Why marketing leaders prefer marketing mix modeling over multi-touch attribution

In the intricate dance of digital marketing, choosing the right analytics tools can be the difference between success and stagnation. The right tools help organizations like yours gain a superior understanding of how their ad campaigns are performing and how to allocate ad spend more effectively. But to truly harness the power of marketing insights, you’re faced with a critical choice: opting for Multi-Touch Attribution (MTA) or Marketing Mix Modeling (MMM). These are two major players in marketing attribution, and choosing the tool that aligns best to your business goals is crucial for ecommerce success.

What is multi-touch attribution (MTA)?

You’re probably familiar with MTA as a product marketing attribution companies are offering, but you might not understand what’s under the hood. Multi-touch attribution, or MTA, (also sometimes called multi-channel attribution) is a methodology that credits each point in your customer’s journey toward a conversion. Every social media ad click, email opened, or website visit they make before purchasing is given a specific value. This assists marketers in understanding the effectiveness of each channel so they can optimize their efforts and budget accordingly.

Technology plays a critical role in this kind of attribution. Multi-touch attribution models aren’t really things ecommerce companies spin up on their own—we’ll get more into why later. For now, suffice it to say that MTA is predicated on the ability to track a user throughout their online AND offline exposure to a brand, and that’s quite challenging. So instead of building them in-house, marketers working on attribution for ecommerce brands using an MTA require specialized software that’s typically an advanced analytics platform. These technologies gather, analyze, and correlate vast amounts of data from multiple touchpoints. Consequently, they provide a comprehensive view of a customer’s interaction with different marketing channels.

It’s worth noting at this point that MTA models will never be better than they were yesterday. That’s largely due to the large push toward data privacy across all consumer web consumption platforms (iOS, ChromeOS, etc). Yes, there’s a lot of talk about whether Google’s cookie depreciation timeline will change, but MTA’s accuracy will still decrease even if they push it back. To be clear, the reduction in MTA accuracy is inevitable.

In ecommerce marketing, MTA is utilized to help companies better understand the customer’s journey. It empowers them to identify which marketing strategies and campaigns are truly driving sales. Having access to this level of detail enables ecommerce brands to refine their marketing initiatives, ensuring they spend their marketing budget wisely while maximizing return on investment (ROI).

How it works & types of MTAs

As a marketing manager or CMO, understanding how MTA works can empower your decision-making process. Essentially, MTA is a methodology that assigns credit to different marketing touchpoints with which customers interact on their journey toward purchasing. Each touchpoint—a display ad, an email campaign, a web search, or a social media ad—is recognized for contributing to the final conversion.

There are various MTA models used in the industry, each with its unique approach to credit attribution. Let’s take a look at a few:

• Linear Attribution: Credit is distributed evenly across all touchpoints in a linear attribution model. This means that if a customer interacted with a social media ad, a blog post, and a product email before making a purchase, each of these touchpoints would receive an equal fraction of the credit for the sale.
• Time-Decay Attribution: This model attributes more credit to the touchpoints closer to the conversion. It operates on the principle that the more recent marketing efforts will likely be more influential in driving sales and, therefore, gives more credit to the most influential touchpoints.
• Position-Based Attribution: This model gives more importance to the first and last interactions—commonly referred to as the ‘U-shaped’ attribution model. Typically, 40% of the credit is allocated to the first and last touchpoints, while the remaining 20% is distributed evenly among the others.
• Custom Attribution Model: Companies can develop their own custom models if they have the resources. Custom multi-touch attribution can assign credit in whatever way aligns with what they think most influences lead creation and conversion.

These MTA models aim to provide a more holistic view of your marketing efforts. They highlight the importance of every interaction, showing that customers’ decisions aren’t typically influenced by a singular touchpoint, but by an orchestrated series of engagements.

Example

Imagine that you’re managing an online bookstore. Your marketing channels are spread across paid search, display ads, email promotions, and even social media interactions. A prospective customer, Jess, first discovers your store from a link in an email promo, clicking through to view a new fantasy novel you’ve highlighted. A few days later, she’s reminded again when she sees a display ad for the same novel while perusing her favorite blog. This piques her interest and she adds the book to her wish list, but she doesn’t purchase yet. Two weeks after, a paid search result when she’s Googling fantasy novels finally motivates her to buy the book.

In this situation, the multi-touch attribution model would assign a portion of the credit for the sale to each interaction—email, display ad, and paid search—that eventually led to Jess’s purchase. MTA recognizes and acknowledges the multi-faceted nature of ecommerce marketing and its effectiveness in nudging prospective customers towards the desired action.

