What is Multi-Touch Attribution?

Multi-touch attribution is a method of marketing measurement that evaluates the impact of each touchpoint in driving a conversion, thereby determining the value of that specific touchpoint.

Today’s buyer journeys span many devices and touchpoints before resulting in a conversion. In order to optimize campaigns and create more customized consumer experiences, marketers need to understand which touchpoints and messages a consumer came in contact with that resulted in a positive action.

The goal is to understand where to devote spend, devoting funds to similar campaigns and diverting funds from those that were ineffective.

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For example, let’s say that a consumer is considering purchasing a new pair of shoes. After doing some research, they are targeted by ads from Nike. First they see a display ad, which they ignore. Next, they see a native ad on their Instagram feed that catches their attention and drives them back to the Nike site. Finally, they get a promotional offer via email with a discount code that causes them to purchase.

Each of these ads represents a touchpoint in the buyer journey. Multi-touch attribution (MTA) allows marketers to look at the native ad and the email campaign and attribute the sale to those efforts. They can then also note that the display ad was ineffective and shift away from that tactic.

There are several multi-touch attribution models marketers can rely on that look at user-level data, i.e., the effect that user-level events (clicks, impressions) have on the ultimate goal. Each of these models scale ad effectiveness differently.

Benefits of Multi-Touch Attribution

Multi-touch attribution models have become important for marketers, especially those looking to measure the impact of digital campaigns. This is because they provide a more granular, person-level view than traditional aggregate methods such as media mix modeling.

One of the top benefits of multi-touch attribution is that it provides visibility into the success of touchpoints across the entire buyer’s journey. This is critical, as consumers are becoming increasingly adept at avoiding marketing messages. Marketers must utilize data-driven marketing to customize their messaging to meet consumers on the right channel at the right time. The granular data offered by multi-touch attribution enables this, helping marketers to identify audiences across channels and determine those users’ specific marketing desires.

In addition to helping marketers improve the consumer experience, multi-touch attribution also helps marketers to achieve higher ROI for their marketing investments, illuminating where spend is most and least effective. This can also help to shorten sales cycles by engaging consumers with fewer but more impactful marketing messages.

Common Multi-Touch Attribution Models

It is important to note that not all attribution models are multi-touch. This only refers to models that evaluate and weigh the impact of several touchpoints.

For example, the first touch and last touch models are forms of single touch attribution. This is because they only factor in either the first or last touchpoint that was encountered before a conversion, rather than every touchpoint engaged with throughout the sales cycle.  

First-Touch Attribution: This model gives full sales credit to the first marketing touchpoint interacted with before conversion.

first touch attribution model

Last-Touch Attribution:  This model gives full sales credit to the last marketing touchpoint interacted with before conversion.

last touch attribution model

There are several popular multi-touch attribution models that look at each touchpoint engaged with before conversion. The key difference between these models is how much sales credit they ascribe to each touchpoint based on sequence, etc.

Linear Attribution: This gives each touchpoint across the buyer journey the same amount of credit toward driving a sale. In our previous Nike example, the native ad and the email would each earn 50 percent of attribution credit.

linear attribution model

U-shaped Model: This model attributes 40% each to the first touchpoint and lead conversion touchpoint. The other 20% is divided between the additional touchpoints encountered in between.

U-shaped attribution model

Time Decay Model: This model gives more credit to the touchpoints a consumer interacts with closer to the conversion. In our Nike example, perhaps 40% of the credit would go to the native ad, while 60% would go to the email promotion.

time decay attribution model

W-shaped Model: This model credits the first touch, lead conversion, and opportunity creation with 30% of the credit each. The remaining 10% is divided among additional engagements.

w-shaped attribution model

Full Path Model: Full path is a highly technical and sophisticated model. Full path follows a similar sequence as the W-shaped model but incorporates an additional touchpoint—the customer close touchpoint. 22.5% of the credit is given to each of these key touchpoints: first touch, lead creation, opportunity creation, and close, with 10% going to any additional touchpoints.

full path attribution model

Custom Model:  A custom model is created by the organization and allows them to weigh the significance of each touchpoint based on their own rules.

