Deeper Data Analytics Help Financial Marketers Ace Digital Experiences

'Contextual intelligence' may be a mouthful. But the idea makes extreme sense for financial marketers. Take the gold mine of data most banks and credit unions sit on and blend it with locational and other data gleaned with permission from smart devices. This permits hyper-personalized offers based on deep understanding of what consumers really need and want.

The more deeply financial institutions understand consumers, the more effectively they can cross-sell and strengthen relationships with them. Studying retail customer demographics makes a good start, but the most forward-thinking financial marketers can actually harness the power of data analytics to learn about consumers’ lifestyles, goals and values — even their daily schedules.

Using these insights, banks and credit unions can personalize relationships right down to the moment. A rewards cardholder might receive an offer for double points at a retailer they are about to pass. A wealth management client might receive an invitation to an exclusive airport lounge a few minutes after landing.

Learning to assemble such “contextual intelligence” enables financial marketers to send the right messages at the right time. Institutions struggling with digital transformation may consider this level of personalization exceeds their reach. Yet most banks and credit unions already possess the necessary data or can obtain it relatively easily. Institutions can gain deep insights into consumers’ lives by blending historical transaction records with data from public sources and customers’ smartphones.

How to Organize to Produce Contextual Intelligence

A data assembly solution can layer over your institution’s existing backend. The goal is to unify data from different sources and channels and combine it to be analyzed.

“Blend weather and location data with a consumer’s historical transactions to determine that they are more likely to stay in and watch a movie at home on a rainy Friday night.”

Two particularly rich internal sources:

1. Internal data gathered across lines of business. Turning single-product relationships into multi-product relationships is easier and cheaper than acquiring new customers. Data silos impede such efforts at many institutions. Breaking down those silos yields a better picture of relationships and identifies opportunities.

2. Public and device-level data. This category includes information collected by a user’s mobile phone, including their location and even their type of movement. (Are they running, walking, or driving?) The devices can also provide broader contextual information like weather in the user’s area.

This data is what allows marketers to tailor their messages to situations as well as general preferences. For example, a financial institution could blend weather and location data with a consumer’s historical transactions to determine that they are more likely to stay in and watch a movie at home on a rainy Friday night. By offering that person a discount on a Netflix membership and pizza delivery, a marketer could arrange a welcome surprise after a long work week — and secure the steady revenue that comes with having a card on file at a subscription service.

With contextual intelligence, marketers don’t have to manually initiate campaigns for pizza-loving Netflix-watchers every time there’s rain in the forecast. Instead, people are enrolled in campaigns automatically based on specific context triggers.

As consumers interact with the various campaigns, the system builds an even richer profile of their preferences, which can be used to further optimize campaigns. Let’s say a consumer responds best to push notifications received during their lunch hour — the system will then “learn” to send communications during that particular time of day. The result is a virtuous cycle that makes a financial institution’s marketing more engaging and relevant over time.

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On the Flip Side, Safeguarding Consumer Data

Banks and credit unions are gatekeepers for extremely sensitive data to begin with, and unifying more information about consumers heightens the risk. Any solution they use needs to incorporate privacy by design. A fundamental of this: Recommendation services must be opt-in, putting the onus on the financial institution to provide real value to consumers.

Financial institutions also need to ask permission to send users push notifications and to use device-level data like location. By being transparent about how data will be used and letting customers make decisions about their data, financial institutions can make customers feel informed and empowered. This builds trust for the long term.

Data security is another important element of privacy by design. To harness the power of contextual intelligence, most financial institutions will need to give a vendor or multiple vendors access to their data. That relationship must be structured carefully to minimize risk.

Look for vendors who can work with tokenized or anonymized data that won’t allow customers to be individually identified. Institutions can also limit vendors’ access by putting the right rules for data queries in place. For example, vendors might be allowed to ask for information about groups of customers — “What were credit card holders in Seattle doing at 8 p.m. last night?” — but not about individual customers — “What was the credit card holder who lives at this address doing at 8 p.m. last night?”

By ensuring both data transparency and data security, financial institutions can harness the full power of contextualized intelligence for hyper-personalized marketing without sacrificing customer trust.

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Build a Hyper-Personalized Future, Or Cede to Competitors

As banks and credit unions face competition from fintechs and big tech companies, staying top of wallet has become increasingly difficult. P2P payment apps, mobile wallets, and other products let customers transact without pulling out a plastic card or cash. The rise of open banking in Europe and the U.K. means more third-party apps and services will be able to access financial institutions’ transaction data, further eroding traditional institutions’ competitive advantage. Now more than ever, it’s critical to focus on how to improve the customer experience in banking — or risk facing the consequences.

In this increasingly crowded field, hyper-personalized marketing informed by contextual intelligence is a major advantage. By delivering relevant, timely messages tailored to each customer’s needs, financial institutions can rebuild their interface with customers as well as improve their ability to cross-sell. The result: Traditional players will enjoy stronger relationships, greater share of wallet and a more secure digital future.

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