Peer Benchmarking for Banking: Taking a Page From Millennial Retail Shopping Trends

Millennials are now the largest and fastest-growing segment for digital banking, growing by 21 million users over the past five years and representing 62% of the overall growth, according to a 2017 report by Aite Group. This fast-growing segment is also stressed about their personal finances. According to the recent Wells Fargo Millennial Study, 69% of respondents say they want to get over their anxiety about money.

As financial institutions look for creative ways to help millennials better manage their finances, they can take inspiration from adjacent digital markets like e-commerce and social media applications where data-driven personalization is making customer experiences more rewarding while at the same time creating new revenue generation opportunities for companies. According to CNN Money, millennials want to share and compare everything. Whether it’s using the customers also bought section popping up beneath purchases, or crowd-sourcing on social feeds, or deciding what to wear, what to eat, and where to travel; the gold standard for guidance is the advice of a friend.

Adriot Digital’s Millenials: The New Age of Brand Loyalty report also supports this notion, with a majority of respondents stating that a recommendation from someone they know, or someone like them, is influential to their decision to buy a brand, second only in influence to price.

How can financial institutions leverage this peer comparison mentality to deliver more meaningful digital banking solutions and services to their customers?

The first step is to quantify the definitions of peer and shopping trends so that they match the definitions that customers have. Grounding these definitions through a synergistic combination of big data, machine learning, and domain expertise is a great start. Banks have a treasure trove of transaction and account level data. As the data flows through banks’ multitude of data marts, they can normalize and cleanse the data to generate a single source of truth.

The next step is to enrich and analyze the data using machine learning and domain expertise to derive meaningful insights and enable peer benchmarking. For example, analyzing user-associated financial data over a period of time is an organic way of finding out how certain financial behaviors impact overall financial wellness. These aggregated data insights combined with financial indicators like debt-to-income ratios help to identify peers or people-like-you which are far more accurate than the peer groups identified using simple demographics of age and location.

Peer benchmarking allows financial institutions to deliver more meaningful insights and just-in-time recommendations to customers and in turn, empower them to learn from the best practices of peer groups to create financial wellness.

Improving the Digital Banking Experience Through Peer Benchmarking

Today’s digital banking population, especially millennials, want more from their online banking experience. They want forward-thinking services that allow them to seamlessly manage their financial lives. According to the 2017 Aite report on Digital Banking Customer Engagement: Adoption, Usage, and Satisfaction, in addition to spend management, millennials are increasingly demanding digital banking solutions to help educate and guide them in their journey to financial wellness – this includes financial coaching and education, peer comparison, and guidance on financial decisions.

As a result, there is a tremendous need for financial institutions to leverage data analytics to identify financial-behavior driven customer peer groups, unearth best practices and common pitfalls to avoid, and provide tools that guide customers in executing decisions that are in line with their financial goals.

Peer Comparisons Grounded in Big Data, Systematic Machine Learning & Deep Domain Expertise

Where should financial institutions start? It is all about harnessing and leveraging the full strength of massive data sets – i.e., Big Data – by first making the data machine learning-ready through cleansing and normalization of data so that machine learning techniques can consume it optimally to generate actionable insights. This creates value for customers, particularly millennials, and the next generation of customers.

Then, financial institutions can assemble these insights in a seamless, stable, and sustainable way that is capable of delivering predictive and prescriptive insights to customers relative to their peers.

The key components to creating this functionality include:

1. Big Data: Massive Data Set

Creating a successful predictive and prescriptive digital banking model requires an enormous amount of data across a variety of use cases to deliver the level of insights required. The massive scale of data enables companies to perform granular customer segmentation, compare financial behaviors among peer groups, and optimize services to meet their needs.

Take Amazon, for example. With over 300 million users, Amazon represented 43.5% of US retail e-commerce sales in 2017. Amazon is gathering the types of questions people ask about their finances and the frequency with which they ask them to proactively enhance offerings and the type of digital interactions with users.

Financial institutions can also implement similar segmentation approaches and integrate with devices like Amazon Echo to answer a multitude of financial questions that are personalized to each user, as well as learn from each interaction.

Another example is Waze, the largest community-based traffic and navigation app in the world with over 65 million active users in over 135 countries. By allowing drivers to join other drivers in their area and sharing real-time traffic and road information, Waze aims to save people time and gas money on their daily commute.

