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From Leads to Deposits: How Marketing Attribution Modeling Can Drive Business Success

Shagun Mehta

Shagun Mehta

PR and Content Specialist
  • Last Updated: January 31, 2024

In This Article

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Attribution modeling is a significant challenge for most digital marketers.

Getting it right requires leveraging the right data and adopting tools and technologies that can track customers through their interactions with the brand — from first touch to conversion.

This article outlines different attribution models, and how financial brands can use them to become more strategic.

Understanding which customer touchpoint (or touchpoints) led to conversion is important for marketing teams.

It helps them get a clearer picture of what’s working — and what isn’t — and it gives them a better sense of the steps people take to become customers.

However, this understanding isn’t always easy to ascertain.

Attribution is actually one of the biggest challenges for digital marketers today.

For starters, as customers opt for more privacy while they’re navigating the internet, it has become harder to capture their interactions with digital brands.

In addition, there are ongoing debates in the industry around which attribution models make the most sense, which tools are the best to drive accurate insights, and how to use the resulting data to make informed decisions.

In the financial sector — this has resulted in 70% of financial institution leaders not knowing which marketing channels deliver the best results — and that’s a problem.

At the end of the day, if you don’t know where your customers are coming from, how can you know to invest more in that channel?

In this article, we’re exploring the different attribution models, sharing tips for how to map out your customer journeys, and providing insight on how to make the most of your data in order to drive deposit generation.

Attribution models in their many forms

A variation of these is the “position-based attribution model”, which still tracks every point of engagement, but attributes a higher percentage of importance to the first and last touchpoint.

This makes sense for products with longer sales cycles, where the customer has to spend a lot of time researching, comparing, and deciding.

Regardless of which approach makes the most sense for your brand, the true success of your attribution modeling will come from the systems you establish and tools and technologies you leverage.

Alternatively, other teams take a more comprehensive approach, looking at the various steps customers take.

“Linear multi-channel attribution” attempts to credit each channel that a customer interacts with before converting.

While tracking each interaction is useful for mapping the common customer journey for each customer persona and identifying trends, it does have its limitations in terms of attribution.

There could be duplicate reporting depending on the accuracy of the tracking and a lack of understanding as to which channel is actually influencing the customer to convert. In order to be successful, a multi-channel approach needs to be supplemented with the right data and analytics tools.

A variation of these is the “position-based attribution model”, which still tracks every point of engagement, but attributes a higher percentage of importance to the first and last touchpoint.

This makes sense for products with longer sales cycles, where the customer has to spend a lot of time researching, comparing, and deciding.

Regardless of which approach makes the most sense for your brand, the true success of your attribution modeling will come from the systems you establish and tools and technologies you leverage.

For many financial brands, this has led to a “single-touch attribution approach” identifying single channels as the core influencers in the customer journey.

Last-touch attribution, for example, credits the sale or the conversion to the last channel or asset the customer interacted with. While this offers a simplified, one-to-one attribution model, it fails to account for all the interactions that may have influenced the customer in the lead up to the decision.

The same is true for first-touch attribution, which only credits the point of entry.

In other words, it attributes the sale to the first touchpoint the customer has with the brand. These single-touch models are particularly useful for products with typically short sales cycles.

Alternatively, other teams take a more comprehensive approach, looking at the various steps customers take.

“Linear multi-channel attribution” attempts to credit each channel that a customer interacts with before converting.

While tracking each interaction is useful for mapping the common customer journey for each customer persona and identifying trends, it does have its limitations in terms of attribution.

There could be duplicate reporting depending on the accuracy of the tracking and a lack of understanding as to which channel is actually influencing the customer to convert. In order to be successful, a multi-channel approach needs to be supplemented with the right data and analytics tools.

A variation of these is the “position-based attribution model”, which still tracks every point of engagement, but attributes a higher percentage of importance to the first and last touchpoint.

This makes sense for products with longer sales cycles, where the customer has to spend a lot of time researching, comparing, and deciding.

Regardless of which approach makes the most sense for your brand, the true success of your attribution modeling will come from the systems you establish and tools and technologies you leverage.

Financial brands typically have myriad ways through which customers can engage with them.

They have call centers, social media channels, live chat boxes on their website, and physical branches, and that variety can make it extremely challenging to determine how to attribute a new product purchase or membership.

For many financial brands, this has led to a “single-touch attribution approach” identifying single channels as the core influencers in the customer journey.

Last-touch attribution, for example, credits the sale or the conversion to the last channel or asset the customer interacted with. While this offers a simplified, one-to-one attribution model, it fails to account for all the interactions that may have influenced the customer in the lead up to the decision.

The same is true for first-touch attribution, which only credits the point of entry.

In other words, it attributes the sale to the first touchpoint the customer has with the brand. These single-touch models are particularly useful for products with typically short sales cycles.

Alternatively, other teams take a more comprehensive approach, looking at the various steps customers take.

“Linear multi-channel attribution” attempts to credit each channel that a customer interacts with before converting.

