Multi-touch attribution defined

Marketers know that every customer interaction — whether it’s a click, a page visit or a customer service interaction —tells a story. Understanding these marketing touchpoints allows us to assign value to a customer’s journey and increase the likelihood of conversion. In today's dynamic marketing landscape, grasping the intricacies of how customers engage with our brands is essential for maximising our return on investment (ROI).

Research shows that getting access to updated tools and technology and optimising the marketing mix are among the top priorities for today’s marketers. These teams are also revamping their marketing metrics for a new era. The same survey shows that 41% of marketing organisations are using marketing attribution modelling as a measure of ROI.

In this article, we’ll go beyond the basics of multi-touch attribution and look at the benefits and the how-tos so that you can use it correctly to drive conversions and maximise your returns.

What you’ll learn:

• What is multi-touch attribution?
• Overcoming challenges in multi-touch attribution
• What are the best practices for achieving accurate attribution?
• Implementing multi-touch attribution
• How you can grow with multi-touch attribution
• Future trends in multi-touch attribution

Multi-touch attribution is a data-driven marketing approach that assigns credit to multiple touchpoints along the customer journey, providing insights into the effectiveness of various marketing channels.

Single-touch attribution is a marketing model that assigns all credit for a conversion or sale to a single touchpoint in the customer journey, typically the first or last interaction. In contrast, multi-touch attribution considers and assigns credit to multiple touchpoints that contribute to the conversion, providing a more comprehensive understanding of the customer's path to purchase.

In a single-touch attribution model, if a customer makes a purchase after clicking a Facebook ad, only Facebook would receive credit. However, in a multi-touch attribution model, if the customer initially discovered the product through a Google search, then engaged with a social media post and finally clicked a display ad before making the purchase, all three touchpoints would receive credit for their respective contributions.

Consider the benefits of using multi-touch attribution for your business:

Multi-touch attribution allows you to see the entire customer journey, from initial touchpoints to final conversions, providing a comprehensive understanding of the effectiveness of your marketing efforts.

By considering multiple touchpoints, multi-touch attribution ensures that credit is appropriately assigned to each channel or campaign that contributed to a conversion, enabling you to make informed decisions about resource allocation.

With insights into the performance of different touchpoints, you can optimise your marketing budget by investing more in channels that drive conversions and reducing spend on underperforming ones.

Multi-touch attribution helps you refine and optimise your marketing campaigns by identifying the most influential touchpoints, resulting in higher engagement and conversion rates. By attributing revenue and conversions to the specific touchpoints that influenced them, you can get a more accurate measurement of ROI.

Without the right strategy or tools, managing multi-touch attribution can be complex. These are some of the most common challenges we face as marketers.

This involves gathering and integrating data from different sources, especially when a large volume of data and various tracking systems are involved. It will help to invest in data management tools and platforms that streamline collection, integration and analysis. You’ll also benefit from implementing data governance practices to ensure data accuracy, consistency and quality.

It can be tricky to choose an attribution model that aligns with your business goals and accurately reflects customer behaviour. Different models have their own limitations and biases, making it important to find the one that suits your needs. To choose the right attribution model, you should conduct thorough research and experimentation. Consider using a combination of models or customising models to better suit your unique needs. What you select of course hinges on your specific business goals and customer journey characteristics. Adding to that complexity is the need to ensure data privacy and compliance. Your business needs to be cautious about collecting and analysing customer data while implementing robust data protection measures and staying compliant with data privacy regulations.

Tracking customer interactions across multiple devices is also a challenge, as customers often switch devices during their journey. This makes it difficult to attribute conversions accurately and understand the complete picture. Cross-device tracking solutions can help to identify and connect customer interactions across different devices. Probabilistic or deterministic matching techniques can also be useful for attributing conversions accurately, even when customers switch devices.

There’s also the problem of lack of standardisation. Since there’s no universally accepted standard, it’s possible to see inconsistencies in methodologies and metrics. This makes it difficult to compare results across different campaigns or organisations. You can get ahead of this problem a few ways: 1) Establish internal guidelines and standards for multi-touch attribution within your organisation, 2) Participate in industry discussions and collaborate with peers to advocate for standardisation efforts and 3) Use benchmarking and industry research to gain insights into best practices and align your approach accordingly.

On top of the challenges you’ll need to address, there are some best practices to consider when thinking about reliable attribution.

Consider incorporating both online and off-line touchpoints into your attribution model. This includes tracking off-line interactions such as phone calls, in-store visits or direct mail responses, which can provide a more comprehensive view of the customer journey.

