform IDs (fbadid, gclid)
UTM Parameters
Custom Parameters

URL Length Considerations:

Keep URLs under 2048 characters
Use URL shorteners for long URLs
Prioritize essential parameters

URL Tagging

Use lowercase consistently for all UTM parameters
Follow standard naming conventions:

utm_source: Platform/origin (e.g., facebook, google, tiktok)
utm_medium: Traffic type (e.g., cpc, email, organic)
utm_campaign: Campaign identifier (e.g. summer_sale)

Document any custom channel requirements with the data team

Testing:

Verify parameters in analytics
Check attribution in test transactions
Validate against our test suite

Days to Conversion

We now track how long it takes customers to make a purchase after their first interaction with different marketing touchpoints. This helps understand the customer journey and conversion timeline across three key dimensions:

Marketing Channels (e.g., Meta, Google, TikTok)
Ad Creatives
Landing Pages

Key Concepts

Only calculated for successful purchases
Maximum tracking window of 120 days
Measures days between first valid touch and purchase
Independent tracking for each dimension
Automatically handles missing or invalid touchpoints

Business Value

This data helps answer questions like:
How long does it typically take for Facebook ads to drive purchases?
Do certain landing pages lead to faster or slower conversions than others?
Which marketing channels have the shortest time to purchase?

Limitations

Only tracks first touch to purchase (not intermediate touchpoints)
Requires at least one valid touch in the dimension being measured
Data older than 120 days is not included

Attribution Models

We apply various MTA models to attribute revenue and conversions across valid touch points:

First-Touch Attribution
- Assigns all credit to the first valid touch point in the purchase journey.
- Ideal for understanding initial customer acquisition channels.
Last-Touch Attribution
- Assigns all credit to the last valid touch point before the purchase.
- Useful for identifying conversion-driving channels.
Linear Attribution
- Distributes credit equally among all valid touch points.
- Provides a balanced view across the entire customer journey.

Each dimension (marketing channels, ad creatives, landing pages) has independent first-touch, last-touch, and linear attribution calculations.

Valid Touch Point Criteria

Marketing Channel Touch Points

Must Be: Non-purchase events with a mappable marketing channel.
Excluded: Google branded campaigns, (other), (none), and null values.

Ad Creative Touch Points

Must Have: A valid ad creative ID (numerical, at least four digits).
Excluded: Brand search-associated ads.

Landing Page Touch Points

Must Be: Non-purchase events with valid page paths.
Excluded: Checkout and order confirmation pages.

Reporting Capabilities

Our model provides detailed reports to help you analyze and optimize your marketing efforts.

Channel Performance Reports

Revenue Attribution: See how much revenue is attributed to each marketing channel.
Conversion Paths: Understand the sequences of touch points leading to purchases.
First vs. Last Touch Analysis: Compare the effectiveness of channels in initiating vs. closing sales.

Journey Analysis Reports

Journey Length Metrics: Analyze the number of touch points and time taken before a purchase.
Multi-Session Patterns: Identify whether purchases occur in a single session or across multiple sessions.
Cross-Channel Interactions: Understand how different channels interact throughout the purchase journey.

Future Enhancements

We are continually working to improve our MTA model. Upcoming features include:

Advanced Attribution Models: Time-decay and position-based models.
Custom Attribution Weights: Ability to assign custom weights to different touch points.
Enhanced Ad Creative Analytics: Deeper insights into the performance of specific ads.
Zero-Party Data Integration: Utilize customer-provided data for richer attribution.
Discount Code Attribution: Include discount codes as part of the attribution analysis.
Direct Ad Platform Integrations: Seamless data connections with platforms like Google Ads, Facebook Ads, etc.

MTA

​Overview

​Why is this a superior approach to MTA modeling?

​Architecture

​Data Sources

​Unified Purchase Journey Dataset

​Data Quality Assurance

​Key Concepts

​Purchase Journey

​Touch Points

​Valid Touch Points

​Lookback Window

​Dimensions

​Distinct Dimension Values

​Attribution

​Channel Classification Overview

​Channel Classification Detailed Logic

​Core Attribution Elements

​Common Implementation Methods

​Attribution Parameter Best Practices

​Days to Conversion

​Attribution Models

​Valid Touch Point Criteria

​Reporting Capabilities

​Channel Performance Reports

​Journey Analysis Reports

​Future Enhancements

Overview

Why is this a superior approach to MTA modeling?

Architecture

Data Sources

Unified Purchase Journey Dataset

Data Quality Assurance

Key Concepts

Purchase Journey

Touch Points

Valid Touch Points

Lookback Window

Dimensions

Distinct Dimension Values

Attribution

Channel Classification Overview

Channel Classification Detailed Logic

Core Attribution Elements

Common Implementation Methods

Attribution Parameter Best Practices

Days to Conversion

Attribution Models

Valid Touch Point Criteria

Reporting Capabilities

Channel Performance Reports

Journey Analysis Reports

Future Enhancements