Understanding Marketing Attribution Models: A Simple Guide for Marketing Directors
March 2, 2018 | Robbie Richards
As a marketer, you need to know one thing: Are your campaigns actually driving revenue?
If you can’t answer that, you’ll struggle to make the right campaign optimizations and push leads further down the funnel.
While vanity metrics like open rates and social shares are super easy to find, the important data — data that tells you which touchpoints assisted vs. closed the sale — is less obvious.
The modern marketer’s technology stack removes the need for analytics guesswork. With a complete map of the journey from anonymous user to loyal customer, you can look at the impact of each tweet, blog post, and PPC ad, then double down on what works, and cut the budget burners.
But here’s the problem:
There are a lot of different ways to assign value to campaigns and touch points. Is the first touch (like clicking a facebook ad) more important than the webinar that generated the sign up? Or, should credit get divided up equally across each touchpoint? Maybe a graduated scale that assigns more value to the touchpoints closer to the point of conversion?
In this article, we’ll review the basics of why attribution matters, and evaluate seven different marketing attribution models by looking at how they work, the pros, cons and best use cases for different campaign types.
- First-Touch Attribution
- Last-Touch Attribution
- Linear (Even Weighted) Attribution
- Time-Decay Attribution
- U-Shaped/ Position-Based Attribution
- W-Shaped Attribution
- Algorithmic Attribution
Why Attribution Matters for Marketing Directors: High-Level Overview
Marketing Directors are confronted with these questions every single day, and the answer becomes more complex as the multichannel B2B buyer journey evolves.
While adoption of marketing attribution climbed 26% between 2016 and 2017, eConsultancy reports less than a third of organizations use a defined attribution model across their marketing campaigns.
So, it comes as no surprise that 43% of company’s list “measuring ROI” as their top marketing challenge. Traditional attribution methods don’t paint the full picture; in a MMAGlobal case study, researchers found there is almost no correlation between click-through rate and sales. It’s too simplistic.
According to an iProspect report, implementing marketing attribution in your organization will increase revenue by 15-35%. So, what is the hold up? The simple answer is most marketers don’t have a clue.
This leaves the field wide open for teams who are ready to step up their game. And, considering that you can do marketing attribution with the same tools you already use, there really aren’t any excuses.
They key is finding the best model for you. We’ll review seven different types that span from the simplest to the more complex.
But first, a full definition.
What Is Marketing Attribution?
Every sale has a journey and a story. Marketing attribution reveals that story. It takes you beyond vanity metrics, and gives you real answers: Where did the lead first meet your brand? What prompted them to buy, and what happened in between?
Let’s say you released an ebook last month. You promoted it with some social PPC, and sent it to your blog subscribers. You see that your overall conversion rate has gone up, which increased revenue, but which part of the campaign drove the best results?
What if you’re a business investing a lot in top funnel social media and display ads designed to drive brand awareness and direct traffic to your site? After being exposed to Google display ads, Facebook video ads, a buyer types your domain into search bar, navigating to the site and converting. Does all the credit go to search traffic? How much credit goes to the display and social ads?
Marketing attribution answers that question.
It helps you map the journey from lead to customer, taking into account every touch point and interaction along the way. This detailed insight shows you exactly what works and what doesn’t — maybe your social campaigns fell flat while your email marketing is responsible for 80% of the results. In that case, it’d make sense to go all in on email marketing and stop spending on social. Without attribution you’d never know that.
Multichannel Marketing Attribution: Giving Credit Where It’s Deserved
84% of companies use more than one marketing channel, but how many are measuring the impact of each channel at different stages of the funnel?
The last click (like an email with a link to a discounted product) is very unlikely to be the sole driver of revenue. Even though it looks like you made “$20,000 with one email”, studies show it takes several different digital touchpoints to make a sale.
Below is a screenshot of an assisted conversion report from Happy Herbivore, generated to find out why direct sales dropped even though Facebook revenue increased:
Happy Herbivore found Facebook’s assisted conversion value was actually higher than its direct selling power by almost double.
This is just one example of the kind of insight you’re missing by neglecting marketing attribution. Consider the sheer number of permutations in a customer journey — blog posts, PPC ads, ebooks, webinars, email and dozens of other channels — a lead’s path to conversion could go a million different ways.
It’s time to get started with marketing attribution. Below, you’ll learn the seven attribution models you can use to get deeper performance insights across your marketing campaigns, optimize your budget, and get rid of the anchors.
Types of Marketing Attribution Models
The godfather of web analytics, Avinash Kaushik said it well:
“There are few things more complicated in analytics (all analytics, big data and huge data!) than multi-channel attribution modeling.”
In this section, you’ll learn about the different kinds of marketing attribution models (single and multitouch) you can use to demystify the customer journey, and give campaign credit where it is deserved.
