There are several methodologies marketers use to ensure they can track, measure, and determine ROI as a result of their marketing efforts. A vital practice in today’s business landscape is data attribution, generally speaking, and multi-touch attribution, more specifically. Multi-touch attribution is a measurement practice astute marketers use to evaluate each touchpoint’s impact during the move toward conversion. With this method, the objective is to determine each touchpoint’s specific value during the buying journey.

While some marketing practitioners are well versed in multi-touch attribution, many others still struggle with its basic principles in addition to successfully implementing such a model. But don’t worry; we’ll cover what multi-touch attribution is, how it works, and how you can incorporate it as a vital marketing measurement tool.

What are the different multi-touch models?

When considering the benefits of multi-touch attribution, keep in mind there are several models to choose from. Each model is different based on how you might attribute the importance of a particular touchpoint. The following list defines which model is suitable for the type of attribution different organizations experience.

Linear

The easiest approach to incorporate is the linear multi-touch attribution model. In this model, equal weight is given to each touchpoint in the drive toward a final sale. In terms of equal weight, each touchpoint receives equal credit regarding revenue. An example of a linear multi-touch attribution model would be selling a pair of athletic shoes to a male in his early 20s. The customer saw an ad on Facebook, viewed his favorite athlete in action on the side of a bus, saw a television commercial, and received an email informing him of an upcoming holiday sale. Each of these touchpoints would receive equal credit in driving the young man to purchase a new pair of athletic shoes.

Time decay

The time decay model gives more credit to those touchpoints that are closer to the final sale. For the sale of an enterprise-level SaaS solution, where a customer received a cold call, visited the solution provider’s booth at a trade show, attended a webinar, and experienced a demo, those events later in the progression receive more sales credit. The cold call might receive 10% of the credit, the trade show booth visit 20%, the webinar receives 30%, and the demo gets 40%. The focus here is on continuous nurturing, so the final stages’ touchpoints are deemed more impactful than those earlier in the process.

U-shaped

The U-shaped multi-touch attribution model gives more weight to the first and the last touchpoints while evenly distributing credit to all other touchpoints in between. If there were a total of five engagements, the first and last engagements each receives 40%, while the remaining three engagements each received 6.66% of the credit. Situations where this might be more applicable might be a buying journey that places significant credit on the first and last touchpoints, such as inviting a prospect to an information dinner and, after several more interactions, offering a limited time discount for immediate purchase.

W-shaped

Like the U-shaped model, the W-shaped multi-touch attribution model focuses on three touchpoints: the first touch, the lead creation touch, and the opportunity creation touch. There may be additional touchpoints between those three, and if so, they each receive equal credit divided between them while the major three receive a total of 90% of the credit (each touchpoint earning 30% each).

Full path

The full path multi-touch attribution model is more complex than the other models we’ve discussed so far. It’s a close cousin to the W-shaped model because it gives equal weight to the same three touchpoints, plus one more in the form of the lead creation touchpoint. Each of the four touchpoints receive 22.5% while any remaining ones divide the remaining 10% evenly.

Custom

The most complex attribution model is the custom multi-touch approach. Rather than rely on a previously determined weight for each touchpoint, the organization incorporating the model develops its own formula to determine and weigh each touchpoint. This method might be best suited for sales cycles with longer timeframes and an account-based marketing approach where it may be difficult to ascertain which touchpoint has the most weight. While this approach can be highly effective, it does require an advanced marketing attribution solution to help devise the attribution strategy.

Algorithmic

No attribution model is as effective as the algorithmic multi-touch attribution model. Rather than assigning weighted credit to different touchpoints based on a predetermined or custom model, the algorithmic model details a prospect’s journey and assigns credit based on ever-changing data. That data is derived from the use of machine learning that collects and analyzes all information retrieved during the accreditation process. Because touchpoint performance can change, the algorithmic model offers great flexibility and is the most accurate methodology marketers can use for multi-touch attribution

Multi-touch vs. Single-Touch Attribution and which is better?

Amid all this talk of multi-touch attribution models, single-touch attribution models still hold value for many marketers. With single-touch models, 100% of the sales credit is given to a single campaign. For example, a single email campaign may be given all the credit for the revenue generated from a sale, although there may be additional touchpoints. While simple, it does not tell the whole story, and it may give more revenue credit to a touchpoint that, in reality, contributes less than other interactions.

