Deposit Growth Challenges in the COVID-19 Virus Environment

The outbreak of the COVID-19 virus in the US has wreaked havoc on the nation’s economy. Financial Institutions have been particularly challenged as the rapid increase in unemployment has led to a decline in income deposits. However, it appears that the CARES Act relief payments for individuals and businesses have temporarily off-set the decline in income deposits. As the country works its way out of the economic slowdown caused by the virus, Financial Institutions will need to continue their focus on retaining and growing deposits as the government stimulus abate in the near future.

Here are four strategies financial institutions can deploy as we work our way through the crisis, to help deepen relationships with existing customers and enable continued deposit growth.

 

 

A common theme amongst these strategies is the underlying importance of having well-developed customer insights to help deepen the engagement of your customers.

 

1-Develop and Elevate Competitive Savings and Investment Offerings

The COVID environment has shown a need for families, businesses, and individuals to have and regularly contribute to an emergency fund and to re-evaluate their retirement and savings strategies. Understanding which customers are savers and investors, and which customers are spenders will help banks target specific messaging to educate customers on savings options and strategy tailored to fit their lifestyle.

 

2-Digital Marketing Efforts

During the COVID crisis, consumers moved rapidly to live and work in the digital world. This experience will accelerate the number of consumers and businesses that move more of their financial lives online. Personalization of communications through digital channels can also enhance deposit growth.

Understanding the digital relationships of your customers will be a key capability to better target your digital marketing messaging.

 

3-Marketing Incentives

Developing customer rewards programs that include relationship bonuses and provide incentives on items such as dollars deposited, debit card usage, direct deposit, and online bill payment can also encourage deposit growth.

 

4-Community Engagement (Sponsorship and Events)

The Financial Services industry has done well to sponsor and support charitable causes related to helping our communities during this crisis. Continued support for local communities that have been hard hit by the economic slowdown associated with the virus outbreak will generate goodwill and brand awareness.

IFM has a proven track record of helping clients leverage near real-time financial behavior of their customers to grow and retain deposits as well as to increase the share of wallet and profitability.

IFM delivers a wide range of solutions from applying machine learning to predictive models, delivering life event trigger leads, and deep insight into customer channel preferences.

Contact IFM today for a free consultation and analysis.

For more information related to how IFM is supporting financial institutions during the COVID crisis, watch this video for more information.

 

 

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5 Ways to Increase Deposit Growth at a Regional Bank

How to Increase Deposit Growth at a Regional Bank

Retaining and increasing deposits are among the primary goals at all banks. Achieving these goals has been made a bit more difficult given the increase in product options and different types of institutions that customers now have to choose from. While deposits, in general, have seen some growth over the past few years, there has been a slight disparity in growth trends between community banks and regional banks. Whereas non-community banks saw a 26.7% rise in deposits in the last five years, deposits at community banks rose 30.7% in the same period.

What Can Regional Banks Learn From Community Banks?

While regional banks generally have a wider market to cover, there are some tactics employed by community banks that others may want to consider. Some examples include localized community involvement, creating calling programs to develop and strengthen relationships, and supporting local business organizations.

1. Develop and Elevate Competitive Investment Offerings

In today’s competitive environment, it is more important than ever for a bank to fully analyze and understand the financial behaviors of its customers. Knowing what types of savings vehicles are used and the institutions they use will help the bank develop competitive products as well as to educate customers on options offered within the bank. Many banks have digital account types that pay better rates and offer brokerage and investment products that customers may be unaware of.

2 – Digital Marketing Efforts

To gain an edge over your competitors, your institution should have a content marketing strategy as well as employ the use of various online marketing techniques for banks. Enabling account opening and a more favorable rate for digital-only access will be attractive to many customer segments. Personalization of product offers communicated through digital channels can enhance deposit growth.

3 – Create Product Bundles

Add value to customers by offering checking account product bundles that include rate bonuses on savings vehicles or discounts on brokerage accounts to attract and retain new deposits.

4 – Marketing Incentives

Developing customer rewards programs that include relationship bonuses on items such as dollars deposited, debit card usage, direct deposit, and online bill payment can also encourage deposit growth.

