So the first step is to understand what marketing attribution model is and why it is important to know the results of your digital content distribution.
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 e-book last month. You promoted it with some social Pay-Per-Click, 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.
Attribution modeling is a framework for analyzing which touchpoints, or marketing channels, receive credit for a conversion. Each attribution model distributes the value of a conversion across each touchpoint differently.
A model comparison tool allows you to analyze how each model distributes the value of a conversion. There are six common attribution models: First Interaction, Last Interaction, Last Non-Direct Click, Linear, Time-Decay, and Position-Based.
Attribution modelling is a framework for analyzing which touchpoints, or marketing channels, receive credit for a conversion. Each attribution model distributes the value of conversion across each touchpoint differently.
A model comparison tool allows you to analyze how each model distributes the value of a conversion. There are six common attribution models: First Interaction/first-touch, Last Interaction/last-touch, Last Non-Direct Click, Linear, Time-Decay, and Position-Based.
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 the first-touch attribution limiting when trying to optimize or demonstrate the value of their efforts.
Last-Touch Attribution Model
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.
Its 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, its 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.
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.
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.
Developing a model you can be confident in, and then processing your data through that model, requires a lot of time, money, research, and failure. Fortunately, Google recently launched Attribution 360. This product is available through the Google Analytics 360 Suite, Google’s paid version of Analytics, and is several steps above the suite’s existing Data-Driven Attribution platform.
Attribution 360 uses machine learning to assign weights to each touchpoint of a user’s journey. It spans different devices and channels, combining data from Google Analytics, AdWords, and Doubleclick to comprehensively determine Digital Attribution. Unlike Data-Driven Attribution, Google Attribution 360’s algorithm is not limited by a 90 day lookback window, offering more accurate conversion insights for businesses with longer decision cycles.
It also pulls in offline data for true cross-channel marketing. With Marketing Mix Modeling, everything from radio and print to digital is aggregated for users to gauge performance across all media. TV Attribution synthesizes advertisement airings data and search query data for keywords associated with that ad. It determines the impact of any given TV ad at a granular level as well.