Because the buyer journey includes multiple interactions with a given brand, the multi-touch attribution model is a better gauge of which touchpoints actually contributed to the sale. A custom multi-touch model might be the ideal method for properly attributing interactions, but even leveraging one of the other multi-touch attribution models allows marketers to consider other interactions as a contributing cause for a sale. Both approaches have their benefits. In fact, there are specific reasons why a marketer may choose to use a single-touch attribution model.

How does Multi-Touch Attribution work in the real-world?

In reality, a multi-touch attribution model can provide valuable insight and foster some predictability when considering which marketing campaigns to use to target a particular audience. When incorporating attribution models, it’s helpful to understand how they can be used in conjunction with tools that are likely already in place.

JavaScript Tracking

• When marketers use JavaScript in HTML source code to track traffic and understand behavior in conjunction with a multi-touch attribution model, valuable information can be gleaned and campaigns made more robust.
• A series of campaigns that engage an audience and incorporates automation works well with multi-touch attribution models.
• More specifically, a target audience member might receive an email sharing helpful information regarding a new product.
• Once that member clicks to learn more, the tracking code will automatically send a follow-up email regarding the new product.
• If the member continues to visit the website to learn even more, they might also receive an invitation to view a demo.
• Once the member engages with the company multiple times, partially due to the code that tracks their behavior, they may eventually move toward making a purchase.
• In this scenario, JavaScript was the tool used to nurture the prospect and facilitate additional touchpoints automatically. Each of the touchpoints would then be identified in the multi-touch attribution model.

UTMs/Cookies

• Like the use of JavaScript, UTM parameters and cookies may also be used, along with the multi-touch attribution model.
• As marketers facilitate additional opportunities for engagement, UTM parameters can provide invaluable information regarding which campaigns were more successful and the extent of each campaign’s reach.
• Cookies also play a vital role in helping marketers track when and where visitors engage multiple times.
• With this additional information, multi-touch attribution models can be developed and refined based on the data provided by UTM parameters and cookies.

Challenges with cookies in the post-GDPR and CCPA world

With the EU, and now the state of California, enacting data privacy laws, marketers may find themselves challenged by possibly losing the ability to track customer behavior online. The European General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) creates an uncertain playing field for marketers, as the last ten years have seen milestones in the ability to attribute customer interactions with revenue generation.

At the very least, marketers, and the organizations they work for, must understand each act’s requirements and understand whether they are in compliance. While this can mean significant changes for some attribution practices, it may also give rise to more innovative attribution practices and technology.

First-party cookies

It’s critical for marketers to understand the customer buying journey. The use of first-party cookies is an important part of that endeavor. While there may be some confusion regarding the use of first-party and third-party cookies, first-party cookies will be with us for the foreseeable future. 

Apple’s ITP is primarily designed to limit marketers ability to track users across domains via Safari using third-party cookies. Because first-party cookies are different, marketers will still be able to effectively track the customer journey by having first-party cookies placed by their browser. 

LeadsRx offers the ability to leverage its Universal Conversion Tracking Pixel to enable first-party cookies to be placed by a visitor’s server in addition to using a visitor’s IP address. The IP address option is ideal for visitors that clear their browser history. Either way, LeadsRx can help their clients stitch together a meaningful customer journey, while remaining compliant, regardless of the challenges and potential preventive measures introduced.

Difference between multi-touch and multi-channel attribution

There are similarities between multi-touch and multi-channel attribution, but it’s important to know the major difference between them. Multi-touch refers to the individual touchpoints, or interactions, a prospect might have with a brand during the buying journey. They can occur within a single channel or across multiple channels and provide a great deal of data. Multi-channel attribution, on the other hand, does not necessarily measure individual touchpoints in the same way. Rather, this measurement method considers specific channels, such as organic search, social media, paid search, etc., and assigns fractional credit for the conversion, no matter where each channel interaction occurred in the process.

How can marketers use multi-touch attribution data to make smarter decisions?

The use of multi-touch attribution models boils down to giving marketers data to make better decisions. There are multiple ways that such data can inform critical decisions. Case in point is the decision a marketing executive might make when facing the second half of a fiscal year and must allocate spend to increase sales by 35%.

Understanding that reporting from an attribution model will help guide where the budget should be spent, a decision may be made to spend more on bringing prospects to the point of requesting a demo (through email, social media, and paid search), as this has historically proven to be the most significant interaction that leads to sales.

The other touchpoints are valuable as well, but driving prospects to the demo likely provides greater opportunity for increased revenue during the last two quarters. While possibly not sustainable in the long run, it can serve a purpose in the short term.

Regardless of the objective, multi-touch attribution models provide the insight marketers need to make smarter decisions with their budgets and their employers’ bottom lines.