5 – Community Engagement (Sponsorship and Events)

Sponsoring charitable causes and local events are some of the best ways to support the communities in which the bank does business and to generate goodwill and brand awareness. Business bankers can also join local business chamber of commerce associations to bring the bank closer to the community and to establish relationships.

IFM has a proven track record of helping clients leverage near real-time financial behavior of their customers to grow and retain deposits as well as to increase the share of wallet and profitability.

IFM delivers a wide range of solutions from applying machine learning to predictive models, delivering life event trigger leads, and deep insight into customer channel preferences.

Contact IFM today for a free consultation.

 

A Quick Guide To Bank Deposit Growth

 

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Use Bank Customer Behavior Data To Determine When People Are Ready To Buy, Borrow, or Refinance

With the fluctuation in the finance industry, the lending environment is rapidly changing for banks, credit unions, and other lending institutions. Historically, banks have been relying on demographic data, including age, education level, gender, race, and geographic location to segment customers. However, the rising younger workforce has rapidly changed this traditional dataset used by banks, pushing them into a new era of customer behavior.

Banks that have adapted to using customer behavior to determine the borrowing habits of their consumers have found new ways to segment customers based on their consumption habits, needs, and preferences. The changing preferences of the young generation, coupled with technological innovations, are paving the way for banks to adopt a strategic approach towards consumer segmentation. Significant drivers are facilitating the transformation of analyzing behavioral data, thus helping lending institutions understand their consumer habits and abilities. These drivers can be categorized into the following areas:

 

Machine Learning

The rapid adoption of machine learning models enables banks to use predictive analytics to detect patterns within big data. This modern approach gives banks the ability to look at their customer’s historical activities to identify which trends would be of most importance to them as compared to relying on demographic data to predict consumption patterns.

Big data has redefined the banking sector to the point where loan opportunities are identifiable through data analytics. Big data and analytics are helping banks locate and target the right people for financial products by analyzing signals based on life events, behavior, and passive information.

Behavior-based signals are some of the concrete actions that consumers take to indicate that they are ready to purchase new financial products. For instance, transactional data can send signals to the bank that there is a potential customer for a mortgage or a loan to purchase an asset. A consumer’s data builds a profile of predictive signals that banks can utilize to provide different financial products.

Digital Services

The explosion of digital services and products that consumers use daily creates an opportunity for banks to acquire data sources to get a better understanding of their customers’ consumption behaviors. This technique is not new in the US, where companies were reported to have spent $20.2 billion acquiring third-party audience data and activation solutions to support their marketing activities. The banking sector will follow a similar approach to segment customers in ways that yield deeper insights, leading to more effective customer service strategies.

Changing Customer Base

Traditionally, banks and other lending institutions succeeded in demographic segmentation due to the customer classification that existed before the eruption of technology. Generation Z and millennials joining the workforce have transformed the banking sector by being socially aware of the technological advancements, which they use for most of their daily functions, including shopping. It is estimated that 61 million of the millennials will join the workforce by 2022, which is an excellent opportunity for banks to take advantage of the tech-savvy customers to sell their products. Given that the younger lot has little patience for brands that do not demonstrate an understanding of their desires and needs, banks will need to do an in-depth analysis of their consumption patterns to appeal to these consumers based on refined and personalized marketing strategies.

How Banks Can Leverage Buyer Persona

1 – Get A Clear Picture Of The Customer

By understanding customer behavior based on their needs, tastes, and preferences, product managers can utilize this information to acquire a higher customer base for specific products. Understanding website behavior, product interest, social media engagement, and email preferences combined with offline activities such as phone calls can help banks leverage digital technology to create products that appeal to their customer base.

2 – Prioritize Personalization

Today’s consumers demand that brands treat them as individuals through personalized marketing. IT executives in financial institutions can help in classifying data based on customer behavior as a way of helping product managers to develop products for a specific consumer niche. Personalizing each product and consumer can be daunting; hence, with the help of Risk Management Executives, the process can be split into categories to be accomplished in bits.

3 – Create A Seamless Customer Journey

Once personalization is complete, the next step is to tie the customer experience together across channels and devices. Banks must leverage in-house transactional data to help in building a continuous journey with customers. The secret is for the entire banking team to keep learning and looking for new ways to apply analytics for fun and profit.