Use advanced analytics techniques like machine learning and predictive modelling to gain deeper insights into customer behaviour and attribution patterns. These techniques can help to identify hidden patterns and correlations that may not be apparent through traditional analysis methods.

A/B testing or controlled experiments can help to validate the effectiveness of different marketing channels or campaigns. By comparing the performance of different strategies in a controlled environment, you can gain more confidence in the accuracy of your attribution model.

Attribution is an ongoing process and it's important to regularly monitor and refine your attribution model. Keep track of changes in customer behaviour, market dynamics and technology advancements to ensure your model remains relevant and effective.

Accurate data is indeed one of the biggest hurdles for many marketers when it comes to multi-touch attribution. Dealing with data discrepancies and measurement gaps can be a significant interference, as it can lead to incomplete or inconsistent insights. Marketers need to invest in robust data collection and integration processes, ensure data quality and consistency and regularly validate and reconcile data sources to overcome this challenge and achieve more reliable attribution results.

Implementing multi-touch attribution means understanding the different model types and taking the right steps to ensure accurate and reliable results regardless of your choice. On top of that, you need to factor in data quality, consistent tracking mechanisms and resource allocation.

Making the right selection for you is crucial. Here’s a quick breakdown of some of the different types of multi-touch attribution models:

• Linear attribution: This model assigns equal credit to each touchpoint in the customer journey. For example, if a customer interacts with an ad, a social media post and an email before making a purchase, each touchpoint would receive 33% credit.
• Time-decay attribution: In this model, more recent touchpoints receive higher credit than earlier ones. It recognises that the closer a touchpoint is to the conversion, the more influential it tends to be.
• Position-based attribution: Also known as U-shaped attribution, this model assigns 40% credit to the first and last touchpoints, while the remaining 20% is distributed among the touchpoints in between. It acknowledges the importance of both the initial and final interactions.
• Algorithmic attribution: This model utilises machine learning algorithms to assign credit to touchpoints based on their impact on conversions. It takes into account various factors and data points to determine the attribution weights.
• Customised attribution: This model allows businesses to create their own attribution rules based on their unique requirements. It can be a combination of different models or a completely customised approach tailored to specific business goals.

In order to have an effective attribution model, you must use data-driven insights for analysis. This will allow you to continue to improve your models with each initiative.

Let’s look at a fictional example to illustrate how a business can implement this effectively. Meet XYZ Blog, a popular online platform that publishes articles on various topics. They want to understand the impact of their marketing efforts on user engagement and conversions. Initially, XYZ Blog used a simple first-touch attribution model, which assigned all credit to the first touchpoint a user encountered. However, they realised that this model didn't provide a comprehensive view of their marketing performance.

To improve their attribution model, XYZ Blog started collecting data on user interactions across different touchpoints, such as social media, email campaigns and search engine referrals. They used analytics tools to track user behaviour and measure the impact of each touchpoint on user engagement and conversions. By analysing the data, XYZ Blog discovered that while social media played a significant role in driving initial awareness, email campaigns were more effective in nurturing leads and driving conversions. They also found that search engine referrals had a strong influence on user engagement and time spent on their website.

Knowing this, XYZ Blog decided to implement a multi-touch attribution model. They chose a time-decay attribution model, which assigned higher weights to touchpoints closer to the conversion event. This allowed them to give credit to multiple touchpoints along the customer journey. As they continued to analyse the attribution results, XYZ Blog noticed that certain types of articles performed better when promoted through specific channels. For example, technology-related articles had higher engagement rates when shared on social media platforms, while educational articles performed better when sent through email campaigns.

XYZ Blog optimised their marketing strategies with these data-driven insights. They allocated more resources to email campaigns and tailored their content promotion strategies based on the performance of different article categories and channels. Over time, XYZ Blog saw a significant improvement in user engagement and conversions.

Cover these basic implementation steps and you’ll be on your way as well:

1. Clearly define your goals and objectives for attribution analysis.
2. Ensure accurate and comprehensive data collection across all touchpoints.
3. Choose an appropriate attribution model that aligns with business goals and customer journey complexity.
4. Assign attribution weights to each touchpoint based on the chosen model.
5. Continuously analyse and optimise attribution results to improve marketing strategies.

