Predictive Analytics: The Future of Financial Marketing

Analytics has been at the core of retail bank marketing for decades. But despite the proliferation of data, the ability to effectively leverage insights for a personalized customer experience and enhanced profitability has remained elusive.

Subscribe TodayGiven the tremendous advance in analytic tools available and the processing power generated by cloud-based architectures, the banking industry needs to take a major step forward to meet the expectations of an increasingly discerning customer base. In short, the industry needs to move from using data to build great internal report of past events to using data to build great customer relationships and experiences based on future needs.

It is clear that the industry must move away from the siloed approach that has always defined banking, toward a model where the focus is on customer needs. This advanced analytic approach has the power to reinvigorate the relationship with customers while increasing satisfaction, improving profitability and building trust at a time when large and small competitors are providing new customer-centric digital solutions.

A report from Aite Group examines the state of marketing analytics, the data sources available, analytic techniques being applied, and how digital interactions with consumers will evolve in the future.

Marketing Trends

Most financial organizations realize they have a massive storehouse of data, but few know what data is important, or how to leverage this data for increased revenues and lower costs. And as channel proliferation increases, the ability to know what channels are the most effective and efficient to reach any individual consumer is getting more difficult. According to Aite Group, “FIs will have to figure out how to better insert themselves into a digital world and find unique ways to engage consumers in interactions based on their needs, life stages, aspirations, and account spending.”

Some of the market trends that marketing must take into account include:

  • A need to generate customer relationship revenue
  • Evolving consumer behavior and expectations
  • A continued focus on improved operational efficiency
  • The need for competitive differentiation through digital engagement

Differentiation Through Data

At a time when customers are interacting with their financial institution through multiple channels, the explosion in consumer data can help banks and credit unions generate key insights that can respond to new market trends and changing consumer behaviors. This can help organizations create better products and personalized experiences which can increase revenues and decrease costs. The results from a predictive marketing perspective will improve the ability to pull consumers in rather than push products out.

In the past, marketing analytics focused on what had already occurred … similar to looking into a rearview mirror. Today, analytics provides the opportunity to look into the future … similar to a ‘financial GPS’ … anticipating consumer needs. This is what is expected by consumers becoming accustomed to the predictive capabilities of giants like Google, Amazon, Facebook and Apple (often referred to as GAFA).

According to FIS’ 2016 PACE Index, 56% of consumers anticipate at least one life event with financial implications in the next 36 months. Nearly 75% of Millennials expect such an event during the same period, including tuition payments, a house purchase or the purchase of a car.

Interestingly, only one-in-three U.S. bank customers surveyed ranked their primary financial institution as the first place they would turn for major life events that required a financial investment. That leaves at least twice as many customers who may consider an alternative resource, particularly when it comes to investing or retirement planning.

Despite the desire to use “big data”, most financial organizations have had limited skills, challenges dealing with data sources and silos, had limited budgets and/or lacked organizational support. Today, many of those hurdles are gone since the capabilities of analytics tools have improved and the cost of these tools (and data storage) have dropped significantly.

Turning Data Into Insight

According to the Aite report, FIs first need to determine what data is needed to answer specific questions. They then need to determine what type of analytics are needed to address that question. Thirdly, the marketing model needs to be implemented to determine which consumer need a particular product or service. And finally, FIs must learn how to use the marketing model to determine how to execute a specific campaign – in real time.

In other words, select the right data, analytic process, marketing model(s) and channel(s), delivering the right message to the audience identified at the moment the need becomes evident … or even sooner.

Data Sources

Just like a house needs a good foundation, advanced analytics depends on powerful and predictive data to provide the foundation for maximum effectiveness. In the new world of AI, machine learning and cognitive analytics, many of the old, traditional sources of data aren’t as important, while new sources of data take on added prominence.

For instance, it is difficult to predict behavior using just traditional demographics (age, income, etc.). Alternatively, new social media and behavioral data sources help monitor key lifestyle changes, which can be the winning formula for the ‘financial GPS’ view of the customer.

Some of the data sources identified by Aite Group that financial service marketers need to include are:

  • Channel preferences: Once a relatively minor component of marketing analytics, the channel(s) used by an individual consumer may be one of the most important data points in delivering an exceptional experience.
  • Social media insight: With over two-thirds of consumers using social media, this can be a valuable source of insight into behaviors and life events.
  • Mobile data: The amount of time on mobile devices continues to increase. Understanding how a consumer uses their mobile phone and their preferences of apps can be key in delivering the right solution through the right channel.
  • Consumer ratings and reviews: What apps and services does a consumer like today? What do they like about the application? This insight can help build a solution that will resonate with a consumer.
  • Bill payment behavior: Understanding who a consumer makes payments to, and how those payments are made, can provide valuable insights into how to serve the consumer with services that other organizations currently provide. The mission hasn’t changed even though the marketplace has … try to get an increased ‘share of wallet’ from the customer.
  • Personal Financial Management: Understanding a consumer’s financial goals is one of the most important components of predictive analytics. Without understanding where a customer wants to go financially, it is almost impossible to help them get there.
  • Geolocation: Before the massive acceptance and use of advanced mobile devices, the concept of providing solutions based on a customer’s location way nothing more than a marketer’s dream. Today, the ability to delver solutions based on location is one of the most powerful tools for financial marketers.
  • Weather and other external elements: Applying analysis of weather and other external factors on top of the data points above provides an added layer of predictability of response for marketers. Obviously, if the customer is given the option of interacting on digital channels, weather is less of an issue, but other external factors, like news events, regulatory changes, etc. can all be part of the customer journey to purchase.