Looking Into The Future

For the future of financial institutions, data will be the greatest asset that these institutions can utilize to build products for their markets. Banks that can combine internal and external data sources to create value will find themselves well adapted to the digital market that will make up for future generations.

For financial institutions, knowing their customers’ actual financial situation is more critical now than ever. Contact our team at IFM to learn more about how we have emerged as the leader in large scale transactional/behavioral analysis for generating detailed knowledge to better understand your customer.

 

A Quick Guide To Bank Deposit Growth

 

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Retail Banking Trends for 2020 and Beyond

The banking industry faces innovative retail banking trends in 2020 with powerful forces reshaping the sector and creating an imperative for change. Banks and other financial institutions must choose what course of action to take – to either lead the change, follow trends, or manage for the present.

Whatever their strategy of choice is, it’s critical for banks to develop new, innovative solutions by taking advantage of big data, transactional and behavioral analytics, digital technologies, and novel delivery platforms.

From making transactions move faster and smoother to the changing and evolving role of retail banks, it’s not entirely clear what the trends discussed below will mean for the sector and the financial industry as a whole. However, the consensus is that the retail banking trends for 2020 discussed below will favor consumers.

6 Retail Banking Trends for 2020

1 – The Expansion of Open Banking

Many view open banking as a European issue and a threat to traditional business practices – the latter is correct. It refers to any initiatives by banks to open their APIs to third parties, giving them access to the bank’s data and functionality. You can use the term open banking interchangeably with API banking, open APIs, banking-as-a-platform, banking-as-a-service, or ecosystem banking.

The concept for open banking encompasses the need for banks and other lending institutions to respond to consumer pressure for painless and straightforward financial experiences. For instance, to buy homes, transfer and receive payments, or manage their financial lives. Fintechs and other big tech companies have already started leveraging the API banking ecosystem to offer financial services.

2 – Real-Time Financial Products

Banks and consumers alike are driving demand for services and products that they can interact with in real-time. This development will see real-time payments become the expected banking norm in 2020. What’s more, the conversation is sure to shift from how banks can set up for a real-time experience to what they can do to become more competitive and attract clients by leveraging real-time payments.

APIs will play a significant role in real-time growth since the fintech community requires them to interact with the banking services that their customers need. Therefore, retail banking trends for 2020 will focus on setting up new, innovative real-time payment services that attract fintech companies and consumers.

3 – Commitment To Digital Delivery

2020 is already shaping up as the year of enhanced digital banking consumer experiences. The industry is ripe for change thanks to the development of new, incredible technology both within and outside the sector that supports digitalization.

For those still mostly offering traditional banking services, they will shift their primary focus to the integration of new technologies and the enhancement of digital offerings with an emphasis on more value and personalized client experiences.

4 – Always-On “Invisible” Banking

As the business world enters the post-digital age, financial institutions will seamlessly integrate their financial services into the daily lives of consumers. This trend has taken the moniker “invisible banking.” An example of an invisible banking transaction is direct deposits.

Technology has created what experts refer to as an “always-on” world where business opportunities appear and evaporate quickly. A time will come when it will not be enough to have the right products and services, but banks must also recognize the exact moment when consumers need them.

5 – Intelligent Assistants and Voice Banking

Thanks to the rapid consumer adoption of voice and digital assistants, it’s now imperative for banks and other lending institutions to seriously consider the implementation of these services. Statistics support this assertion with a 78% growth of voice assistants and smart speakers users in the U.S.

Already, a handful of large banks have invested in digital assistants, including Capital One, Barclays, BofA, USAA, and U.S. Bank. Some smaller institutions like Mercantile Bank of Michigan have also followed suit.

6 – AI-Driven Predictive Banking

The ability to observe, analyze, interpret, and catalog the actions of your bank customers (while respecting their privacy) allows you to design and deliver rich, individualized experiences that will help build customer loyalty during the post-digital age.

Therefore, the banking industry is leaning towards the consolidation of all internal and external data to build predictive profiles of their customers in real-time.