Just like Waze, financial institutions can utilize the massive amounts of data from customers to offer peer benchmarking features, such as provide guidance on how to reach their financial goals – while learning from the mistake of others and tracking their journey towards success.

Making it all work together requires a big data plan and thoughtful big data architecture that not only examines current data streams and repositories from crowd-sourced data but also accounts for specific business objectives and longer-term customer and market trends. Just as important, the data needs to be cleansed and normalized so that it is consumption-ready for machine learning techniques and made available to blend seamlessly with the overall customer experience.

2. Artificial Intelligence (AI) & Machine Learning

While simple data visualization techniques may yield useful insights, it is through new and advanced AI-driven tools, like deep learning, that we are better able to identify patterns in massive amounts of structured and unstructured data sets. AI agents that not only gather and analyze data, but then learn from that data analysis to adjust their predictions for better performance results, or to predict future customer behaviors, are key. We are in the midst of an exponential growth phase of machine learning and AI domains where these techniques are maturing and becoming increasingly reliable at a very rapid pace, therefore enabling data-driven insights based on peer comparisons.

Take tech giant Google, for example, where chief executive Sundar Pichai had declared the company an AI-first company. AI and machine learning are being used to improve seven of Google’s products, each with more than a billion users. Google is also piloting a program in 25 US cities where it makes predictions about parking at a journey’s destination in Maps.

AI can now read emails, and make predictions about the responses people might make, and offer them as alternatives. This type of solution would be beneficial for financial services customers as well, predicting how long it will take for customers to get from their current situation to their desired financial destination with recommendations along the way that are informed by peer comparisons.

Robinhood is a free stock-trading app which has 3 million users and over $100 billion in transaction volume. By combining machine learning and user-research, Robinhood provides customers with unique tools featuring its proprietary data in a user-friendly interface. Robinhood for Web provides socially-driven tools for investors, featuring new information on each stock-detail page, including the number of people who own that stock and the average price they paid. There is a People also bought feature that shows which other stocks people have also bought. Financial institutions can integrate similar peer comparison features into their digital banking solution or partner with FinTech companies to provide innovative tools for customers.

With the ability of machine learning-based applications to improve efficiency, augment the decision-making process, enable peer benchmarking, deliver predictive insights, and improve the customer experience, the technology can offer financial institutions immediate benefits for their business and their customers. While machine learning relies on algorithms that can learn from data without relying on rules-based programming, taking AI to the next level requires applying rules of the road and customizing the information based on its need and use case. That’s where domain expertise comes in.

3. Deep Domain Expertise

When big data and AI-driven machine learning are combined with deep domain expertise, data science teams can create smarter platforms that convert outputs of these algorithms to automate day-to-day financial tasks and allow peer comparisons. This creates actionable recommendations for millennials and others to help them better manage their financial lives.

For example, IBM Watson Customer Insight for Banking uses advanced prebuilt industry-specific analytic models that combine predictive and cognitive capabilities. The AI-based solution enables dynamic behavioral segmentation to uncover actionable customer insights, allowing banks to create personalized sales offerings and marketing campaigns. Financial institutions can partner with technology providers that bring expertise from specific domains, as well as big data and machine learning, to deliver personalized banking solutions with peer comparison features for millennials.

Taking advantage of a broad spectrum of big data and analytics tools available – including machine learning and deep learning, as well as predictive and prescriptive analytics based on data from peer benchmarking – AI is forging a new era in digital financial services that is changing the face of banking in a way that creates more compelling and engaging services & solutions for customers.

Technology is transforming the way millennials and others behave and this is quite evident in financial services. By combining the power of AI with the capacity to learn from large customer datasets and establish patterns and correlations in customer financial behaviors, financial institutions can deliver more personalized digital banking solutions to target segments with recommendations based on peer benchmarking. This leads to more engaged customers, and potentially higher revenue and profitability.

Financial institutions can either develop these technologies in-house and/or partner with a third-party provider that offers those capabilities. While many financial service providers may excel at one or two of the key components for peer comparisons, the companies that thrive amidst the competition will likely be those that can combine all three capabilities to deliver financial wellness solutions for millennials.

Author’s note: The information, analysis, and opinions expressed herein are for informational purposes only. Nothing contained in this piece is intended to constitute legal, tax, accounting, securities, or investment advice, nor an opinion regarding the appropriateness of any investment, nor a solicitation of any type.