Moving From Foundational to Predictive Analytics

According to Aite Group, “Foundational and advanced analytics provides information about what has happened in the past and what marketers should do about it now. Predictive analytics has been primarily used in banking for fraud and risk, but marketers are starting to consider what role predictive analytics can play in marketing to consumers, building personalized offers, and delivering engaging, personalized consumer experiences.”

As we move from traditional analytics to predictive analytics, we can leverage new technology to deliver marketing messages to customers. Beyond direct mail, email, and even digital marketing, new touchpoints, such as chatbots, and voice-first interactive assistants will provide new ways to engage with a consumer. “Artificial intelligence (AI) that is fueled by predictive analytics, machine learning, and natural language processing will be the brains behind the face,” states Aite Group.

Predictive analytics is the future of financial institution marketing, predicting when a consumer will experience a life event or need a financial service solution. This advanced form of needs analysis, once only available to the largest organizations, is now financially and operationally available to organizations of all sizes.

The combination of predictive analytic tools and advanced digital delivery options can guide the customer to the best financial solution at the most opportune time … sometimes before the consumer even realizes they have a need. This level of predictability and advisory positioning will foster an improved relationship and increased loyalty, providing financial institutions with the differentiation required to compete in today’s, and tomorrow’s financial services marketplace.

About the Report

The 19-page report from Aite Group examines the current state of marketing analytics, what data sources are available and emerging, what analytic techniques are being applied to find quality prospects, and how interactions with consumers will evolve in the future through new digital capabilities. It contains analysis from 20 late 2016 and early 2017 in-depth Aite Group interviews with senior executives at U.S. banks, vendors, and consulting firms that offer marketing analytics solutions or consulting service.

The report is available to clients of Aite Group with an option for purchase from non-clients. The table of contents and list of charts is available here.

Jim MarousJim Marous is co-publisher of The Financial Brand and publisher of the Digital Banking Report, a subscription-based publication that provides deep insights into the digitization of banking, with over 150 reports in the digital archive available to subscribers. You can follow Jim on Twitter and LinkedIn, or visit his professional website.

This article was originally published on March 6, 2017. All content © 2018 by The Financial Brand and may not be reproduced by any means without permission.


  1. Very good point about how regulatory changes or even weather can impact customer decisions! Financial marketing is definitely unique in this way, which is why I reached out to financial marketing experts to gather more tips. You can check them out here:

  2. I would respectfully argue that AI, BI, FI that claims to be able to predict the future from either a macro or micro analysis is a flawed strategy. We cannot predict, but we can prepare and we can use the mass of data that still generates limited knowledge to better prepare. As an example from the article, if the financial institution has data that points to a Millennial who is about to pay off his/her student loan, the institution should have a trigger set to begin a campaign for a home loan or something else appropriate. But in the end we are using historical data to build our next campaign. That’s not predictive but based on fact. There are too many variables to create any predictive campaigns. Rather than predictive, think “reactive.” When something outside the norm happens, be prepared to react.

  3. In my post, I’ve given several examples of how banks make great use of data gathered from the earth and sky and of predictive analytics to make a quick buck. So, I disagree that banks can’t or don’t know how to use predictive analytics. If they’re found wanting by some – not me – in usage of data and predictive analytics, there could be other reasons: (1) Customers say they don’t click on ads, but they do in actual practice. The fact that 2 out of 5 most valuable companies in the world derive >90% of their revenues from ads is adequate testimony. Likewise, customers say they want personalized offers but, like in the example described in my post, they find them creepy when they actually get them. (2) Like all large companies, banks created silos to improve manageability. To de-silo their organizations for the sake of predictive analytics is not necessarily such a smart move – the loss from reduced manageability is certain, the gain from results of predictive analytics is not so certain.

  4. This just out:

    CEB research shows most consumers find marketing personalization creepy – via @gdaviddodd

  5. I am continually amazed and disappointed that so many ‘experts’ on data analytics refuse to review the work of analysts from the days before regulatory and political powers replaced the need to attract consumers with the need to employ good lobbyists. Since The Fed lowered rates to near-zero and partially deregulated Banks (continuing since the Bush/Reagan era , well before the 2008 collapse) Banks have not needed consumers so much for a source of funds. So the interest in consumer proclivities has since waned. The advent of digital data collection (an unstructured form of Big Data) served as distraction for commercial enterprise just as the channels it provides have distracted Bank Marketers. For some years now, FIs insist that they should become more digitally active, allocating more and more of their budgets to digital ads & channels – all the while knowing that the effectiveness (ROI, diversity of the franchise, cost per profit dollar) is less, and less controllable, than other channels. The fad of the ‘new’ data analytics still refuses to consider the progress made in the decades before the digital fashion took over, and the ‘revelations’ this author mentions are not new at all. Consider, for example, the work done 15 years ago associating our attitudes to product usage and profitability – leading to excellent predictions of profitability with the knowledge of consumers’ behavior and attitudinal predispositions – published in the Journal of Advertising Research March-April 2002 (Interactive Psychographics: Cross-Selling in the Banking Industry). Big Data does not furnish such specific and dependable information as the combination of customer (the Bank’s) behavioral and descriptive data + secondary data (credit, compiled demographic & lifestyle data + Good survey research data (a lost art itself). Lifestyle changes, for example, are one of the indicators that correlate with simple age/income segments, attitudes toward risk, opportunity, money. – and we’ve been able to specify that on an individual basis for many many years now. But the new wave of digerati seems to refuse to make use of this and other bots of wisdom. To refer the industry to Social Media data – largely unavailable at the individual level – is further encouraging the faddish following in lieu of building on the wisdom that behavioral scientists decided and proved decades ago. In effect, the advancement of FI marketing and planning rests on the courage of Bank Management to seek the real answers – if they really care – to better forms of the questions at hand. Science has taken a vacation from FI marketing, and Entertainment has been ushered in.

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