Are Multi-Touch Attribution Models More Effective Than Last-Touch?

Feb 21, 2020   |   Clock Icon 6 min read
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When many marketers consider the customer journey, they tend to focus on the following:

  1. Which actions led to a conversion
  2. Which touchpoints had the most influence along the way

This isn’t a bad start. Both of these angles are essential components of tracking the ROI of marketing channels. But oftentimes, they may offer limited insight into how customers found your brand in the first place—and what inspired them to make their final decision.

Banks are in a unique situation in which data is baked into nearly every process. Because customers exchange personal information for nearly all your products, much of a customer’s lifecycle can be tracked back by data points. Data segmentation can help banks and other financial organizations track behavior against the customer lifecycle. And effective modeling can help illustrate important trends and other information.

Despite the advancements of data collection strategies, however, many financial marketers still find it difficult to measure marketing performance to conversion points along the customer journey. Because of this, many teams are unable to effectively measure which channels and campaigns are bringing in new customers.

From spending marketing dollars to planning for campaigns, many marketers know that data can help fill in the gaps. But measurement these days is much more nuanced than ever before. It requires a clean, effective data collection strategy.

To help illustrate the importance of clean data, let’s take a look at attribution models.

What is an attribution model?

As we’ve outlined before, attribution models are designed to help marketers assign conversions to touchpoints across a user’s journey. These days, there are a myriad of ways that a user can enter you site:

  • Direct entrance
  • Organic search
  • Paid search ads
  • Emails or newsletters
  • Web referrals

What’s more, a single user may visit your site multiple times—and may enter through different sources each time. By the time they convert, they’ll generally have had multiple interactions with your brand. Attribution models can help banks understand omnichannel marketing campaigns. They’re designed to assign credit to the different clicks along that journey.

What’s the difference between single-touch vs multi-touch attribution models?

Single-touch attribution models map credit for a customer to a single touchpoint.

These models accredit a customer’s full revenue amount to one channel—regardless of how many touchpoints led to a conversion. As we’ve written about, Google Ads has several single-touch attribution models:

  • Last click attribution gives all credit to the last clicked Google Ads ad and corresponding keyword in the conversion path. This is the default model used in Google Ads and is the most commonly used model.
  • First click attribution gives credit to the first clicked Google Ads ad and corresponding keyword.

Unless consumers have unwavering brand loyalty, it can be assumed that they will do more than one search for your brand, products, or services. Chances are, they’ll most likely have interactions with your competitors as well. The modern customer often takes hundreds of steps in their journey. And single-touch attribution only accounts for one step of that journey.

Multi-touch models account for omnichannel marketing experiences.

While single-touch attribution models may have worked for finance organizations in the past, users may now enter and exit your website through a number of channels. Multi-touch attribution models are designed to share the credit across multiple touchpoints of a customer journey. Algorithmic-based models split the credit by assigning fractional credit to each touchpoint—so that marketers can see how much influence each channel has.

Multi-touch attribution is designed to eliminate biases by algorithmically allocating credit. Of course, there are many types of multi-touch models. Below are three used by Google Ads.

  • Time decay attribution provides credit to touchpoints that happen closer to the time of conversion. The model uses a seven-day half-life in which a click eight days before a conversion gets half as much credit as one does one day before a conversion.
  • Position-based attribution leverages a system in which the first and last touchpoints receive higher percentages than those in the middle of the conversion path.
  • Data-driven attribution uses machine learning to split credit for conversions based on how people are searching for your business. It uses data from your account to determine which ads, keywords, and campaigns have the biggest impact on your goals.

Which attribution model is right for your team?

Let’s consider attribution modeling through the lens of paid search.

Because PPC is becoming more and more automated, marketers may leverage smart bidding where Google sets CPC based on a specific CPA goal. Or, marketers may use a rules-based approach to find keywords that aren’t driving conversions. However, many of these automations use conversion data to drive results. And in a last-click attribution system, automations may come across high-level keywords with no conversions—and may eliminate them automatically.

As this scenario illustrates, even automations require a human touch. And while last-click attribution models may work when there are fewer interactions across a conversion path, the model is inherently risky to use when you’re leveraging automations.

Unfortunately, there’s no single attribution model that works for everyone. What works for one team may not work for the next. Single-touch models may be effective for financial teams with shorter sales cycles and fewer interactions across the customer journey. Conversely, multi-touch models may be better for teams with longer sales cycles and numerous touchpoints.

Spreading your marketing efforts across multiple channels.

Ultimately, leveraging multiple channels like PPC and paid social media advertising can help you reduce over-reliance on any one single channel. As is the case with investing, diversification can help spread your efforts across a number of channels.

Portfolio diversification is underscored by variety. In finance and investment planning, it means combining a variety of assets in hopes of reducing the overall risk of your portfolio. The overall goal is risk management. By holding numerous assets, you help eliminate specific risks, such as reliance on industry. Because it can be assumed that different assets rise and fall at different times, diversification is defined by the idea of mitigating the returns of your overall portfolio.

The same can be said for digital marketing. By expanding the breadth of your marketing services, you only increase your reach. Attribution models exist as a way to correlate channels and campaigns with ROI. And tracking and attribution become an extension of each other—and are used as a way to maximize digital marketing budgets.

Regardless of which channels your financial marketing team uses, clean data is the foundation for effective tracking. Need help figuring out what to do with your data? Contact us today.

How can banks make the most of their data?

Our guide discusses how to market the right products to the right people.

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Trenton Reed