The Importance of Multi-Touch Attribution to Your Business

To manage your marketing spend effectively, you need to know which campaigns and channels are driving the best results. Yet the top performers aren’t necessarily the touchpoints that lead directly to the most conversions.

After all, few customers convert after a single interaction with your brand. The typical buyer’s journey involves multiple touchpoints across several channels—and each has its own cost and value.

So how can you attribute results accurately so you can optimize campaign performance and marketing spend?

Meet multi-touch attribution (MTA), a data-driven method that gives credit to each touchpoint so you can get a complete view of your marketing efforts. Find out how MTA can drive value for your business and improve results at every step of the buyer’s journey.

What is Multi-Touch Attribution?

Multi-touch attribution is the process of establishing the value of every touchpoint that contributes to a conversion. This approach factors in channels, campaigns, messaging, and sequencing to credit each touchpoint accurately. Ultimately, MTA insights reveal your highest value channels and campaigns, allowing you to optimize marketing and advertising spend.

MTA vs. Alternative Attribution Models

Marketers often adopt MTA after finding other attribution models too simplistic. Some of the most common alternatives include:

  • Platform Attribution: This model focuses on the channel, such as social or paid search. However, platform attribution doesn’t take the campaign or sequence into account, which eliminates key data points from the calculation.
  • Last-Touch Attribution: One of the simplest models, last-touch attribution assigns all credit to the final touchpoint prior to the conversion. While this model is significantly easier to track, it ignores the contributions that other channels and campaigns make at earlier stages of the customer journey.
  • First-Touch Attribution: Essentially the opposite of last-touch attribution, first-touch attribution gives all credit to the first touchpoint. Although this model identifies what first draws customers to your brand, it doesn’t account for what drives the final conversion—or other touchpoints along the way.

Why Multi-Touch Attribution Matters

MTA requires more complex calculations and modeling than other standard attribution options. That’s because it provides a holistic picture of your marketing rather than looking only at isolated channels or campaigns. With the right tools and analysis, this method can drive significant value for your business.

To visualize how MTA might work for your brand, consider a key conversion event like an e-commerce purchase. Then map the path that your customers generally take before converting. Your brand’s customer journey may look like this:

  1. Discovering your brand by seeing and clicking on a Google Display ad
  2. Developing an interest after viewing a Facebook ad and following your page
  3. Signing up for your newsletter after clicking on an organic Facebook post
  4. Purchasing a product after receiving a sale email

MTA takes into account the cost of each of these touchpoints as well as the significance you assign to each. For instance, you may give more weight to the sale email that reaches customers in the conversion stage of the buyer’s journey. Your calculations would also consider the value that each interaction generates, ultimately resulting in a model that identifies and optimizes for the most cost-effective and efficient touchpoints.
This type of attribution may sound complicated, but it doesn’t have to be. Marketers often rely on advanced attribution tools and partners like Operam to streamline the process and gain valuable insights.

Key Challenges of Measuring Multi-Touch Attribution

As powerful as MTA can be, it does have some limitations. It’s important to understand the challenges so you can address them effectively.

Click-Through Conversions vs. View-Through Conversions

When analyzing touchpoints, MTA tools often consider click-through conversions only without factoring in view-through conversions. It’s true that views generally have a smaller impact on conversions. However, ignoring them completely in favor of clicks can lead to distorted MTA models that provide inaccurate results.

Blended Multi-Touch Attribution Views

In many cases, disregarding view-through conversions can give inordinate weight to organic conversions. For instance, your paid search campaign may deliver an ad impression to a customer who later searches for your brand and makes a purchase.

If you don’t account for the view-through conversion, then you would discount the value that the paid search campaign provided. Instead, all credit would go to organic search, and you may mistakenly undervalue paid search over time.

To get a more in-depth picture of the results from your marketing efforts, consider using a blended MTA view. It takes into account both paid and organic campaigns as well as view-through and click-through conversions.

To credit touchpoints accurately, a blended MTA view typically uses a shorter view-through window—such as one day—to account for the limited period of time views and impressions generally have an effect on conversions.

Difficulty of MTA Cross Web and App

Tracking touchpoints across the web can be difficult, but using MTA for both web and app conversions can prove impossible. Because mobile apps don’t use the same tracking mechanisms that web-based platforms do, the two areas generally exist in separate silos:

  • Websites and digital marketing platforms like paid social and paid search share cookies and other tracking data, making it possible to map the customer journey through multiple channels.
  • Apps can generally identify web-based conversions that directly contribute to installs, but they typically use first-party data to track in-app conversions.

With mobile attribution, you can go beyond crediting the final touchpoint before app installation. Instead, you can map each touchpoint of the customer journey accurately so you can identify and optimize for the most effective mobile conversion pathways.

Importance of Data-Driven Attribution Models

Prescriptive attribution models like first- and last-click attribution fail to consider all available data points, which means they can’t assign credit where it’s due. In contrast, data-driven MTA models credit each touchpoint with a specific value or percentage. Many marketers experiment with one or more of these standard MTA models to find the right one for their business:

  • Linear MTA Model: To give every touchpoint equal credit, use a linear MTA model. This model is ideal for clarifying your customer journey but less helpful for pinpointing the most important interaction along the way.
  • Time-Decay MTA Model: If you consider the most recent touchpoint more important than interactions from days or weeks ago, use a time-decay MTA model. This model places much more importance on bottom-of-the-funnel interactions while still acknowledging the entire customer journey.
  • U-Shaped MTA Model: If you consider the first and last interactions key to driving a conversion, use a U-shaped MTA model. This model places a much higher value on the touchpoint that begins the customer journey and the final interaction before a conversion. All other touchpoints receive a smaller percentage of the total credit.
  • W-Shaped MTA Model: For longer customer journeys that depend on cross-channel initiatives, a W-shaped MTA model can offer added insight. This model gives more credit to the first, middle, and last touchpoints while placing less emphasis on all other interactions.

These data-driven attribution models incorporate game theory to give credit fairly. They use a combination of algorithms and the average marginal value from each touchpoint—known as the Shapley value—to distribute credit accurately. As a result, you can better understand your customer journey and optimize your marketing spend for the best results.

Is one attribution model best for every business? Definitely not—and there’s no single source of truth either. MTA isn’t perfect, but it does help you directionally understand the value channels are bringing to your business.

Ultimately, your best bet is to use a data-driven MTA model while considering the insights you glean in a larger context. Similar to cubism, you view each element as part of a whole to understand the entire subject. After all, MTA attribution is an art built from science.

At Operam, we look at your marketing as a whole rather than in isolated silos. As a result, we optimize the value you realize from every step of the customer journey, no matter how many channels it entails. Contact Operam today to learn how we can help you reach your marketing goals.