Banks with a competitive edge in the market will go a step further to help their customers optimize their spending, give them preferred access to excellent deals, and nudge their behaviors in a way that creates a better long-term financial health.

One AI challenge that many institutions face is finding a balance between privacy and proactive insight, which is where transactional and behavioral analytics apply.

The Bottom Line

As the financial services industry undergoes rapid change and retail banking trends in 2020, institutions must invest in transactional and behavioral analysis to remain competitive, increase customer experience, and meet strategic goals.

Since 2002, IFM has been providing clients with cutting edge technological solutions, near real-time insights, predictive machine learning-based intelligence, and behavioral-based triggers. IFM’s proprietary processes is what allows them to provide banks with a data standardization solution and near real-time behavioral insight.

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Is AI the Secret Sauce for Financial Marketing Solutions?

Among the more recent technologies, Artificial Intelligence (AI) could have the most significant impact on the financial services industry.

First discovered about 70 years ago, AI has transformed many industries already. From supply chain to retail and travel to education, AI has completely changed how work is done in these industries. The technology is predicted to have a similar impact on finance.

Common Challenges in Finance Marketing

Although financial service providers face many marketing challenges, most providers struggle with three fundamental problems, namely:

Commoditization

As the financial services market grows (thanks mainly to digitization), so does competition. Today the competition is so high that many financial services providers find themselves offering the same products.

Commoditization is a situation where the products and services offered by multiple market players are pretty much the same. When this happens, products from competing players can become interchangeable. As a result, consumers feel that they can move between service providers without losing value. Where there’s high commoditization, it’s very easy to lose customers no matter the quality of your branding.

Lack of Consumer Trust

For a long time now, financial service providers have complained about the lack of trust among clients. In a 2016 survey by the National Association of Retirement Plan Participants, for instance, over 90% of respondents said they did not have faith in their financial services providers.

Again, the chief contributor to the increased distrust is technology. After witnessing so many cyber-attacks and data breaches in the last few years, the majority of consumers feel that their data and money are not safe. The financial crisis of 2008 also seriously eroded the little trust consumers had in financial companies.

Automation

In most of the industries where technology is revolutionizing work, automation is one of the major highlights. In these industries, you’ll find many tasks being automated. You’ll also likely find robotic machines working alongside humans to complete tasks faster and with fewer mistakes.

Unfortunately, the finance industry has lagged in automation for several reasons, one of them being the delicate nature of the landscape. In finance, even one small mistake can have grave and far-reaching consequences. Compliance and regulations also make automation a big headache, often forcing providers to stick to traditional, familiar methods.

How AI Solves the Perennial Challenges

Although it’s impossible to solve all the challenges in finance completely, experts predict that Artificial Intelligence can ease many of the problems. Here’s how;

1 – Smarter Credit Decisions

More than three-quarters of consumers prefer to pay via credit and debit cards. Indeed, only 12% of today’s consumers still prefer to pay in cash. What this means is that the credit card segment is more important to financial institutions than ever.

Artificial intelligence provides for a faster, more accurate assessment of loan candidates – at a lower cost. Better still, AI-powered credit assessment solutions account for a wider variety of factors, leading to better-informed, data-backed decisions.

2 – Risk Management

In financial services markets, risk can be deadly if not given proper attention. Accurate predictions are critical to the protection of businesses.

AI will play a starring role in risk management going forward. Using superfast computers and AI solutions, providers can handle vast amounts of data in a short time. Cognitive computing (a branch of AI) helps to manage both structured and unstructured data, making it possible to catch potential issues early.

3 – Analysis of Customer Behavior

In the financial services industry, institutions find it difficult to develop the same deep relationships with customers that may exist with companies in other industries. Through transactional and behavioral analysis, artificial intelligence is empowering the finance industry with the ability to analyze money movement at scale so F.I’s can anticipate the future financial needs of an individual customer. Service providers such as IFM can work with banks to foster the development of A.I. solutions via IFM’s cutting edge technology that cleans and categorizes bank customer electronic financial transactions in near real-time. IFM’s data analytics service enables financial services firms to offer timely products and services to their clients and strengthens the relationship between a customer and the F.I. With IFM’s capabilities, a financial services firm can analyze client behavior and money movement – in near real-time – and can also trigger security mechanisms if patterns of transaction activity seem unusual.

