Attribution For Beginners: A Glossary of Terms You Should Know
With more and more marketers expected to meet (and even exceed!) targets with reduced budgets, understanding where is best to spend your marketing budget has never been more important.
That’s where attribution comes in.
But, the world of marketing attribution can be a complicated one, full of technical phrases and jargon that makes it seem like a daunting subject – even for the most experienced marketers.
So, we’ve pulled together a list of some of the most important terms you should know. With our straightforward ‘attribution for dummies‘ explanations, you can begin your journey to effective and accurate attribution with confidence.
- Ad Fraud
- Artificial Intelligence (AI)
- Clickstream Data
- Lookback Window
- Machine Learning
- Marketing Attribution
- Multi-Touch Attribution
- Out-of-Home (OOH)
- Return on Ad Spend (ROAS)
- Session Stitching
- Visit (Campaign) Level Attribution
- Walled Garden
When marketers see great engagement rates on ads, it can be easy to take it at face value and believe all these interactions are coming from real human beings. But unfortunately, this isn’t always the case.
Ad fraud happens when any deliberate activity stops the delivery of ads to the intended audience or in the intended place. It can skew campaign results so they look more successful than they really are. Frequently, this is done by using bots to view or interact with adverts to inflate viewing or click-through rates.
It’s a massive issue in the world of paid advertising and in fact, The World Federation of Advertisers forecast fraud will cost brands more than $50 billion by 2025.
Artificial Intelligence (AI)
We all know about AI from books, movies and tv shows telling us robots will soon take over the world but what does AI mean for marketing attribution and marketers?
AI has a crucial role to play in using your marketing data in the most effective way. Although it’s often interchanged with Machine Learning (which we’ll look into later in this post), AI is an umbrella term used to covers different aspects of computer-based intelligence algorithms.
Put simply, a clickstream is the pathway that a user takes through their online journey. It is usually focused on a single website and generally shows how the user progressed from search to purchase.
In terms of search engines it shows where a user has searched for a single or multiple terms and has then gone on to click on a term, and whether they return to search after this.
The clickstream is all about linking together the actions a single user has taken within a single session. This means identifying where a search, click or purchase was performed within a single session.
Sadly, when we talk about cookies in marketing, we don’t mean the crumbly, chocolatey kind that go nicely with a cup of tea.
Cookies are pieces of data stored on a user’s computer by a web browser. They are used to remember stateful information or to record the user’s browsing activity. So, when you go back to a website you’ve visited in the past and it remembers your login information, that’s the browser cookies storing your information.
But, big changes are on the way as large industry players begin removing third party cookies from their browsers.
Deterministic data is any data about people collected through marketing activities that is clear, unequivocal, and 100% accurate – so essentially, it can be determined.
Deterministic data include:
- interests and
- previous purchasing history.
With this data, you can be certain of the identity of an individual that has interacted with your brand, unlike probabilistic data which we’ll dive into later in the post.
Incrementality is a way to measure an event that wouldn’t have occurred without a specific interaction that resulted in the desired outcome. In effect, incrementality measures if a view of an ad led to a conversion vs whether that conversion would have still happened without the ad view.
This allows you to see if your ad spend is contributing towards sales, or whether those sales would happen anyway. Ultimately it means you can see how effective various activities are or if you are wasting money on certain ads or campaigns.
Want to know more about incrementality in simple terms? Check out this blog.
Lookback windows are the time period between an interaction with a brand (i.e. an ad view or website click) and a conversion. You might think that just by having an attribution or analytics solution in place you’ll have access to all of your data. This is not the case.
Run-of-the-mill analytics tools struggle to provide marketers with a holistic view.
For example, Google Analytics only offer a 90-day lookback window. So, if someone first clicked through to your site from an ad in August 2020 but didn’t actually complete an online purchase until January 2021, you will not be able to see that the initial ad they clicked through from had a positive impact on that conversion.
At its most basic, machine learning uses programmed algorithms that receive and analyse input data to predict output values within an acceptable range. As new data is fed to these algorithms, they learn and optimise their operations to improve performance, developing ‘intelligence’ over time.
