Multi-Touch Attribution and Models: A Complete Guide [2025]
![Multi-Touch Attribution and Models: A Complete Guide [2025]](https://be.trueprofit.io/uploads/Multi-Touch-Attribution.svg)
- Lacking in Offline Metrics: MTA is frequently used for campaigns that make use of digital marketing platforms since it tracks consumer behavior (e.g. clicks). This makes it difficult for these models to include offline data, like exposure to a print or television ad.
- Data munging: These models are all limited in their ability to fully reveal the consumer journey. For the most accurate insights, you must therefore use numerous models and then correlate the data from each.
- Poor awareness of outside factors: MTA considers user-level information. Therefore, you lack visibility into external phenomena, such as seasonality, that could influence marketing efforts and conversions without using aggregate data.
- Complexity: Single-source models are much simpler than MTA models. These models may be challenging to set up, administer, and analyze due to their complexity, particularly for businesses lacking sophisticated analytical capabilities.
- Huge data requirements: To be effective, MTA models need a lot of data from several channels. To ensure that all the data from various channels can be effectively integrated and analyzed, you must not only be able to acquire this amount of data but also have reliable data integration methods.
- Possibility of overvaluing less important touchpoints: MTA models run the danger of overvaluing less significant interactions because they spread credit among all touchpoints.
- Time and resource burden: MTA models can be time and resource-consuming because of their complexity and data requirements.
- Simple and easy to implement
- Ensures all interactions are recognized
- May misrepresent the true impact of each marketing effort
- Doesn’t account for varying influence of touchpoints
- Native ad on a website: 5%
- Email marketing: 10%
- YouTube commercial: 20%
- Sponsored promotion: 50%
- Ideal for businesses with longer sales cycles
- Reflects recency bias in customer decision-making
- May not be accurate for businesses with slow decision-making processes
- Undervalues early-stage awareness efforts
- Display advertisement: 40%
- Native ad: 10%
- Online commercial: 10%
- Sponsored promotion: 40%
- Balances awareness and conversion efforts
- Recognizes the role of initiating and closing interactions
- May not suit complex customer journeys
- Doesn’t fully account for mid-funnel activities
- Television commercial: 30%
- Display advertisement: 5%
- Native ad (lead generation): 30
- Email campaign: 5%
- Sponsored promotion: 30%
- Highlights key conversion events
- More balanced than the U-shaped model
- Can be complex to track and implement
- Still undervalues some touchpoints
- First touchpoint: 22.5%
- Lead generation: 22.5%
- Opportunity creation: 22.5%
- Final conversion: 22.5%
- Other touchpoints: 10%
- Comprehensive view of the marketing funnel
- Helps businesses optimize each stage of the customer journey
- Requires advanced tracking tools
- Data-heavy and complex to implement
- Most accurate for unique customer journeys
- Fully customizable to business needs
- Requires sophisticated analytics
- Can be difficult to set up and refine
- What is the average sales cycle length, and how does it vary by channel?
- Which marketing channels drive the most conversions?
- Are there any recurring barriers to conversion?
- How do different channels influence customer engagement?
Tracy Ng
Senior Content Executive at TrueProfit & SEO/Content Specialist
Tracy is a senior content executive at TrueProfit – specializing in helping eCommerce businesses scale profitably through content. She has over 4 years of experience in eCommerce and digital marketing editorial writing. She develops high-impact content that helps thousands of Shopify merchants make data-driven, profit-focused decisions.
Tracy is a senior content executive at TrueProfit – specializing in helping eCommerce businesses scale profitably through content. She has over 4 years of experience in eCommerce and digital marketing editorial writing. She develops high-impact content that helps thousands of Shopify merchants make data-driven, profit-focused decisions.
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