In the lightning paced world of digital marketing, understanding the impact of each touchpoint in a customer’s journey is the name of the game. Gaining this understanding will allow you to adjust your marketing strategies to better achieve sales and cut down on wasted effort.
One way to achieve this kind of insight is to deploy what’s known as ‘multi-touch attribution’, although it does have its drawbacks and limitations.
To unpack this concept further, we wrote this comprehensive guide that explores the intricacies of multi-touch attribution, why it’s important, the types of models you can use to analyze multiple touchpoints, implementation strategies, as well as a host of benefits and challenges.
A brief definition of multi-touch attribution
Multi-touch attribution, also known as ‘MTA’, provides you, as a marketer, with insights into how your various marketing efforts contribute to conversions.
Unlike single-touch attribution models, which credit a single touchpoint for a conversion, multi touch attribution models consider all marketing touchpoints that influence a customer’s decision.
What’s the importance of multi-touch attribution in modern marketing?
In today’s omnichannel marketing landscape, consumers will likely interact with your brand or business through a range of online marketing channels and devices etc.
To bring order to this diverse marketing landscape, MTA provides a view of your customer’s online journey, helping you allocate marketing budgets more effectively and optimize your campaigns based on more accurate insights.
Limitations of MTA
The problem with relying on MTA to understand customer journeys is that it only works with online marketing channels, and is reliant solely on the use of cookies.
This means that any other marketing pursuits you undertake, like TV, radio, out of home ads etc. do not get measured. That’s a rather large blind spot.
Furthermore, unlike MMM, which takes a much broader view, you also don’t receive intel on how other factors, like the weather, or economic phenomena, play into marketing effectiveness and ROI.
For these reasons, you should not rely solely on MTA, but rather look to the broader scope and functionality that MMM can provide.
Why does multi-touch attribution matter?
So why should you care about MTA as a marketer? What benefits will you gain?
MTA is more advanced than single-touch attribution
MTA provides a more accurate reflection of your online marketing’s performance by taking into account those separate online interactions that may collectively lead to a sales conversion.
It’s more than likely that you didn’t get a sale because of one ad or one brand interaction, but because of a host of separate marketing touchpoints across multiple channels.
Multi touch attribution models basically evolved from the limitations of traditional single-touch attribution models, which often overlooked the complexity of modern consumer behavior and often assigned credit to first or last touch attribution.
It helps increase your marketing ROI but is also limited
By understanding the contribution of each online touchpoint, you can then optimize your digital marketing strategies to improve your return on investment.
By understanding your online ROI better, and how it came about, you can choose to ditch or pursue certain channels or adjust your strategy as you go. As stated, however, its limited by the reliance on cookies. All other elements of your marketing mix will remain unseen.
It helps you understand your online customer journeys better
MTA really helps you create detailed maps of your customer journeys and thus gain deeper insights into your customer’s behavior and preferences. It helps you get a clearer picture of your purchase funnel.
It will make you a more efficient marketer and budget more effectively
MTA is a godsend when it comes to marketing budget allocation. By using MTA, you can make better budget recommendations and focus your investments on your better-performing channels and campaigns. In the same vein, you can ditch those investments or channels that aren’t really working for you.
What are the different types of multi-touch attribution models?
There’s more than one type of MTA. They are constructed differently and have their own strengths and weaknesses. Let’s take a look at a few of the main types.
Linear multi-touch attribution
Linear attribution model is all about crediting distribution equally. So basically, it assigns equal credit to each touchpoint in the customer journey, offering a straightforward approach to attribution.
Time decay multi-touch attribution
With a time decay attribution model, credit for a sale is based on time proximity to the conversion. So, with this model, you give more credit to touchpoints that took place closer in time to the conversion event, reflecting the immediate influence of that particular marketing effort on the sale.
U-shaped multi-touch attribution
U-shaped attribution models will just focus on the first and last touchpoints. This model essentially places a lot of importance on the first and last interactions in the customer journey, and crediting these with their role in sparking and closing conversions.
W-shaped multi-touch attribution
W-shaped attribution models take the U-shaped model a bit further by also giving credit to the ‘middle’ touchpoint in a customer journey.
This attribution model will value to the first touchpoint, major interactions in the middle, and then the final touchpoint, providing a more balanced view of the customer journey.
Full path multi-touch attribution
As the name suggests, full path multi touch attribution gives you a comprehensive view of all major touchpoints.
The full path option will basically examine all of the significant touchpoints throughout the customer journey, offering a complete understanding of your campaign effectiveness.
Custom multi-touch attribution
If you have specified needs in your chosen approach, you can also choose a more tailored attribution model, known simply as ‘custom MTA’.
With a custom model, you can speak to any specific business goals you have by personalising a model that suits your specific aims and objectives.
How does multi-touch attribution actually work?
So now that we have a pretty good idea of what it is – how does it actually function?
Primarily, MTA models are based on the collection and assembly of great slabs of online marketing data through the use of cookies. You’ll also definitely need some fit-for purpose software to get moving here and many such solutions will help you assemble this data.
First you’ll need data collection and analysis
- The type of data you’ll need will include data from multiple channels and platforms which capture the entire customer journey accurately.
- Your data sources will typically include CRM systems, marketing automation tools, web analytics platforms, and other sources to gather relevant data.
Now for your attribution techniques
- Choose a model and plot associated rules to allocate credit to marketing touchpoints based on your predetermined criteria.
- You can also lean on more advanced attribution by leveraging machine learning algorithms and statistical analysis to attribute credit dynamically based on actual data patterns.
How do you go about implementing multi-touch attribution models?
Right, so what are the steps you’ll need to follow to develop a multi-touch attribution model?
- Define your objectives and identify all potential touchpoints that contribute to conversions.
- Implement a robust data collection strategy to gather quality customer interaction data.
- Use advanced analytical software tools capable of processing large sets of data and generating actionable insights. You won’t get far without good software.
- Deploy tracking codes and scripts to capture user interactions across digital channels effectively.
- Refine the multi touch attribution model based on performance analysis and validation against actual conversion data.
What does the future hold for multi-touch attribution?
To finish off our MTA guide, let’s look at what we think may be in the pipeline for MTA in terms of future trends.
- AI and machine learning algorithms are already enhancing attribution accuracy and efficiency. This will likely continue in leaps and bounds.
- The integration of MTA with advanced analytics techniques will likely advance to help you gain deeper insights into consumer behavior.
- It won’t be long until we see new multi touch attribution models and techniques that may shape the future of marketing analytics.