What is Marketing Attribution? A Complete Guide for Marketers​

The customer journey is more complex than ever. And being able to attribute the impact of specific marketing touchpoints is crucial, as pressure from internal stakeholders to prove ROI intensifies. Unravelling the impact of specific touchpoints on conversion is priority number one for marketers.

So, what exactly is marketing attribution? Where does it fit in? And what are the benefits of getting it right?

Our blog covers everything you need to know to get started with effective attribution.

• What is marketing attribution?
• What are attribution models?
• Why is attribution important?
• How do I choose the right attribution solution?
• What attribution tools are available?

What is marketing attribution?

Marketing attribution is the process of determining which marketing touchpoints and activities are contributing to conversions – which could come in a variety of shapes and sizes from sign-ups, to downloads, to purchases of a product or service.

A relative value is then placed on each touchpoint to determine the greatest impact on conversion.

A quick history of marketing attribution

Media Mix Modelling

Media Mix Models (MMMs) go as far back as the 1950s. But rose to popularity in the 1980s.

However, their popularity purely came down to the lack of alternatives available to marketers at the time.

Many marketers still use MMMs for top-level media planning and budgeting. Mostly because they haven’t yet made the transition to more modern ways of understanding their marketing mix.

Multi-Touch Attribution

With MMMs failing digital marketers, new models were created including Last Click attribution.

Multi-touch models filled gaps left by MMMs such as the ability to measure in much greater detail. Therefore, enabling marketers to see more granular analysis of what’s working, such as targeting strategies, creative and time of day.

However, these methods fail to capture the value of many other factors that may have contributed to the desired behaviour. Or, understand if that behaviour might have happened in the absence of the advertisement “touch.”

Machine Learning and AI-Driven Attribution

It’s become clear that existing solutions fail to capture the entire customer journey. But Machine Leaning and AI processes are paving the way for a new era of attribution.

Standard models rely on cookies and IDs to track touchpoints. However, ML and AI-led approaches (which we use for our attribution solution, Corvidae) allow for the rebuilding the raw, cross-channel analytics data to see a complete customer journey.

If you’re new to the world of attribution, check out our attribution glossary to learn some of the key terms you need to know.

What are attribution models?

Attribution models work to analyse which touchpoints, or marketing channels, should be credited for a conversion. However, the amount of credit an interaction gets varies depending on the type of model used.

One of the key things to understand upfront about attribution models is that there isn’t a definitive, one-size-fits-all answer for everyone.

And, in practice, identifying which model is right for your brand or your business is going to be impacted by a range of different factors from the stage, scale and maturity of your marketing activity to how much time, budget and effort you are able to apply to it.

Types of attribution models

Here’s a quick rundown on some of the most used models:

• First-click
  First-click is built around the premise that a customer’s first touchpoint is the most important. So, 100% of the credit for conversion is attribution to the first interaction.

• Last-click
  Similarly, last click allocates 100% of the credit for a conversion to a single touchpoint. However, in this case it allocates credit to the final touchpoint – giving zero credit to any other integrations across the customer journey,

• Linear
  The linear attribution model applies equal clue and credit to every touchpoint in the customer journey.

• U-Shaped
  U-Shaped assigns more value to the first and last touchpoints in a journey, as they are seen to have more influence on conversion.

• Time Decay
  Time decay assumes that touchpoints closer to conversion should get more credit. Therefore, it increases the value associated to an action the closer it is to a conversion point.

• W-Shaped
  An extension of the U-Shaped model, with additional weight for the opportunity stage. Here, first-click, lead conversion and opportunity creation share 30% credit each. Then 10% is shared out between the other touchpoints.

Marketing attribution model example

For most marketers, identifying potential customers earlier in the buying journey is easier said than done.

To do this effectively, you need access to accurate attribution data that proves the value and contribution of every touchpoint in a conversion journey.
This can be a challenge when most off-the-shelf attribution solutions rely on flawed attribution models like last click.

By attributing 100% of conversion revenue to a user’s last touchpoint, last click solutions fail to account for the complex and multi-faceted nature of most users’ true conversion journeys.

For example, while Google Analytics may show you that a user came to your website directly, a realistic view of their journey may look something more like this:

• A user clicked through on one of your Display ads they saw while reading their favourite blog.
• After visiting your website once, they might have clicked on a remarketing ad on Facebook.
• Now that they’re aware of your product or service, they use Google to weigh up their options with other companies that provide the same thing.
• After doing their research, the user decides you have the best service proposition and re-visits your website to convert.

Despite the important role that Display and Paid Social played in the journey to that conversion, last click gives them none of the credit.

With limited views like this from their attribution solutions, marketers are left spending more of their budget on oversaturated, lower-funnel markets when the real opportunity lies further up the funnel.

Why is attribution important in digital marketing?

When done properly, marketing attribution allows marketers to create a single, unified data view.

This view allows them to re-assess their marketing mix and make adjustments to marketing spend to drive true marketing ROI.

Furthermore, marketing budgets are being slashed left, right and centre. But marketers are still expected to produce results with far less resources at their disposal.

Accurate attribution places the power to make the right budget decisions back into their hands.

3 reasons why marketing attribution is more important than ever

• Increasing pressure to prove ROI
  Prior to recession, there was already evidence of an increased level of scrutiny on marketing spend and internal pressure from key stakeholders on this. But recently, marketers are facing even more pressure to create spend efficiencies and show the impact their activity is having on the business’s bottom line.