4 – Personalized Banking

Personalization is the new way to market – even in finance. In multiple studies, consumers have made it clear that they are more likely to buy if the experience is personalized. In one study, for instance, 44% of respondents said they are likely to become repeat customers if a brand offers customized services.

AI currently offers some of the best solutions for personalizing the marketing of financial solutions based on consumer behavior and transactional analysis.

Bottom Line

Financial Services firms are faced with three common marketing challenges: Commoditization of products and services, lack of consumer trust, and the ability to automate solutions. Artificial Intelligence will help to solve these perennial challenges by providing an opportunity for smarter credit decisions, improved risk management, and a more in-depth analysis of customer behavior to provide a more personalized banking experience.

What strategy should your institution move forward with to solve these marketing challenges? Data Science experts believe that the key to developing A.I. solutions that guarantee better productivity and ROI rests on access to clean and categorized transaction data that can be utilized to power A.I. related solutions.

Reach out to our team at Insight Financial Marketing today to learn how IFM’s Intelligentsia™ service could have a positive impact on your institution’s ability to market the financial solutions of the future.

 

A Quick Guide To Bank Deposit Growth

 

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Cross Selling In Banking: 4 Steps To Predictive Analytics

According to the book, ‘Marketing Metrics’, it is easier to sell a product to an existing customer (a 60-70% conversion rate) than to sell a product to a new qualified prospect (a 5-20% conversion rate). With existing clients, businesses already know their clients’ pain points, and the clients may have already become loyal to the financial institution. In the banking industry, banks often have a variety of products, but a good fraction of current customers might only utilize one or two products.

It can be challenging for a banker to sell a full range of financial products to a single customer, so front-line employees may only master a few of the high-performing financial products. Fortunately, banks have a valuable asset: customer data. With the right approach, a financial institution can evaluate their data and generate insights on cross-selling opportunities. This strategic approach to cross-selling is where predictive analytics comes in.

Here is how predictive analytics can be used for cross selling in banking:

The Power Of Predictive Analytics

The commoditization of financial products makes cross selling in banking arduous. Since customers may feel that they can get a better deal somewhere else, they might pick the most affordable product from your financial institution and hunt for other products elsewhere. This commoditization has resulted in banks bundling multiple banking products in an effort to create higher perceived value for the customer.

However, push-based selling and “one-size-fits-all” campaigns might not suffice to lure the modern-day customer. They need access to valuable products, and they need it now. Any product you bundle with the rest of your financial offerings should add the most value to their lives.

Given that banks collect data through CRM software and online tools, they can use this data to identify what their customers need. The data provides insights into:

  1. What customer to contact first

  2. What to sell them

  3. How to communicate with them

Predictive analysis allows banks to evaluate buyer behavior through recent account activities and sometimes even online activities such as reviews and complaints. Instead of offering a single generalized offer, financial institutions can personalize their products to a specific prospect group which can improve a campaign’s return on investment.

Predictive Analytics Steps

1. Start With A Question

Banks collect vast chunks of data, and they will be nothing more than data without analyzing them. To successfully identify opportunities for cross selling in banking, they must create a question and look for its answers through predictive analysis. Unlike conventional business intelligence (BI) tools that tend to be retrospective in nature, predictive analytics tools should provide insights into the future. You can get answers to questions like:

  • What customer demographics are the most likely to churn?

  • What is the estimated number of leads the institution will get from a marketing campaign?

  • What are the odds of a customer purchasing product y after purchasing product X?

  • How profitable might a specific product package be over the next two years?

2. Collect Data

The next step is to identify and collect the data that might bring the bank close enough to the answers. However, the level of confidence a bank can have in its predictive analytics software will significantly depend on the quality of the data it collects. As long as the data meets a quality threshold, it will provide trustable insights.

For the financial institutions storing outdated, inconsistent, or even incomplete client data in their CRM, data collection can become quite time-consuming. As a result, the onus is upon bank managers to spearhead data quality management that lays the foundation for a streamlined process.