We’ll use Google Analytics as the example tool because it’s free and has great online documentation. Goals are a prerequisite for attribution, so be sure you have the necessary goals set up in order to accumulate enough data to make an informed decision on the best model for your business.
Log into your Google Analytics account and click Conversions in the sidebar, then Goals. Goal configuration is a topic best reserved for another article. Click here for a guide to learn more about setting up your goals in Google Analytics. With that configured, you can use the Model Comparison Tool to compare the attribution models we’re about to look at. Alright, now that we’re all set up we can jump into weighing the pros and cons of each marketing attribution model.
First-Touch Attribution Model
How It Works:
First touch attribution gives 100% of the credit to the first touch point in the journey. If a customer signed up for a webinar after reading an article they landed on from organic search, then did a quiz they saw in a nurture email, then converted at the end of the quiz, all the credit would be given to the first touchpoint — the webinar.
The logic with this model is simple: no sale ever gets made if a business doesn’t know you exist.
This model is not widely used, especially in the B2B space where the first touchpoint is usually several steps away from the point of conversion.
Easy to set up, and helpful for marketers who are solely focused on demand generation and brand awareness. First-click attribution is also very simple track as there are no calculations or arguments around weight distribution.
The last-touch attribution model omits the impact of any campaigns beyond the first touch. First touch attribution is like crediting a first date with a marriage. It only tells part of the story. It only tells you part of the story.
This model tends to overvalue top-of-the-funnel channels. Almost every customer will have multiple touchpoints across different channels before converting. These nurturing touchpoints often impact the conversion decision more as people move further down the funnel.
The biggest downfall — marketers find first-touch attribution limiting when trying to optimize or demonstrate the value of their efforts.
Best Use Case:
The most appropriate use case for first touch attribution is demand generation. It doesn’t give you a full overview of the journey, but it does show which first touches lead to sales, which is useful for measuring the most effective top-of-the-funnel marketing campaigns.
Last-Touch Attribution Mode
How It Works:
Last-touch attribution is the second of the single touch attribution models. It is commonly used, and the most popular model on the list. It is also the default attribution model in your Google Analytics account.
It has the simplicity of the first-touch model, but instead shifts all the credit to the final step in the conversion path. It focuses on the last thing that triggered the conversion while ignoring the path up to that point.
For example, a retailer might see that one of their non-brand AdWords campaigns is converting at 0.5:1. At face value, it seems like one of those “budget burners” we mentioned above. It has a negative ROI, right? A lot of businesses will jump to reallocate that budget into other seemingly higher converting campaigns.
What you have to consider is that a lot of people who were unfamiliar with your brand probably did a search for a keyword related to your product or service, saw your ad, clicked on it, and then returned to search where they later clicked a branded ad and converted.
In this case, the branded AdWords campaign is the only one that would receive credit for the conversion.
It highlights the channels that directly lead to revenue. If conversions are the primary campaign goals, the last-touch attribution model is one the most useful to use.
It’s simple to use, and easy to set up. As mentioned above, it’s the default Google Analytics attribution model. So, if you haven’t given any thought to your model up until this point, it’s the model you will be using by default.
Last-touch attribution ignores all the steps that were taken up until the point of conversion. All the nurturing emails, retargeting ads, SEO and content marketing efforts could receive no credit, even if they did in fact play a part in the customer journey.
Every customer journey is different. There are countless ways of getting to the point of conversion. Similar to first-touch, this model fails to paint an accurate ROI picture.
Best Use Case:
Last touch is best for conversion-focused campaigns. For example, when comparing a set of landing pages, you could use last touch attribution to figure out which page has the most persuasive copy.
Linear (Even Weighted) Attribution Model
How it works:
A linear attribution model divides credit equally between each touchpoint. The first touch, last touch, and any intermediate events are all treated with the same importance. With linear attribution, a journey with 10 touches would give 10% of the credit to each; a journey with five touches would credit 20%.
The biggest challenge with linear attribution is deciding how much attribution each touchpoint in the buyer journey deserves. The easiest answer to that question is the linear model: give them all the same amount of credit.
Linear attribution is a step up from first or last touch attribution because it assigns equal importance to every touchpoint, giving marketers a better idea of what happened in the middle of the journey. The middle stages might be just as vital for revenue as the first touch, and by using linear attribution you are able to see patterns that were otherwise hidden. You are now getting a more complete view of the entire story.
A linear model can also be a useful benchmark to compare against other models, and it’s not tricky to set up. Unlike other models which may need calculation and discretion, in a linear model there’s no confusion over which touches should be credited.
Not all touchpoints are created equal. The linear model is idealistic — it’s not possible that every touch truly contributed the same amount towards the sale. Is webinar attendance just as important as a Twitter like? What about a demo request generated by a branded PPC ad vs. a welcome email open?