The Difference Between Multi-Touch & Multi-Channel Attribution

While multi-touch and multi-channel attribution are often used interchangeably, there are some notable differences between these two models.

Multi-channel attribution weighs attribution credit by channel (social, paid, organic, etc.). It does not factor in specific touchpoints, messaging, or sequence.

Multi-touch attribution is more granular, focusing on specific ads, including which channel they ran on, the message, and the sequence of interaction.

Challenges of Multi-Touch Attribution

While multi-touch attribution models provide more insights than other marketing measurements, they are not perfect. As marketers aim to perfect their attribution measurements, there are a few main challenges they face.

Lack of Offline Metrics: MTA is largely used for campaigns that employ digital marketing platforms, as it measures consumer actions (clicks, etc). This makes it challenging for these models to incorporate offline data, such as exposure to a TV or print ad. However, these can be crucial components of the consumer journey.

Data Wrangling: None of these models can provide full visibility into the customer journey. Therefore, marketers will have to employ multiple models, then correlate the data from each for the most accurate insights. The volume of data and complexity of the models then pose a challenge, with many marketing analysts spending more time aggregating this information in a digestible way than deriving meaningful insights from it. The need to use multiple attribution models exacerbates this challenge.

Lacks Visibility into External Factors: Unlike media mix modeling, which looks at aggregate data, MTA looks at user-level insights. Without incorporating aggregate information, marketers do not have visibility into external trends that might affect marketing efforts and conversions, such as seasonality.

Multi-touch attribution models are necessary but incomplete on their own. This is why marketers must employ unified marketing measurement. Unified measurement combines the person-level data offered by MTA with the aggregate data of media mix modeling for a comprehensive view of marketing engagements and trends.

Where to Use Multi-Touch Attribution

Multi-touch attribution models should be applied to campaigns that are based on digital spend, such as email or online paid advertising that run across multiple channels and devices. Additionally, marketers must be able to tie an individual to the marketing event.

Insights from MTA can also be applied to automation platforms for tasks such as email deployment.

Finally, MTA data is a crucial part of all optimization and A/B testing efforts. This data will show the messages that resonate with your consumers on which channels and at which times, allowing marketers to optimize campaigns and messaging to meet those needs.

Steps to Implement Multi-Touch Attribution

Marketers looking to deploy multi-touch attribution models should consider the following steps for the most effective results:

  1. Determine the Models and KPIs: Marketers need to determine which attribution models to employ based on their organizational goals. When choosing the models, considerations such as length of sales cycle and types of campaigns should be factored in. From there, marketers must select their KPIs. These will be the metrics by which to measure success or failure. Marketers using MTA are typically trying to improve ROI and user experience, so these metrics must reflect what this means for the specific organization.
  2. Establish the Team: Next, marketing teams must align with key team members and stakeholders. This will of course mean coordinating with skilled marketing analysts, but also budget stakeholders and creative teams in order to optimize messaging based on insights.
  3. Deploy a Marketing Analytics Software:  When working with multiple complex attribution models, an advanced analytics software is necessary to normalize and correlate the data into digestible metrics from which insights can be derived. This platform should offer person-level, granular data, as well as other insights that might indicate the motivation behind a conversion, such as brand equity or effective creative.
  4. Apply Insights:  Once marketers feel they have a complete understanding of how their campaign has performed based on the data provided by their attribution model, they can apply those insights in real time to begin course correcting, allowing them to create a more customized experience.
  5. Continue to Optimize & Test: This should not be a one-time exercise. Rather, marketers should be continuously evaluating their MTA data to optimize and test campaigns. Regular optimizations and tests will enable marketers to determine the best strategies and marketing sequences to reach their consumers at the right time.

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