Data stored within banking CRM might not be sufficient for some predictive models. Banks might need to get data from other sources. Some of these sources include:

  • ACH transactions

  • Bill payment behavior

  • Geolocation

  • Personal financial management

  • Wire & check payment data

  • Credit cards and debit cards

3. Build A Predictive Model

Next, data analysts have to create a predictive model that will define and determine the probability of specific events happening. These analysts can leverage artificial intelligence and machine learning methods, such as deep learning or linear regressions, to predict this. Once the model is created, test data has to be used to assess the predictive power of the model. For models that do not meet the expectations of the bank, they can be fine-tuned to offer higher predictive accuracy.

Data normalization can help increase the accuracy of a data model. Normalization helps achieve greater overall database organization, reduction of redundant data, improved data consistency within the database, and makes database security more manageable.

Once an accurate model is created, it can be a game-changer. Managers only need to feed their normalized data into the models and get the output they need to make decisions for cross-selling in banking.

4. Pay Close Attention To Assumptions

The idea that the future will always mirror the past is a major assumption throughout predictive analytics models. While there is some truth to this, consumer behaviors do change with time. If changes occur to the behavioral assumptions you might have made when creating your predictive analysis models; the models can become invalid.

Also, the variables of the models might change with changing market trends or time. For instance, the financial crisis of 2008 was significantly driven to by the assumption that house prices would always go up, which was not the case. Banks should pay attention to these assumptions to ensure that their predictions are still viable.

Predictive analysis isn’t a silver bullet for achieving cross selling in banking. Not all variables can be predicted to come up with trustworthy insights. Everything from the weather to the country’s political landscape can change buyer behavior. However, predictive analytics offers a much better solution for insightfully allocating marketing dollars than running financial marketing campaigns on underdeveloped research and half-baked ideas. Predictive analytics can provide financial institutions with a much-needed competitive advantage.

Reach out to our team at Insight Financial Marketing today to learn how you can get started with predictive analytics and how to translate changes in customer behavior into opportunities for your business.

 

A Quick Guide To Bank Deposit Growth

 

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Benefits of Big Data in Banking

The banking industry generates an enormous amount of data every day. Some of it comes from ATM logs, ACH transactions, SMS and online banking sessions, voice response systems, and more. Years ago, it wasn’t possible to collect, process, or store massive and complex data sets. Businesses had limited ways, if any, to leverage such data.

Today, there are a variety of technologies that have made big data a pivotal innovation driver in different industries. Big data analytics allows organizations to explore vast data sets to uncover insights like patterns and correlations, customer behavior, market trends, and so forth. This information helps managers to make informed decisions.

Impact of Big Data in Banking

Any financial institution that doesn’t jump onto the big data analytics train will have itself to blame for losing revenue. Studies have shown that the banking sector can attain about 18 percent revenue growth by making use of big data.

According to C-Suite banking executives, the modern customer wants highly personalized services. Big data in banking can help to meet customer demands, grow their business, and improve security and compliance. Here’s how banks can achieve this.

Enhanced Risk Management

Banks utilize business intelligence tools to identify potential risks related to lending money. With big data algorithms, lenders can identify customers with poor credit scores and decide whether to approve or decline their loan application. Big data analytics also assists banks in evaluating market trends and determine the opportune time to raise or lower interest rates for specific clients.

Big data in banking reduces the chance of data entry errors when filling out forms. By analyzing customer data, the system detects anomalies. Similarly, the bank can detect irregular transactions and potential fraud incidents and act accordingly.

For instance, if a person usually makes payments using a credit/debit card, an attempt to withdraw all their funds via ATM should be a matter of concern. It could mean a fraudster is trying to steal from the customer. The bank can call the account holder to clarify if the withdrawal is legitimate. Analyzing transactions using big data analytics has helped banks to ward off many fraudulent actions.

Personalization of Banking Solutions

Clients today detest the traditional one-size-fits-all approach to banking. People want banks that understand their needs and present sensible solutions. Consumers are likely to ignore banks that continually send mismatched offers. Annoyed customers won’t browse the rest of the portfolio, yet it could contain more exciting products.

