Inside Marketing: Machine Learning Is the Secret to Personalization

hand reaches out tap a digital brain button to activate machine learning
By Patrick Enderby

4 minutes

Technology enables credit unions to meet expectations and nurture the member journey—without being swallowed by data.

“Machine learning” and “financial services” are not two concepts that obviously go together—after all, financial institutions, particularly credit unions, rely and pride themselves on established trust with their members that traditionally stems from rapport built over time with one-on-one communications. However, the fast-evolving digital landscape has heavily influenced customer expectations across all industries, including how they choose and interact with credit unions.

Meeting widespread, real-time customer expectations for personalized communications (without getting drowned in data) is infinitely more difficult in today’s complex digital age, especially considering that 81% of consumer financial institution research now begins online, according to a report by Digital Growth Institute. Luckily, machine learning is an efficient solution to help marketers meet these expectations and subsequently nurture the member journey while saving valuable resources and reaching members in new meaningful ways at scale.

Machine learning has transformed how marketers process and apply data to optimally engage their audiences with the right message at the right time, via the right channel. It is an application of technology under the overarching umbrella of artificial intelligence, made up of algorithms that improve as they are exposed to more data over time. In other words, the algorithms learn and gain better accuracy with more available information.

Machine learning has become an intricate part of today’s digital marketing engines, though understanding its capabilities and benefits can be clouded by misconceptions about how (or if) it is used for personalized marketing.

Collecting and Interpreting Data in the Age of the Consumer

Having access to so much data is a double-edged sword. On the one hand, each person’s digital breadcrumbs collected and stored in a database is a treasure trove of information, which enables marketers to tailor personalized communications just for them. On the other hand, the sheer amount of data is overwhelming and can be meaningless if you don’t know how to decipher or analyze it.

More than three in four consumers are willing to share their data in return for personalized services, because they recognize that it takes sophisticated analytics to provide the customized experience they expect. Additionally, Digital Growth Institute estimates that consumers use an average of nine different information sources over a 60- to 90-day period when shopping for a financial product—this online consumer activity provides crucial data for financial marketers to tailor their services to a customer’s needs and distinguish themselves from competitors. But it’s impossible to manually comb through, make sense of and apply this data to each consumer.

Which is exactly where machine learning comes in. Machine learning deciphers these digital breadcrumbs and leverages them in real time for member communication that best reflects their financial interests and engagement patterns.

For instance, machine learning can figure out and drive recommendations for particularly relevant financial content using the member’s preferred channel, like social media, text or email. The algorithms predict the best time to reach out—not only the actual time in which they’re online and engaged, but also when they’re most likely ready for a conversation about your financial services after performing independent research. By recognizing these unique engagement patterns and financial interests on the individual level, credit unions can strategically streamline the personalized member journey.

How to Adopt Machine Learning in Your Marketing Practices

Machine learning is not a stand-alone tool to add to your CU’s existing marketing technologies, but rather algorithms that are embedded in other software. It’s important to keep this adaptive functionality top of mind when evaluating your marketing tools.

Marketing automation platforms are the most prominent example of machine learning in a software solution. Predictive technologies built into such platforms (using machine learning) can leverage data in real-time to anticipate buyer behaviors, modify and automate outreach, and inform the credit union whom to engage, when to engage and how to engage.

Now for the question of the decade: Will machine learning replace human jobs, specifically in marketing?

In short, yes and no. Machine learning is certainly transforming the role of marketers and, in most cases, amplifying their efforts. Machine learning will never replace the strategic thinking and creativity of your marketing team. Personalization delivered via machine learning enhances the member experience on a faster and broader scale than ever before; however, it does not solely dictate the experience. Remember, marketing professionals write the valuable content that machine learning works to make visible, and they’re behind the strategies that incorporate machine learning technology.

Are there fewer manual tasks for marketers to perform once machine learning is implemented? Absolutely, that’s why it exists. But there’s a difference between replacing humans and careers and replacing manual work. The industry will continue to grow with plenty of opportunities for marketers to evolve, unrestricted from being swallowed in cumbersome data.

Patrick Enderby has more than a decade of product marketing experience at the likes of Intel and McAfee, where he developed strategy and implementation of marketing automation to drive lead conversion and sales enablement. Now at Act-On Software, Enderby specializes in vertical marketing practices, AI adoption and predictive analytics for financial services, telecommunications, retail and manufacturing.

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