Requirements

If you’re a company looking to develop your own multi-touch attribution model, you’re in for a complex journey. It’s akin to attempting the construction of a skyscraper on non-regulation material—it’ll stay up, but the foundation is faulty and untrustworthy. An accurate MTA requires detailed knowledge of marketing, customer behavior, data analysis, and various marketing channels and how they interplay in a consumer’s path to purchase.

Understanding isn’t the major hurdle of building multi-touch attribution models, though; it’s the price. (This is true of MMMs as well.) You need sophisticated data management capabilities to process vast sets of structured and unstructured data and a team of skilled data scientists to build and maintain intricate algorithms. It’s a challenging endeavor, and aiming to build it single-handedly can drain valuable resources. (Those head counts are some of the priciest on the market.) This complexity is why most ecommerce companies prefer to entrust this task to specialized marketing attribution companies.

Conversely, if you’re an ecommerce brand hoping to hop onto the MTA bandwagon via an attribution company, there are different considerations to bear in mind:

• Ensuring your data can properly integrate with the attribution platform is vital. This calls for clean, accurate, and comprehensive data across all marketing channels you utilize.
• Establishing uniformity across different data points, maintaining a consistent structure, and ensuring all relevant information—such as ad exposure and customer reactions—are recorded is critical. This will allow the attribution model to accurately attribute credit for sales and conversions among all marketing touchpoints.

The goal is to paint a complete picture of the journey from awareness to conversion, with uniformly formatted data for each marketing touchpoint.

If a little red flag just went up in your head, that’s probably because you already know this part of multi-touch attribution work is getting harder. You’re probably using data on your customer touchpoints from in-platform reporting tools that use pixels, which are facing increasing hurdles. Multi-touch attribution takes complete data, and if you don’t have it because pixels can no longer track your customers as data privacy increases, you simply don’t have what you need for accurate insights. More on that later.

Single-touch attribution vs multi-touch attribution models

As we established, MTA is an approach that considers various touchpoints a customer interacts with before purchasing. It acknowledges that the conversion process is complex and that various marketing efforts contribute at different stages.

Multi-touch attribution models assign credit differently, sometimes giving equal credit and sometimes giving weighted credit based on the influence of events. MTA assigns credit for a conversion across several channels, like social media, email marketing, PPC ads, and more, based on their influence on customer decision-making. This model provides a more holistic view of the customer journey, allowing marketers to understand the true effectiveness of each channel and strategize accordingly. However, implementing MTA can be complex, as we just discussed, requiring advanced tracking and analytics tools to measure and attribute conversions across multiple touchpoints accurately.

In contrast, single-touch attribution models, such as last-touch and first-touch attribution, assign all the credit for a conversion to one specific interaction:

• Last-touch attribution, the most traditional and simplest form, credits the final touchpoint before the conversion. For instance, if a customer last clicked on a PPC ad before purchasing, last-touch attribution would assign that ad all the credit. This model is easy to implement but often oversimplifies the customer journey, potentially leading to misinformed marketing decisions.
• First-touch attribution, on the other hand, gives all the credit to the customer’s first interaction with the brand. While this model is useful for understanding what attracts customers initially, it disregards the influence of subsequent interactions in driving a conversion.

Both first-touch and last-touch attribution models are easy to implement and understand, making them popular among businesses with limited resources for complex analytics. They also now come standard in many analytics platforms like TikTok reporting and Google Analytics. However, their oversimplified approach can lead to a skewed understanding of the effectiveness of different marketing campaigns and channels. By ignoring the multifaceted nature of the customer journey, these single-touch models can lead marketers to undervalue certain channels that play crucial roles in lead creation and customer nurturing throughout their decision-making process. They also fall subject to the same downfalls of digital fingerprinting as MTAs.

Attribution models that capture a more nuanced understanding of the customer journey are better. We’ll cover more of the benefits of multi touch attribution models below. But that also doesn’t mean this type of attribution model is the best option for marketing attribution, either.

Multi-touch attribution benefits

MTA can be a helpful tool for ecommerce companies seeking to optimize their digital advertising efforts especially if they’re trading up from single-touch attribution. By analyzing the impact of multiple touchpoints on a customer’s journey to a purchase, MTA offers several benefits:

1. Deeper Customer Journey Insights: MTA provides a more nuanced view of a customer’s path to conversion than alternatives like last-touch attribution, recognizing that multiple touchpoints contribute to a single sale or conversion. This method offers marketers more insights compared to single-touch attribution models, which overly simplify the journey by attributing the conversion to just one touchpoint, such as the last click.
2. More Effective Budget Allocation: With MTA, ecommerce businesses can allocate their advertising budgets more effectively than they can with a simpler attribution model. By identifying which channels and marketing campaigns contribute most to conversions, companies can invest more in high-performing areas and reduce spend on less effective ones, maximizing return on investment (ROI).
3. Personalized Marketing Strategies: By understanding the specific touchpoints that influence their customers, ecommerce companies can create more personalized marketing strategies. This tailored approach can increase customer engagement and loyalty, as marketing messages are more relevant to the individual’s journey.