• Impending removal of cookies
  After a few delays from Google, their deprecation of third-party cookies is looming. And without accurate attribution, marketers are going to struggle to effectively track the complete user journey in a cookieless future.

• Analytics are under scrutiny
  Recent news around the legality (or otherwise) of Google Analytics has left the world of measurement up in arms. Having fully compliant analytics isn’t a ‘nice-to-have’, it’s a necessity. And without analytics you can trust, marketers are putting their marketing efforts at risk.

4 signs you need a new attribution solution

1. You struggle to justify your marketing budget and spending decisions
   Marketing spend decisions are under greater levels of scrutiny than ever before. And the pressure to connect marketing investment to growth and revenue is non-negotiable. However, marketers using ineffective attribution models lack accurate data to prove the effectiveness of marketing activity.
   
2. You lack confidence in your marketing analytics data
   Secondly, you simply might not trust your marketing data. At the heart of the issue are flawed attribution models and solutions that simply aren’t fit for purpose. Marketers are left struggling to create a single, data driven joined up view of the customer journey which is only compounded by the data silo issues.
   
3. You have difficulty identifying fraud and wasted marketing spend
   Digital advertising’s meteoric rise in popularity has shifted the way that brands can deliver targeted messages to the right type of audiences. This creates unparalleled opportunities to deliver ad placements on thousands of sites, at the right time and to the right audiences. Programmatic is at the heart of this. For example, high profile studies have shown that bots are responsible a quarter of all “views” of video-based ads. And therefore, contributes to a fraud problem that the World Federation of Advertisers forecasts will cost brands more than $50 billion by 2025.
   
4. Your existing attribution approach has limitations
   You might also have some limitations in your current attribution solution that holds you back. If you don’t have effective attribution in place, then it’s a safe bet that you also don’t know what is – and isn’t – working across your marketing mix. So, assessing and improving “true” marketing performance isn’t possible for you.

How do I choose the right attribution solution?

With so many attribution tools available, it can be difficult to know which is right for you, your business and your goals.

Here are some questions to ask yourself when choosing a new tool:

• Is the attribution model being placed on top of broken marketing data?
  The quality of the data being fed into your attribution tool is just as, if not more, important than the model itself. Therefore, choosing a solution which can rebuild the underlying data is crucial for accurate attribution. Consider solutions that overcome these limitations by rebuilding and enhancing your raw clickstream data. As well as ones which use Machine Learning and AI to stitch together each interaction across the customer journey.

• Can you customise the model to understand your customer journey?
  Attribution’s most common application is the rule-based last click model (Google Analytics default attribution approach). But no two businesses are the same – and no two buying journeys are the same. Therefore, relying on a “one-size-fits-all” approach to attribution means you can never truly understand your complete customer journey. Multi-touch, custom attribution models are the only way to understand the impact of marketing across the customer journey, to provide long-term insights and actionable results to drive marketing ROI.

• Does the attribution tool rely on cookies?
  The cookie is crumbling. And all standard solutions rely on the use of first- and third-party cookies for analytics. But cookies are deterministic and are stored in siloed devices, meaning these attribution solutions fail to capture a complete view of the user behind the device. Furthermore, by working on the raw clickstream of client websites, we discovered that cookie-based analytics tools are only 20% accurate. Therefore, it’s likely that you’re basing important marketing decisions on 80% broken data. Choosing a cookieless attribution tool is the only way to achieve accurate attribution in 2023 and beyond.

What attribution tools are available?

No two attribution solutions are created equal. Depending on if you’re working in the B2B space, B2C or even the specific activity you’re looking to track, there’s a different solution on the market for you.

So, how can you be sure you’ve chosen the right attribution tool for you, your business, and your goals in 2023?

Here are just some of the attribution vendors currently available for digital marketers:

• Corvidae
  Corvidae is QueryClick’s unique attribution tool and is the only tool on the market which doesn’t rely on cookies for analytics. It’s a revolutionary tool with patented AI and Machine Learning technology to completely remove the need for all cookies (first- AND third-party) and was awarded the Masterclassing’s Most Effective Attribution Solution in 2021 and 2022.

• AppsFlyer
  AppsFlyer is a marketing attribution tool specifically for mobile apps and aims to help marketers map app installs to individual touchpoints across the web.

• LeadsRx 
  LeadsRx leverages multi-touch attribution for TV advertising, including live, broadcast, and dual-feed cable programming – allowing marketers to better optimise spend on their TV ads and achieve increased ROAS.

• Dreamdata
  Dreamdata gathers, joins and cleans all revenue-related data to present transparent, actionable analysis of what drives B2B revenue.

• Ruler Analytics
  Ruler Analytics is primarily a B2B marketing attribution and call tracking software — using closed-loop marketing attribution to provide revenue and conversions in one location.

Corvidae: The future of marketing measurement

Corvidae is the only solution on the market which uses AI and Machine Learning to completely replace the cookie. 

Our patented approach rebuilds the raw, cross-channel analytics data and unifies online and offline data before applying neural network processing to provide accurate attribution. 

Corvidae’s cookieless attribution allows digital marketers to: 

• Break down siloed data
• Gain an understanding of the complete customer journey
• Make better marketing budget decisions
• Achieve greater ROI