Customer Data & Analytics Blog

Why The Banking Industry Struggles With Customer Marketing And How To Get Ahead

Katie DeMatteis | 6 minute read

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"Banks must begin to act less traditionally and follow the path forged by other customer-centric organizations that allow themselves to be shaped by customer demand, using more mobile, more two-way, more “right-now” experiences to give customers what they want when they want it.”” 

— KPMG, Banking Outlook

Why Banking Is At A Critical Moment

It’s no secret that the banking industry is at a critical moment.  Stringent regulations, more demanding consumers, and increased competition are challenging the status quo.  Add to this major technological developments, and one thing is clear: Banks must either adapt or die.  Traditional silo’d approaches are a thing of the past.  Consumers expectations are shaped by companies like Apple, Amazon, and Google. They are demanding omni-channel access and all-in-one solutions.  While an app like Venmo may be used for transactions today, it’s a short stretch from there to tech startups replacing banks all together. Financial Institutions are facing 21st century challenges using 20th century processes and technology.  

 On top of this, only 32% of consumers say that they have confidence in banks, and 79% of consumers see their banking relationship as merely transactional.  Brand loyalty is almost non existent, especially with younger generations. In the last year, 18% of millennials have switched banks.  This is in stark contrast to the 10% of customers 35 to 54 and 3% of those 55 or older.  

So, what does all this mean?  What can banks do to save their reputation and their business?  The answer is surprisingly simple: Focus on the customer. 

According a recent survey, there are 5 key things consumers are looking for in a bank:

  1. Reward them for their business. This can be in the form of credit card points, discounts, or free money for opening an account
  2. Give “anytime, anyplace” access to balances. In today’s world, there’s no excuse not to have an app
  3. See the customer as a person. According to the report, “the majority of financial consumers want omni-channel services, regardless of age or income”
  4. Provide them with wealth-building advice. Don’t simply serve as a place to store money.  Use your expertise to add value to your customers’ experiences. 54% of customers expect their banks to suggest discounts, 53% would like proactive bill pay, and 52% want proactive product recommendations
  5. Show what they are spending on and how they can save.  As a financial institution, you have access to all your customer data.  There’s no reason you can’t leverage this data to provide personalized advice to your clients

How Banks Can Prosper During These Changes

Accenture has proposed a new paradigm called the “Everyday Bank,”and it makes a lot of sense. This so called “Everyday Bank” connects with it’s customers by building close relationships around three key roles: 

  1. Advice Provider: A bank must draw insights from customer data to recommend the best products and services for customers, whether or not these options come from the bank or from third parties, including other banks. The bank must parlay the deep data they have into a role as an advisor. Doing this at scale is clearly a challenge, and is something we’ll discuss solutions for.
  2. Access Facilitator: A bank must connect their customers to financial and non-financial products and services that make their lives easier, using the channels they are comfortable with and streamlining payment and processing flows
  3. Value Aggregator: A bank must bring their customers relevant merchant funded offers, everyday purchase discounts and loyalty programs that go beyond the basic propositions that are now common

While at first glance this may seem daunting, there’s no need to panic. This new paradigm gives you all the tools you need to be successful in a fast paced, technology driven world.  What powers this paradigm though? The answer shouldn’t surprise you. It’s data.  Deep, comprehensive data and analytics that can create and predict accurate and timely profiles of customers along with their needs and wants.

Most banks keep their data in thick walled silos.   Aggregating and processing this data is strenuous and difficult in and of itself. Add to this the task drawing thousands of data points into meaningful correlations, and suddenly it seems near impossible. Many banks are addressing this issue by hiring or outsourcing teams of data scientists to pool data and produce ad-hoc solutions or reports on a per use case basis. While this limited solution can yield some results, in a world of shrinking margins and rapidly expanding data this is not sustainable.   So what can be done? 

What An AI Solution Can Do To Enable The Everyday Bank

A popular way characterize data is the three D’s paradigm: Data, Decision, and Delivery. good.pngWhen collecting data, quality and completeness is paramount in order to give effective and reliable metrics. Once a reliable metric has been established, analysis and decision making needs to take place. Trends like customer-product affinity, for example, can be used to find at risk customers for churn. 

The final step is delivery to the identified segments and cohorts through outreach andpromotion. This stage requires the a genuine approach, which is why human to human outreach is still the mosteffective strategy.  Companies need to move through this cycle of data collection, decision making, and delivery quickly and accurately in order to resonate with their ever-changing customers and become an “Everyday Bank”.

To achieve this, there are two key things a business must doand do well:

  1. De-silo, and unify your data.  In this way you can create a comprehensive and integrated view of your customers and clients across all touch points, even enriching this data with third party information that goes beyond what they provide you themselves
  2. Leverage that clean and complete data to conduct deep analysis and draw correlations between thousands of data points.  In this way you can analyze and predict the behaviors that are most important to your business. Things like churn likelihood, propensity to certain products (cross-sell potential), and more

Here’s where an AI engine can help.  Implementing Artificial Intelligence/Machine Learning enables the creation of systems that are both smart and adaptive enough to solve problems faster and better than a human ever could. This requires a Dynamic Data Engine (DDE) and an Embedded Analytics Engine (EAE).

To have a comprehensive view of your customers, you need to unify data across all sources: online, mobile, in store etc.  A DDE can identify, cleanse, unify, and enrich this data in real time, and for each individual customer.  Once this has been done, an EAE uses the curated data to predict customer behavior.  It can tell you, for example, that people who withdraw a third of their checking account every month and also have a credit score below X are more likely to switch banks.  It can also show you exactly who these people are, and suggest offers and promotions most likely to keep them loyal to your brand.

AI has the power and the speed to take care of the first two D’s (data and decision) quickly and well, with little human oversight after the initial training.  The power of these systems lies in their flexibility.  With the data you already possess, they can find the signals your customers are sending, and suggest the right time and way to create meaningful engagement. This frees up your time and energy to focus on “Delivery”. 

Where Does ZyloTech Come In?

It’s no secret that engagement is tough and competition is fierce.  If your business can act faster, with more accuracy, and in line with customer needs, you will have a technological competitive advantage in the banking sectora sector characterized by its conservative models and slow adaption of new tech. 

Using the ZyloTech platform, companies have leveraged years of data on millions of customers to drive revenue and increase retention.  Among other things, our users have seen 200% overachievement in retention, 4x increase in overall lift/LTV, and 35% reduction in customer churn. 

What happens next?

If you’d like to learn more, or talk about a possible use case at your financial institution, please get in touch with us so we can explore this together and see where you goals may align with an AI solution.

Topics: Customer Intelligence