While tracking each interaction is useful for mapping the common customer journey for each customer persona and identifying trends, it does have its limitations in terms of attribution.

There could be duplicate reporting depending on the accuracy of the tracking and a lack of understanding as to which channel is actually influencing the customer to convert. In order to be successful, a multi-channel approach needs to be supplemented with the right data and analytics tools.

A variation of these is the “position-based attribution model”, which still tracks every point of engagement, but attributes a higher percentage of importance to the first and last touchpoint.

This makes sense for products with longer sales cycles, where the customer has to spend a lot of time researching, comparing, and deciding.

Regardless of which approach makes the most sense for your brand, the true success of your attribution modeling will come from the systems you establish and tools and technologies you leverage.

Financial brands typically have myriad ways through which customers can engage with them.

They have call centers, social media channels, live chat boxes on their website, and physical branches, and that variety can make it extremely challenging to determine how to attribute a new product purchase or membership.

For many financial brands, this has led to a “single-touch attribution approach” identifying single channels as the core influencers in the customer journey.

Last-touch attribution, for example, credits the sale or the conversion to the last channel or asset the customer interacted with. While this offers a simplified, one-to-one attribution model, it fails to account for all the interactions that may have influenced the customer in the lead up to the decision.

The same is true for first-touch attribution, which only credits the point of entry.

In other words, it attributes the sale to the first touchpoint the customer has with the brand. These single-touch models are particularly useful for products with typically short sales cycles.

Alternatively, other teams take a more comprehensive approach, looking at the various steps customers take.

“Linear multi-channel attribution” attempts to credit each channel that a customer interacts with before converting.

While tracking each interaction is useful for mapping the common customer journey for each customer persona and identifying trends, it does have its limitations in terms of attribution.

There could be duplicate reporting depending on the accuracy of the tracking and a lack of understanding as to which channel is actually influencing the customer to convert. In order to be successful, a multi-channel approach needs to be supplemented with the right data and analytics tools.

A variation of these is the “position-based attribution model”, which still tracks every point of engagement, but attributes a higher percentage of importance to the first and last touchpoint.

This makes sense for products with longer sales cycles, where the customer has to spend a lot of time researching, comparing, and deciding.

Regardless of which approach makes the most sense for your brand, the true success of your attribution modeling will come from the systems you establish and tools and technologies you leverage.

Best Practices for Attribution Modeling in Financial Brands

There are a number of factors that will determine which attribution model you use: your company’s maturity, the number of tools you have available to you, what data sources you have and the state of that data, as well as your marketing team’s short- and long-term goals.

For brands that are opting for multi-touch models that provide a wealth of insights, here are the things you can do to set your team up for success.

Go deep on your customer journey mapping. Leverage all the data you have to fully understand how your customers become customers.

How are they arriving at your site? What content are they engaging with? How often do they visit the product page? Who are they talking to and where?

You should be set up to capture all these interactions and correlate them back to the same individual.

Qualitative data can also help here: take the time to survey new customers and ask them what helped them decide to become a customer.

Integrate your systems seamlessly. Think of your attribution model like an ecosystem of marketing data.

Your martech stack should be integrated in such a way that all data is readily available to your analytics tools. Plus, It’s only with the full picture that you’ll be able to make the best decisions for how to invest your marketing dollars.

Set up your data tracking correctly from the beginning. Use all the tools at your disposal to make data tracking as efficient as possible.

This includes setting up UTM tags to track traffic sources, and investing in tools that can store and sort through large quantities of raw data.

Make sure your data is clean and organized. The clearest insights will come from data that doesn’t have gaps or duplicates. Take the time to set data hygiene requirements and plan regular checks to clean your stored data.

These are all things that will bring increased clarity to your attribution model, ensuring you have the right information to invest in the right campaigns and channels, and enhance the ROI of your marketing efforts.

Leveraging Data and Analytics to Drive Deposits Generation

Today’s financial brands are increasingly competing with new brands and digital disruptors in the space — and that means they can’t afford to not take a data-driven approach to their marketing initiatives.

With all the customer data you have available to you, it’s key to invest in tools that can help capture data across the customer journey and provide actionable insights that lead to informed decisions.

With Fintel Results, for instance, our web-based tracking and analytics platform plugs into any customer journey in order to measure end-to-end performance campaign.

Using comprehensive tracking and campaign management — as well as real-time reporting — it helps teams measure the bottom-line impact of any marketing campaign or channel.

Fintel Results’s integration capabilities are an important feature, as they ensure that data is captured across various systems and analyzed accordingly.

With the right data and analytics platforms, your team can also build a culture of testing and iteration, reviewing performance in real time to determine what approach works best for each channel or campaign.

This won’t just apply to the campaigns you generate for new customers, but also for existing ones.

Ultimately, this will ensure that you’re allocating your budget efficiently, make your team more agile and impactful, and drive increased deposit generation.

Discover top tactics that financial experts and marketing teams are using to drive cost-effective and sustainable deposit growth in 2023 and beyond.

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