November 28, 2018
Fast-evolving consumer expectations present one of the biggest challenges facing the financial services industry today. Consumers expect their financial institutions to deliver the same intuitive and dynamic digital experiences that tech giants like Amazon and Uber offer. They are looking for hyper-personalized experiences that simplify their financial lives and help them make sound decisions.
A recent survey of banking customers found that technology brands have established stronger emotional connections with consumers than their banks.
To replicate the digital success of their tech counterparts, banks should consider focusing on:
Removing friction from the overall banking experience
Supporting users through their financial decision-making process
Understanding and anticipating users’ changing financial needs
Uber is a really good example of an app removing friction from the engagement process by streamlining every step of the digital (and physical) journey their customers take. They addressed everything from the uncertainty of finding a cab, to giving the driver directions, sharing their ETA with friends, and paying for the ride.
Similarly, simplifying and removing friction from the overall financial management process will be key to creating an emotional connection between banks and their consumers. Digital banking consumers should be able to take care of all or most of their banking needs through their digital app of choice — open an account, apply for a loan, make a deposit remotely, and deactivate or reactivate a lost ATM or credit card.
Voice and chat channels are another great way banks can simplify the overall banking experience. Voice and chat offer an intuitive way for consumers to find answers to their questions, complete common tasks like transferring funds between their accounts, or pay bills.
Additionally, these dialogues with consumers give a bank the opportunity to update consumers on new features in the pertinent context.
Finally, analyzing the exchanges generated through conversational interfaces can help banks prioritize the points of friction they should address next.
Earlier this year, Bank of America introduced additional features for their virtual assistant, Erica. Erica offers voice and chat interactions to simplify common banking tasks for its mobile banking customers. Results to date have been promising. Erica recently surpassed more than 3.6 million users and has assisted with more than 12 million consumer requests to date. We can also assume that Bank of America is mining the conversational data to prioritize features they should offer next.
To help consumers make sound financial decisions, banks need to personalize their support and provide guidance and recommendations. Amazon is a good example of how personalized recommendations are key to deepening relations. It’s estimated that Amazon’s recommendation engine generates 35% of all Amazon sales. Its recommendation algorithms seem to be based on a few key data points including a user’s purchase history, items in their shopping cart, items they’ve rated and liked, and what other customers have viewed and purchased. Amazon analyzes these data elements and draws conclusions about recommendations that are then provided at the point of purchase or through email marketing. The key to their success is the personalization aspect.
To emulate this type of experience, banks need to develop and implement a robust data strategy that moves them from a product-centric focus to a consumer-centric focus. This means breaking down internal data and operational silos to organize data around the consumer.
Over time, banks should also consider mining unstructured data sources on their digital sites like search results and conversational data. They can also consider asking for consumer permission to bring in external data sources to get a better understanding of the consumer’s overall financial picture. A mash-up of these data elements can enable banks to recommend the next best products and experiences.
An effective way of engaging with consumers is through monitoring their accounts and transaction level data, analyzing the results, and pushing out helpful insights that they can take action on. Account level alerts that notify consumers about low balances or suspicious account activity is a good starting point, but more can be done.
For example, consumers with high cash balances in low-interest accounts are great targets for high-interest savings account offers. An insight that notifies the consumer of the recommendation and offers a seamless way of opening a new account and transferring funds can help build goodwill. It also gives the bank permission to ask the user to invest more in the digital experience.
As an example, the bank can ask the user to link accounts they hold at other financial institutions to go through the same monitoring process. This is what we refer to as the give to get model. By providing tangible value to the consumer, the bank can ask for additional data elements from the consumer that help deepen the relationship and improve the overall experience. The consumer and bank are creating a virtuous cycle of data and associated value.
Finally, to make a true emotional connection, any experience should anticipate a consumer’s changing needs and adapt product offerings, guidance, and experiences accordingly.
Netflix uses explicit and implicit data points to predict user preferences. Users tell Netflix about their preferences by giving explicit ratings to what they’ve just seen. Recently Netflix replaced its star rating system with a thumbs up/thumbs down rating which is widely understood to imply that you are training an algorithm to know what you like, according to Netflix’s Cameron Johnson. That simple change led to an over 200% increase in ratings.
The inclusion of a percent match number also reinforces the idea that these recommendations are personalized. Netflix also uses implicit data that tracks user behavior and user profiles over time.
These two data types are critical components of the analytics model that generates personalized recommendations for Netflix subscribers.
To anticipate their consumers’ needs, banks should consider building feedback loops into their experiences to capture user preferences and behavior. Soliciting feedback on insights and asking users to share their financial goals can help banks personalize the overall experience and anticipate financial needs.
Significant investments in machine learning (ML) and artificial intelligence (AI) are needed to fully implement these initiatives but there is much that can be done in the short term. Embedding data derived insights into digital experiences can help simplify day-to-day financial management and acclimate consumers to engage and share feedback and data.
The secret is to start small, iterate, and evolve capabilities over time.
Remember that it’s not only your capabilities that need to evolve. Consumers will also need time to adjust to a more prescriptive banking experience. Listen. Respond. Tackle one issue at a time to create traction and build connection and trust. As Desmond Tutu wisely said, There is only one way to eat an elephant: a bite at a time.
Author’s note: The information, analysis, and opinions expressed herein are for informational purposes only. Nothing contained in this article 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.