Machine Learning is a subset of Artificial Intelligence that focuses on using computer algorithms to automatically improve through experience. Essentially, Machine Learning algorithms use a sample set of base data to make decisions or predictions without ever explicitly being told to do so.
If you’re looking to invest in a marketing attribution solution then the first logical step is to explore what marketing attribution is – and more importantly, what it isn’t.
In simple terms, marketing attribution allows marketers to assess which marketing activities are contributing to sales or conversions. Attribution, at its core, is about giving the right credit where it is due.
Learn more in our beginner’s guide to attribution.
Multi-touch attribution is a method of marketing measurement that evaluates the impact that each touchpoint has in driving a conversion, and then determining the value of each specific touchpoint.
Multi-touch attribution models are the only way to understand the impact of marketing initiatives and media across the customer journey. They provide the long-term insights you need for actionable results to drive your marketing ROI.
OOH, not to be mistaken with OOO, stands for ‘out-of-home’ which, essentially, is what it says on the tin.
Unlike TV, Radio or even Social Media advertising, OOH covers any form of outdoors advertising. So, when you see a billboard with the latest streaming services advertised or an advert on a train for a breakfast cereal, that’s OOH.
Earlier in the post, we looked at cookies. You could think of pixels as the milk of the milk and cookies combo. They work separately but together they make the most common combination for data collection.
Cookies are stored in your browser and can’t follow user from device to device. The major downfall of cookies is that individuals can choose to clear their cookies whenever they want or block you from using them during their visit. Ironically, pixels – or tracking pixels – act as a trail of crumbs left across the web as you move from site to site.
Pixels can come in many forms and have a whole host of uses – from retargeting website visitors on Facebook to knowing when a conversion has been completed. They’re crucial to understanding the full customer journey and having the ability to get the right content in front of the right audience at the right time and place.
We’ve looked at deterministic data so what about probabilistic data?
Data that is referred to as being ‘probabilistic’ means data you might receive from offline advertising or social media channels, like Facebook or Amazon.
You know that your paid, earned, or shared channel exposure is likely to have reached a given number of people that fit within the profile of your target consumer. But it’s not possible to be certain that someone who takes the desired action on your website was one of those consumers.
Like the name suggests, this type of data can only suggest what is probable but not definite.
Return on Ad Spend (ROAS)
Return on Ad Spend (Or, ROAS – marketers love an acronym!) is often seen as one of the most important metrics for marketers. Put simply… If you spend X amount on Campaign A, in Channel B, what will you get?
Not to be confused with ROI (Return on Investment), ROAS looks at how effective a specific campaign, ad or even keyword has been whereas ROI focuses on the overall effectiveness of a campaign.
Earlier, we looked at clickstream data – which tracks the steps a user takes on one website that ultimately led to a conversion. But what if that user explores multiple channels before taking the plunge?
The modern consumer jumps from device to device exploring multiple different channels before they make a purchasing decision so being able to ‘stitch’ those separate browsing sessions together is crucial to get a complete view of the customer journey.
By having session stitching in place, you can then understand where the best channels and campaigns are to spend your marketing budget wisely.
Marketing touchpoints are customer interactions with your brand over the course of their buyer journey. Mapping those touchpoints across the buyer journey will help your brand provide the right experience for your customers across channels.
Visit (Campaign) Level Attribution
What if you could have analytics data that goes beyond channel or even campaign level attribution – to show you the behaviours of the person behind the pixel? Introducing… Visit Level Attribution.
We worked with the University of Edinburgh to create a new approach to attribution. Visit Level Attribution (VLA) gives marketers an accurate view of an individual’s probability to convert at every stage in their journey. By scoring each touchpoint individually, you can see exactly which campaigns to invest in to increase conversions and achieve lower CPAs.
Walled gardens are: “A closed ecosystem in which all the operations are controlled by the ecosystem operator.”
Like building a moat around a castle, social networks have made it almost impossible for other analytics platforms to access their data and so marketers are left confused about who is reporting the results of their campaign accurately.
The Complete Guide to Marketing Attribution
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