First Touch Attribution.
First touch attribution gives 100% of the credit to the first marketing effort that drove your client to your website. Since 100% of the credit is given to a single touchpoint, the model tends to overweight a single part of the marketing funnel.
In most cases, First Touch will provide more weight to Top-of-Funnel marketing channels that drive awareness.The model is also susceptible to errors from technological limitations.
For instance, website cookie time-out periods, which if your marketing cycle is longer than 90 days, may impact the data when it comes to “First Touch” drivers since the original First Touch attribution has expired.
The model is really attributing credit to the first touch within the cookie expiration life-cycle, rather than the actual first touch. As a single-touch model, First Touch is easy to implement but susceptible to single-channel bias and tech limitations.
Lead Creation Touch Attribution
The Lead Conversion Touch model is often mistaken for the First Touch model.
This is due to the website session where the lead was created is generally the first session where data is tracked. In a marketing analytics system designed for lead generation, like a marketing automation system, where the first touch was anonymous, it likely was never tracked and therefore does not exist.
The benefits of this model are that it helps your organization understand what marketing channels drive lead conversions. While it is important, it is just a small part of the customer journey. It may not actually track the full journey of the customer, particularly the Top-of-Funnel attention generating actions that lead to the lead creation session.
In a long B2B journey, there may be many marketing touch-points so lead conversion getting 100% of the attribution simplifies the marketer’s role in the customer journey.
Last Touch or Opportunity Attribution
This is the simplest attribution model for attribution systems to measure.
Makes sense right? By measuring the last touch with 100% of the conversion, you effectively have a one-touch attribution that leads to a sale. The last touch takes credit for 100% of a deal based on the last action in the marketing cycle.
In a long B2B buyer journey, the last touch to conversion can be much shorter than first touch or the lead creation touch. You have effectively cut out any value from the actions taken at top-of-funnel and lead gen marketing periods.
This means that you are not measuring the full customer journey, and it will be harder to justify the marketing expense of the earlier stages of the marketing funnel.
Last Non-Direct Touch
Slightly more useful than the Last Touch model as it eliminates the limitations of Direct data.
Direct Data is a pain. Traffic attributed to Direct is typically defined by marketing analytics as any time a visitor manually enters your URL. Just about every marketing analytics product Direct is counted as any visitor who does not have a referral source.
This means that data is prone to errors, where traffic from untagged, and commonly mistagged, social posts and ads is classified as Direct. Direct becomes the catch-all bucket for traffic that doesn’t fit into any of the other predetermined filters, and some of those that have been mislabeled due to bad attribution data.
Direct data is often misleading, which means you get a major benefit of Last-Non Direct Touch is that it avoids the trouble with direct touch attribution.
Last ‘Marketing Channel’ Touch Attribution
A channel specific attribution model, meaning search marketers will use Google Ads touch model. Social Media Marketers will use Facebook or Twitter Attribution models.
This is a platform specific, or rather, campaign type specific attribution model. The pros of this model is that they come standard with their parent channel — Facebook Insights, Google Ads Analytics etc.
The cons of the model is that they are extremely bias towards their own channels. If you use each model separately, then amalgamate them into a single report, you will likely have duplicate records that skew your conversion data.
For example, if a prospect clicks on a Facebook ad on Monday and then an AdWords ad on Tuesday and then converts, both Facebook’s Last Facebook Touch model and Google Ads’ Last Ads Touch model will claim 100% of the conversion credit.
Since it is just one sale, neither can be given 100% of the conversion credit but the data will need to be parsed through to understand this.
Linear attribution is a multi-touch model, and the simplest of these models.
It distributes credit by evenly applying credit to every touchpoint on a customer journey. Credit where credit is due, and while this does provide credit to every touch in a journey, since each interaction is not measured independently, it can be harder to discern what counts towards conversion at the various stages of the customer journey.
This model does not take into account the impact of different marketing touches. A prospect who spends time at one of your events, then goes to your site multiple times via Direct, the true value of the conference will not be recorded, since it gets a an equally distributed value with each Direct visit the prospect made to your website.
Simple to implement but harder to measure what touchpoint had the most weight for client conversion.
Time Delay Attribution
A time delay model is a multi-touch model that adds more weight to touchpoints closest to conversion. It makes the assumption that the closer to a conversion, the more importance that touchpoint had in the conversion.
This means that the model has a tendency to remove credit from top-of-funnel marketing efforts, as their weight is significantly lessened along the customer journey and they interact with marketing touches along the way.
Top-of-funnel becomes the least valuable touchpoint in this model, even though it was the initial touchpoint that kicked off the customer journey.
Position-Based Attribution (U-shaped) Attribution
What Google calls Position-Based Attribution, is a multi-touch model for marketing teams that focus on lead generation.
A model that tracks every touchpoint, but rather than distribute credit equally like the linear model, it emphasizes key touch-points: The First Touch and the Lead Conversion Touch. These two touchpoints get equal emphasis with 40% of the credit for the marketing effort, while the remaining 20% is split across any other touches on the customer journey.
While the model is good for lead conversion, it doesn’t consider marketing effort beyond lead conversion. This makes it an ideal model for lead reports and marketing orgs that don’t do prospect targeting beyond lead generation stage.
An extension of the U-Shaped model, which takes the model to the opportunity stage. For many organisations, this is the end of the marketing funnel.
The W-Shaped model followed the same attribution model as the Position-Based (U-Shaped) model with a different % split for credit for First Touch and Lead Conversion. First Touch, Lead Conversion, and Opportunity Creation receive 30% credit each, with the remaining 10% split equally across the remaining touchpoints on the customer journey.
In spreading the credit across this distribution, the W-Shaped model highlights three key stages of a customer journey, showing value across the life-cycle of the journey and showcasing the importance of each stage.
Full-Path or Z-Shaped Model
This marketing attribution model expands on the W-Shaped model beyond the opportunity stage. This model includes a fourth and final stage, the customer close.
Each of the 4 key stages is weighted equally at 22.5% of the credit and the last 10% is split equally among the remaining touchpoints. While it may seem like this is the “perfect attribution” model, being as it touches on the most stages of the funnel, it is really only appropriate for marketing to existing sales opportunities. More touchpoint attribution is not necessarily better.
This model requires extreme alignment between marketing and sales. Most sales orgs would prefer to create their own language/messaging for closing a deal and adding marketing as attribution to the close can lead to bad feelings if the teams are not in close alignment.