Millennials are living in a vastly different world than their Baby Boomer parents. They live in a time in which a phone isn’t just a piece of plastic used for making calls, it’s now “smart” and acts as an extension of oneself. A time in which “going shopping” or “depositing a check” no longer requires you to leave home. We are living in a world dominated by the rise of online/mobile and the demise of brick-and-mortar. This changing consumer landscape is being primarily driven by Millennials as they demand more personalized experiences.
Defining Millennials – Millennials (also known as the Millennial Generation or Generation Y) are the demographic cohort following Generation X. There are no precise dates when the generation starts and ends. Researchers and commentators use birth years ranging from the early 1980s to the early 2000s.
Millennial's Love for Big Data/Analytics
Millennials were raised in a time of unprecedented mobility and unparalleled access to information. They expect retailers to know what they want before they even know what they want. This expectation has been spurred by companies like Amazon. Amazon does a terrific job of customizing the entire consumer experience from the initial stages of attracting customers to post-purchase marketing, and it does this through Big Data/Analytics.
Big Data/Analytics enables retailers such as Amazon to build deep relationships with their customers by getting to “know” them. You will never have physical interactions with Amazon but the data they collect on you allows them to understand a considerable amount about you. Every time you interact with Amazon (by clicking on the page), they get to know you better which gives this retailing innovator the power to make your shopping experience more convenient and enjoyable.
Millennials have a different perspective on information than previous generations. They are willing to share information about themselves in order to make their lives easier. They are receptive to suggestions of products and services based on their daily habits. Companies like Amazon and Spotify understand this very well. They have figured out how to create a win/win relationship with millennials where data is collected and provided back in the form of recommended products and services that are highly personalized and valued.
Millennials and Financial Services
The information expectation may have started with companies like Amazon but it hasn’t stopped there. Millennials are demanding more customized experiences in all areas of their lives. Retail banking is no exception. Retail banking organization operate in a very similar manner to retail shopping which begs the question, “Why don’t credit unions and banks try to mimic the successful processes of companies like Amazon?” How does one credit union or bank separate itself from another unless it tailors the customer experience using Big Data/Analytics?
Retail banking institutions have a tremendous opportunity to capture market share by attracting the biggest generation since the baby boomers. Through the utilization of Big Data/Analytics, retail banking institution can:
- Predict the Next Best Product – There are common patterns customers reveal when buying products. For example, if you were to buy an iPod on Amazon, you will likely be purchasing an iPod case and a set of new headphones. With Big Data/Analytics, similar patterns can be uncovered at retail banking institutions. For example, someone who already has a checking account and a home mortgage may have a need for an auto loan or line of credit. Uncovering common patterns enables more accurate marketing alerts that Millennials actually want to read.
- Competitively priced loans - Utilizing Big Data/Analytics allows lenders to more accurately price loans and achieve a higher net interest margin. Many of the older, meet-the-market models currently used are overpricing loans based off of their risk categories. In this economy, many of the traditional lower credit score categories may be less risky than they have been in the past but this is failing to be factored into the pricing models of non-data-driven lenders. New data-driven models are exposing many missed opportunities in the high yielding, “too risky” credit categories. Many “noncredit worthy” Millennials fall into this category.
- Execute Affinity/Basket Analysis – Affinity/Market Basket Analysis tells a retailer that customers often purchase certain products together (e.g. – shampoo and conditioner). Putting both items on promotion at the same time might not create a significant increase in revenue. However, a promotion involving just one of the items would likely drive sales of the other. This same concept can be easily applied at retail banking institutions with checking accounts, certificates, loans, investments, etc.
- Target Marketing - With all the member’s data (e.g. – share accounts, checking accounts, car loans, mortgages, certificates, etc.), retail banking institutions are able to personalize their marketing process. The marketing team can identify all products and services a member currently has/lacks and post offerings for one of the products. Either to get a new product or transfer over a product from another financial institution such as a loan with a lower interest rate. This data serves two important purposes, customer satisfaction and revenue creation.
The Importance of Millennials on the Future of Retail
Big Data/Analytics serves many purposes but attracting Millennials may be one of the most important in the near future. Millennials are the biggest generation since the Baby Boomers. They are the generation currently entering the work force, getting married, starting families, and becoming the largest consumers. Failure to address this segment of the market will challenge the future viability of most banks and credit unions. The retail financial services industry is at a cross roads and needs to think about how to re-invent itself. Fortunately, this industry sits on vast troves of data about how their customers use their services. The next step is to employ Big Data/Analytics to turn that data into information that can easily be consumed by the Millennial generation.
Article originally posted by Austin Wentzlaff, OnApproach, on CU Insight on January 23, 2015.