Ultimately, MTA offers ecommerce companies a more powerful way than a single-touch attribution model to understand and enhance the effectiveness of their digital advertising. MTA can support smarter, data-driven decisions that drive growth and profitability by providing detailed insights into the customer journey and the impact of various marketing efforts. But it also has its flaws.

Shortcomings of multi-touch attribution

Yes, MTA offers more robust insights than an attribution model capturing only a single touch—but it’s far from perfect and faces many challenges.

One main concern is the increasing prevalence of technologies that block pixels, cookies, and other tracking tools. Since MTA heavily relies on these to gather and analyze data, its effectiveness has diminished since its inception. It will continue to do so as users adopt these blocking technologies. This makes it increasingly difficult for ecommerce platforms to gather accurate data on consumer behavior and purchasing patterns, raising the question of MTA’s effectiveness and reliability.

Implementing an effective MTA strategy can also be time-consuming and costly. The complexity of correctly attributing value to each touchpoint and maintaining the whole system up-to-date may not always be feasible, especially for smaller or burgeoning ecommerce businesses.

In light of these challenges, it’s crucial for you to seek more reliable and holistic alternatives. One such alternative is the Marketing Mix Model (MMM), which offers a broader, more aggregated approach, making it a more efficient tool for drawing valuable conclusions about your marketing performance.

Multi-touch attribution vs media mix modeling

If you work in marketing, you have the same goal: to optimize your marketing dollars to achieve a better ROI. While MTA and MMM are both tools for understanding marketing effectiveness, they take different approaches.

Multi-touch attribution models track the customer journey across various touchpoints, attributing monetary value and conversions to multiple channels. It correctly emphasizes the importance of several marketing touchpoints along the customer journey. However, it can be hindered by privacy legislation, cookies, and device tracking issues, and it tends to overlook external factors such as market trends, competitor activities, or macroeconomic effects that may influence a customer’s purchase decision.

On the other hand, MMM takes a broader view. It considers media and non-media factors such as brand equity, promotional events, pricing changes, and macroeconomic variables to ascertain how these elements collectively impact your sales performance. This wide lens can provide a more holistic picture of your marketing effectiveness.

So, why do many consider MMM a more robust source of data? For one, MMM takes historical data into account, allowing for a comprehensive analysis of long-term marketing strategies. Additionally, MMM isn’t affected by the privacy restrictions and cookie limitations that can hinder MTA, making it more reliable in today’s digital landscape.

In a nutshell, while MTA provides a detailed look at individual interactions in the buyer’s journey, MMM offers a wide-angled and holistic perspective on marketing strategies and performance. Therefore, MMM often emerges as the more reliable source of information for understanding the impact of marketing spend on revenue and ROI.

We would be remiss if we didn’t mention here that building MMMs is also prohibitively expensive for most brands. Ecommerce companies that want to try may find that MMMs are worth the time and expense compared to MTAs because they don’t have the same issues with digital fingerprinting. If you still can’t afford to build one in-house despite the benefits, we’d like you to meet the Prescient AI dashboard.

Meet Prescient AI’s MMM model

We go into this more in our marketing mix modeling guide, but there are different types of MMMs as well. Most MMMs use regression analysis—a type of model that’s been around since the 60s. We knew MMMs needed to evolve and that these older models, while evolutionary, had limitations that don’t work for the modern marketer.

With a team of expert data scientists and marketers, we developed a new type of MMM using AI and statistics that can illuminate campaign-level insights. Platforms using regression analysis can only work down to the channel level. Our models also update daily; traditional MMMs can take a month or longer to update. Using our new methods also means our platform is future-proof to further user data restrictions.

Want to see it in action with your own brand’s historical data? Set up a time to go through a demo.

MTA FAQs

What is multi-touch attribution?

Multi-touch attribution is a marketing analysis method used to evaluate the impact of various marketing touchpoints on a customer’s decision to make a purchase or take a desired action. Unlike single-touch attribution models that credit just one touchpoint, multi-touch attribution distributes credit to several touchpoints along the customer journey, recognizing that each interaction can influence the final outcome. This approach provides a more comprehensive understanding of how different marketing channels contribute to conversions, allowing marketers to optimize their strategies and budget allocation more effectively.

How does multi-touch attribution work?

Multi-touch attribution works by tracking and analyzing all the touchpoints a customer interacts with across their buying journey, from initial awareness to final purchase. It uses advanced algorithms to assign a value or credit to each interaction, reflecting its contribution to the eventual conversion. This method allows marketers to see not just the last click before a purchase but the full spectrum of marketing efforts that influenced the decision.