The linear model can still be inaccurate. You know there are certain touchpoints that impact conversions more than others. So, more credit should be given where it is deserved.
Best use case
Holly Chessman, VP of marketing at Glance, uses a linear attribution model because Glance is a B2B company with a long sales cycle, and sales generally aren’t closed on through the website. With a small marketing team, Holly says it’s important for Glance to credit every area that brings in leads. A linear method works well in this case because it accounts for every middle touch.
In combination with first and last touch views, the linear attribution model can help fill in the blanks. You get a more complete picture without having to build a complex, algorithmic model. Apply this model to campaigns which direct leads to several similar touchpoints, but be aware that in the real world it’s impossible for every touchpoint to have the exact same impact.
Time Decay Attribution Model
How it works
Time decay attribution assigns more credit to touchpoints closer to the point of conversion. It’s a multi-touch model that aims to acknowledge different touchpoints along the customer journey have different value.
It makes sense that recent interactions are worth more because each touchpoint brings a prospect closer to conversion. The latter touchpoints are typically representative of the middle and bottom of the funnel.
But, it is not 100% accurate either. For example, if someone signs up for a 60 min product demo the week before they purchase, but click a link to a blog post in your email the day before they buy, should that blog really get more credit than the demo? Probably not.
A time decay attribution model enables marketers to optimize touchpoints that lead to (and directly result in) conversion. Unlike single-touch attribution models, time decay looks at the entire journey and attempts to weight different touchpoints based on the proximity to conversion. Typically, the latter touchpoints have a great impact on conversion, and this model attempts to account for that.
Early touches can still be incredibly influential. Perhaps a lead signed up for a top-of-the-funnel webinar that ended in a hard sell, therefore pushing the lead further down the funnel than the analytics imply. Is it really fair to devalue that initial touch? When relying on time decay attribution, these are circumstances you need to consider.
Best use case
Time decay attribution can work well for B2B businesses with longer sales cycles, but also works for timed promotional campaigns because it rightfully devalues the early parts of the campaign that didn’t convert the lead.
Marketers who believe their campaigns are bringing in high quality traffic, but are seeing low overall conversion rates should consider time decay attribution because it focuses on optimizing the later stages of the funnel.
U-Shaped / Position-Based Attribution Models
How it works
The U-shaped attribution model assigns 40% credit to the first touch and 40% to the last touch, then divides 20% evenly between every middle point. It can be customized by applying your own weighting to the middle 20% if you know that one or more intermediate touches are more likely to contribute to conversion, but the main focus is: “where did your customers find you?” and “what made them buy?”. It addresses a lot of the weaknesses from the other models.
If you believe, as a lot of marketers do, that the first and last touchpoints are the most valuable, this model is for you. However, keep in mind that position-based models still have flaws. For example, all the touchpoints in the middle of the customer journey may not actually be created equal. Each one will likely have a different impact on the final conversion.
The company found out that organic search was $5.1M more valuable. If you’re the SEO manager, you just started looking a lot better in the monthly management meetings 🙂
A U-shaped approach is less heavy-handed than first or last touch attribution, but still places importance on the initial interaction and the final point of conversion. The middle stages also get credit but the credit is equally divided, similar to a linear model.
The initial interaction a lead has with your organization could be a vital first impression, and the last touch is important because it is directly related to conversion.
Josh Spilker from Workzone uses U-shaped attribution to measure the effectiveness of campaigns that start with PPC ads and end with product tours or pricing page views; it makes sense to use U-shaped attribution in this case to optimize both PPC spend and sales-focused touches.
Depending on the campaign, the first and last touches could be unimportant. Perhaps word of mouth or an offline event invisibly led to the first touch. You should also consider whether the first interaction really deserves the same amount of credit for the sale as the last point of conversion.
Best use case
A U-shaped model works best for campaigns that aren’t designed to nurture leads over a long period of time. If lead nurturing is an important part of the buyer journey, this model undervalues the middle stages of your campaign.
W-Shaped Attribution Model
How it works
The W-Shaped attribution model still assigns the most weight to the first and last touchpoints, similar to the U-Shaped model. However, it also gives more credit to the point where a prospect converts to a lead. This event usually takes place somewhere in the middle of the customer journey, hence the “W”). The rest of the credit is divided evenly among the other mid-funnel touchpoints.
Essentially, this model attributes the most value to three main customer journey stages — visit, lead, and opportunity/sale. The three touchpoints receive 30% credit each, and the last 10% is split across the others.
It gives credit to all the touchpoints, while placing higher weighting on key action-based events.
It’s more complicated to set up, and still tends to undervalue other touchpoints outside of the first, last, and middle touchpoints.
