There have never been more opportunities – or more challenges – in digital marketing, especially for financial services. With a plethora of channels and corresponding metrics, how do marketers decide which ones to prioritize? And in a digital landscape that’s constantly changing, how can marketers efficiently drive bottom-line impact?

Often, challenges and opportunities are one and the same, so let’s look at three key opportunities:

Content

Within several financial services categories, the digital space is dominated by aggregators like CreditCards.com. These sites are often at the top of search engine results pages for financial terms, above even bank and insurer websites, due to how Google values content and authority.

For example, a site for personal loans would have different pages that answer questions like “what is a personal loan,” “how do I apply for a personal loan,” “who are the best providers of personal loans,” and “how do I calculate the total interest paid on a personal loan”. All of this content helps a site rank for ‘personal loans’ related queries, including the high-volume generic term.

Because they offer great content, the aggregators attract many links from other high-quality websites. These links signal to the search engines that the aggregators are topic experts, increasing their authority at both the page and domain level.

The most forward-thinking financial services companies follow the aggregators’ approach: offering informational content that addresses a wide variety of searcher queries around a given theme and building links to this content from other sites.

Targeting

Marketing to targeted lists is nothing new in financial services. Yet when you’re investing in newer user-driven channels like search, display, and social, you need a way to optimize audience engagement while remaining compliant with fair-lending laws.

A compelling alternative to third-party data is lookalike modeling, using your own data. This involves looking at the terms used in paid search, tracking their effectiveness in driving account signups and determining individual terms’ approval rates. This approach identifies the purchase journeys taken by the most qualified customers.

In addition, consider how you can allocate keywords based on the type of searcher. A phrase like “what is a mutual fund” is one that a consumer would use. A term like “high yield mutual fund,” on the other hand, is more likely to be searched by financial advisers.

Modeling

Data needs to be leveraged to personalize the customer experience, hone in on the right prospects, and extract more value from current customers. Yet understanding how to actually harness data for revenue growth is a considerable challenge.

At the frontier of digital marketing are mathematical modeling techniques that ingest data from multiple channels, weigh each channel’s contribution to sales, and point to the channel mix that proves most effective in driving conversions.

This kind of modeling requires a lot of analytical rigor. It can be daunting to marketers with a traditional qualitative skillset. But advanced models can lead to more accurate projections and more objective decisions – which is why a growing number of organizations are hiring data scientists and other quantitative experts to help drive their marketing success.

These models offer a glimpse at the future of marketing, in financial services and other industries. Those businesses that most effectively utilize data for decision making will lead in engagement, new customer acquisition, and upsell.

–Tony Hooper – Vice President, Strategic Accounts East

As Vice President, Strategic Accounts East, Tony oversees the management and growth of the company’s largest global account. Tony previously held positions in management consulting, including General Manager of Equus Group, serving global clients such as Unilever and American Express. He also spent ten years leading marketing teams in the Consumer Card business at American Express. Tony holds a dual degree in Economics and Mathematical Methods in the Social Sciences from Northwestern University and an MBA from The Wharton School, University of Pennsylvania.