Editor's Note: Updated 3/26/19
Multi-touch attribution is a broad discipline that includes multiple methods and models. Knowing which model is right for your business needs depends on what you want to do with output.
This post explains the fundamentals of multi-touch attribution, highlights the differences in available methods and models, and helps you choose the right model for your business.
What is Multi-Touch Attribution?
Multi-touch attribution is an advanced way of measuring marketing effectiveness that accounts for the cross-channel consumer journey. It replaces traditional, rules-based attribution approaches such as first- and last-touch, which give all of the credit to the first or last marketing touchpoint before the consumer converts through a purchase, download, or other conversion event.
By arbitrarily applying rules, these methods fail to accurately measure the contribution of every touchpoint in the consumer journey, causing marketers to make decisions based on biased data.
Multi-touch attribution eliminates biases by algorithmically allocating credit to every element of every touchpoint in the consumer journey, across marketing and advertising channels and tactics, according to its influence on driving a conversion event.
Leveraging individual, user-level data across addressable channels such as direct mail, online display and paid search, multi-touch attribution calculates and assigns credit for a KPI event to the marketing touchpoints along the consumer journey that influenced a desired business outcome.
These insights into past performance can also be used to inform more efficient and effective planning and optimization decisions for future campaigns, as well as those already in flight.
There are Different Multi-Touch Attribution Models
While there are many different types of multi-touch attribution models, the level of sophistication of each model can differ dramatically.
- Rules-based methods are subjective, relying on people to define the rules of how credit is allocated to one or more points in the consumer journey.
- Algorithmic methodology is based in objective, statistical modeling and machine-learning techniques. The output of an algorithmic model can be used to predict outcomes to help marketers plan or optimize future marketing efforts. But even algorithmic models vary in terms of their modeling sophistication and granularity of predictions.
No matter the method, all multi-touch attribution models help marketers determine the impact of their marketing investments at granular levels by assigning credit for a KPI event (e.g., conversion, lead, etc.) to one or more touchpoints.
Read on for an overview of attribution models:
Rules-Based Single-Touch Attribution:
- Last Touch: In a last touch model, the last touchpoint in the consumer journey receives 100% of the credit for the KPI event. Due to its history as the default marketing measurement methodology, this model is often used as a baseline to compare other multi-touch attribution models.
- First Touch: In a first touch model, the first touchpoint in the consumer journey receives 100% of the credit for the KPI event. This model is often used to measure marketing efforts that are intended to drive awareness by reaching new consumers for the first time.
Rules-Based Multi-Touch Attribution:
- Even Weighting: In an even weighting model, also known as a linear model, all touchpoints in the consumer journey receive equal credit for the KPI event. This model is often used to measure marketing efforts that have long consideration cycles, thus making it important to reinforce the message many times throughout the consumer journey. It is also a good starting point for marketers that are new to multi-touch attribution and do not have an existing understanding of the typical consumer journey across channels.
- Position-Based: In a position-based model, also known as a bathtub or u-shaped model, the first and last touchpoints in the consumer journey receive a higher percentage of credit than the touchpoints in between. This model is often used when marketers want to favor the “Opener” and “Closer” touchpoints more heavily than the “Supporter” touchpoints in the middle of the consumer journey.
- Time Decay: In a time decay model, the percentage of credit gradually builds while leading up to the last touchpoint in the consumer journey. This model is often used to measure marketing efforts that have short consideration cycles, such as promotional campaigns.
Algorithmic Multi-Touch Attribution:
- Fractional Attribution: Fractional attribution employs machine-learning to calculate and assign fractional credit for a given success metric to the influential marketing touchpoints and dimensions (campaign, placement, publisher, creative, offer, etc.) along the consumer journey, and to inform future marketing spend allocations. Nielsen Visual IQ's algorithmic multi-touch attribution model, TrueAttribution, is a multi-dimensional modeling technique that models for every dimension of every touchpoint before assigning fractional credit. Not only can marketers use this model to objectively measure marketing performance, but they can also use our scenario planning interface to predict outcomes at the most tactical levels of their marketing and media plans. That means, not only can they understand optimal budget allocation at the channel and sub-channel levels, but also the most granular levels like keyword, placement and creative. Models are rebuilt as often as daily, so marketers can make optimization decisions while campaigns are still in flight.
Which Model is Best for You?
At a time when marketers are increasingly held accountable for demonstrating marketing success, any of these models is better than siloed channel measurement or no measurement at all. These models will arm you with insight into the consumer journey that you didn’t have before.
Ultimately, deciding which multi-touch attribution solution is right for you comes down to your goals, business requirements and how you want to use the output to improve the effectiveness of your marketing.
Download this ebook to learn more about attribution: Untangling Attribution’s Web of Confusion: A Primer for Marketers.
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