Cover these basic implementation steps and you’ll be on your way as well:

1. Clearly define your goals and objectives for attribution analysis.
2. Ensure accurate and comprehensive data collection across all touchpoints.
3. Choose an appropriate attribution model that aligns with business goals and customer journey complexity.
4. Assign attribution weights to each touchpoint based on the chosen model.
5. Continuously analyse and optimise attribution results to improve marketing strategies.

Multi-touch attribution enables you to make data-driven decisions, allocate resources and optimise your marketing strategies. This level of optimisation leads to improved campaign performance, increased conversions and ultimately, drives growth for your business.

With multi-touch attribution, marketing campaigns provide a comprehensive view of the customer journey and the impact of different touchpoints. But in order to do it correctly, you must identify high-performing channels and touchpoints.

By understanding what works, you can pour targeted efforts and budget into these channels. When you get it right, multi-touch attribution helps you to measure ROI and prove the value of your marketing efforts.

So what are the channels commonly recognised as high-performing channels? It's important to note that the effectiveness of marketing channels can vary depending on the industry, target audience and specific campaign goals. Answering those questions for yourself is goal number one.

Here are a few channels that are often considered high-performing:

• Paid search advertising, such as Google Ads, allows businesses to target specific keywords and appear prominently in search engine marketing (SEM) results. This channel is effective for capturing intent-driven traffic and driving conversions.
• Social media platforms like Facebook, Instagram and LinkedIn offer robust targeting options and a large user base, making them effective for reaching and engaging with specific audiences. Social media advertising can be particularly useful for brand awareness and lead generation campaigns.
• Email campaigns remain a powerful channel for nurturing leads, driving conversions and building customer loyalty. Personalised and targeted email campaigns can yield high engagement rates and ROI.
• Creating valuable and relevant content, such as blog posts, videos and infographics, can attract and engage audiences. Content marketing is effective for building brand authority, driving organic traffic and generating leads.
• Collaborating with influencers who have a strong following and influence in a specific niche can help businesses reach their target audience effectively. Influencer marketing can generate brand awareness, credibility and drive conversions.
• Encouraging satisfied customers to refer their friends and family can be a highly effective marketing channel. Referral programmes leverage word-of-mouth marketing and can result in high-quality leads and increased customer acquisition.

• As with many marketing strategies and trends, emerging technologies are shaping the future of multi-touch attribution. And there are a growing set of tools available that combine data with AI to fuel the process.
• Advanced software platforms like Google Analytics offer capabilities for automating data collection, analysis and reporting. Artificial intelligence (AI) and machine learning algorithms can analyse vast amounts of data to identify patterns and correlations between touchpoints and conversions. These technologies enable more-accurate attribution modelling, helping us to understand the true impact of each touchpoint on customer behaviour.
• Tools that integrate data into a single platform also facilitate multi-touch attribution. For example, unified customer data platforms (CDPs) consolidate customer data from various sources, such as customer relationship management (CRM) systems, marketing automation platforms and website analytics. This provides a comprehensive understanding of customer behaviour and helps us attribute conversions accurately.
• In the context of multi-touch attribution, predictive modelling can help to identify the most influential touchpoints and predict the likelihood of conversions based on various customer attributes and behaviours. Let’s say a retail business wants to determine the optimal marketing mix to drive online sales. By using predictive modelling, they analyse historical data on customer interactions, purchase patterns and marketing touchpoints. The model identifies that email campaigns, followed by social media ads, have the highest impact on conversions. With this insight, the business can allocate a larger portion of their budget to email campaigns and social media ads, resulting in increased online sales.
• We know that AI-driven attribution uses machine learning algorithms to analyse vast amounts of data and determine the contribution of each touchpoint in the customer journey. These algorithms can identify complex patterns and interactions that traditional attribution models may miss, providing a more accurate understanding of the impact of each touchpoint. Take a software-as-a-service (SaaS) company that wants to optimise their marketing campaigns to drive more trial sign-ups. By implementing an AI-driven attribution model, they analyse data from various touchpoints, such as website visits, content downloads and webinar registrations. The AI algorithm identifies that a combination of search engine ads, content marketing and personalised email campaigns has the highest influence on trial sign-ups.
  
  In both examples, AI-driven approaches enhance multi-touch attribution by uncovering complex relationships and patterns in customer behaviour. This enables businesses to make data-driven decisions, optimise their marketing efforts and achieve better results in driving conversions and growth.

Understanding the stories of customer interactions is no longer a luxury, it’s a necessity. By embracing the power of multitouch attribution, your business can unlock invaluable insights into your customers' journeys. When done properly, you can optimise your marketing efforts and ultimately, elevate your bottom line.