Best use case
This model will work well for B2B companies with longer sales cycles. It goes another level deeper than the U-Shaped model, and should be used across multitouch campaigns where there will be important lead conversion events in the middle of the buyer journey.
Algorithmic Attribution Model
How it works
Algorithmic attribution refers to a custom model unique to each business. It is one of the most advanced ways to model attribution data in order to most accurately represent the buyer journey. According to a report by AdRoll, 96% of respondents indicated algorithmic attribution was at least somewhat effective. This is much higher than any other model. Each channel and touchpoint is assigned a custom weighting to represent its perceived importance. Depending on the complexity of the software you use, this could be calculated with machine learning, or input manually.
Fresh data should be used as you engage new prospects and customers in order to refine the model. This is where the machine learning element comes into play.
Every other model described in this post requires you to make some pretty bold assumptions about your campaign. With algorithmic attribution, you can assign extra weight to interactions that you know are more important, not just touch points that happened first or last.
Algorithmic attribution models give B2B marketers the most complete view of the customer journey. While other advanced models like W-Shaped do come close to assigning the proper weighting to key touchpoints, they are not as accurate as a model driven by historical customer data.
Algorithmic attribution is as powerful as you make it, but that obviously involves a lot of a custom configuration and maintenance. In some cases, it might require the skills of a data scientist, or, at the very least, a fresh manual configuration for each campaign.
How to set it up
Algorithmic attribution is a feature available in Google Analytics 360 (where it is called data-driven attribution). It is also offered with SAS Customer Intelligence 360. These high-end tools are extremely powerful, but their pricing positions them as an enterprise solution.
Best use case
Algorithmic models are the most flexible and accurate approach. For example, you know that your campaign includes a very important landing page in the middle stages, so you can assign that stage a higher percentage of the credit; the nuances of that strategy would be lost on a model like first touch attribution, but can be handled elegantly with an algorithmic approach.
5 Tips for Choosing the Right Marketing Attribution Model for Your Business
In the wise words of SAS Principal Solutions Architect Suneel Grover:
“There’s really no reason to believe that any single weighting system somehow captures accurately the right credit for any given sequence of campaigns and there’s every reason to think that the credit should vary depending on the order, time and nature of the individual campaigns.”
As we saw above, you can get wildly different campaign insights depending on the model you choose. There’s no one-size-fits-all answer here, but after asking yourself a few important questions the choice will become a lot clearer.
Here are some tips to guide you in the right direction:
Look at the average number of touches prior to conversion. The more touchpoints, the more nuanced the model. A last touch model would leave a lot of questions unanswered and be unhelpful for optimizing the nurturing phase of longer B2B sales cycles. However, for a direct response campaign the last touch model would work.
Consider the length of time it takes for a lead to convert. Simple models like linear and first touch don’t tell the whole story for long sales cycles; the longer it takes for a lead to convert, the more likely it is that the journey is complex and might involve multiple revisits, competitor evaluations, and retargeting months down the line.
Look at the number of active channels across your sales cycle. Multichannel reports give you a clue as to the timing, length, and path trends of your campaigns, which in turn inform your choice of model. The fewer channels involved in a conversion, the simpler the model you can use. Complex paths over long periods of time may well require algorithmic (or at least W-shaped) attribution models.
Define what you’re trying to measure. Demand generators care about the marketing efforts that bring the most leads in through the door, so a first touch model is ideal. For conversion marketers, however, first touch alone would be unwise because it doesn’t credit the touch that converted the lead. Your goals and desired insights define the “right” model for the use case.
Compare, test, and optimize. There’s nothing stopping you from building a model based on a combination of attribution models. Ultimately, a correctly-configured custom or algorithmic model gives the best insights, and the more you test and optimize the closer you get to attribution utopia 🙂
Which Marketing Attribution Model Are You Going to Use?
Which attribution model should you use? As with most things, there’s no clear cut answer to that questions.
As we saw above, there are a lot of factors – customer journey, number of channels, length of the sales cycle, and overarching campaign goals — that will dictate the best aatribution model for your campaign.
For B2B marketing teams dealing with longer multi touch sales cycles, an algorithmic attribution model is going to be the most powerful and flexible. But, the amount of data and expertise it requires puts it out of reach for many small-medium sized businesses. A solid alternative would be looking at a W-Shaped, U-Shaped or Time Decay model to more accurately assign weighting across each of the customer touchpoints.
Single touch models like first-click and last-click will be better suited for shorter buying cycles with few touchpoints, as well as brand awareness and direct response campaigns.
Many tools — Google Analytics included — allow you to quickly switch between different views and build a detailed picture of your conversion paths. Over time you’ll identify which models assign credit where it is deserved, and allow you to make the smartest analytical decisions that will impact your bottom line. Until then, keep experimenting.