Insights from big data analytics can help marketers to identify the type of products customers already have and what they would possibly want. They can then target individuals with products and services tailored for them from the point of understanding their needs. By doing this, banks can solve existing problems, win customer loyalty, and differentiate themselves from other financial institutions.

Accurate Cross-Selling

Big data can help banks to cross-sell auxiliary products more effectively by performing predictive analytics using wire data, check data, bill pay data, and credit card/debit card data. To succeed, the organization should focus on the value a product brings and the propensity of an individual to purchase it. A high-saving customer, for example, may be interested in tax-free investment opportunities like mutual funds.

Without information, organizations cannot avoid spamming consumers with unwelcome offers. For instance, it’s not worth the effort to market a short-term loan to a low-spending individual who is struggling with debts.

Banking technology and big data tools such as Hadoop and Fiserv can help automate the job. They can search through large data sets and enable financial institutions to make insightful decisions.

Transaction Channel Identification

Banks can benefit from understanding their customers’ preferred payment channels. Take the example of a business customer who prefers to make payments using paper checks. A business banker can reach out to this client and discuss treasury management service options that could help the customer’s business processes.

Final Thoughts

Businesses that are lagging in the big data analytics race are undoubtedly losing out in many areas. By utilizing big data in banking, banks are winning and retaining customers by offering personalized services and heightening security. Banks, on the other hand, are discovering new business opportunities while making their workplaces more conducive for their staff.

By utilizing big data in banking, banks are winning and retaining customers by offering personalized services by learning more about their customers’ needs. Banks are also discovering new business opportunities while improving risk management.

Insight Financial Marketing has over seventeen years of experience in helping banks identify opportunities to improve customer loyalty, grow revenue, and reduce potential risk through big data processing and analytics.  Contact the IFM team to learn how your institution can begin to reap the benefits of utilizing big data in banking.

 

 

A Quick Guide To Bank Deposit Growth

 

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Using Big Data to Generate Mortgage Leads

Data trails have become an integral part of the modern consumer’s lifestyle. Every day, people leave traces of data on the internet, through bill payments, and even when making phone calls. 90% of the data present in the world today was produced in the last two years. For attentive lenders, these data trails can be a great lead generation tool.

Behind these sets of data sits information that can guide lenders into establishing the risk profiles of potential borrowers as well as unearth new business opportunities. The science lies in identifying the type of data on which to concentrate. The art is determining the kind of insights for which to look. Luckily, with the help of big data analytics tools, machine learning, and the right resources, it can be easy to use such data to revolutionize lead generation and customer retention in the mortgage industry.

Here is how big data can revolutionize lead generation in the mortgage industry:

 

Building the Right First Impression

The customer journey that lenders take potential customers through will have a significant impact on their final decision. Nowadays, digital properties have been playing a pivotal role when it comes to interacting with potential customers, as well as presenting the nitty-gritty details of loan offerings to them. In many cases, the customer’s experience with the company might start with a personalized marketing campaign that drives a prospect to a lender’s website.

If the experience raises some red flags or seems tedious to them, then the chances are that they will look for another business with which to work. For instance, asking customers several random questions only to offer them generic loans might put off some customers. With big data, businesses can analyze both internal and third-party data to come up with a consumer journey that creates the right impression off the bat. The data collected during this experience can also translate into how lenders handle customers throughout the lifetime of their loans, increasing customer retention rates.

Better Assessments

It is quite easy for people with thin credit files to be judged using generic credit scores. In many cases, these people could easily manage to borrow and pay back more than what lenders offer them. Big data can provide insights into the risk profiles of customers who haven’t tapped into enough credit throughout their life. For instance, a good number of millennials might not use credit cards, take out car loans, or even work as salaried employees. This generational behavior makes it unfair to judge such mortgage leads under the generic mortgage models.

However, these people do pay phone bills, own bank accounts, and use a mobile payment app. All of these pieces of data can be significant indicators of their risk profile. This information can produce a more thorough profile that can also apply to underserved communities that lack definitive credit histories.

Detecting Fraud

The mortgage industry is among the most fraud-targeted sectors of the economy. While lenders want to limit fraud as much as possible, they neither want to lose legitimate business nor run afoul with regulators for making aggressive rejections to loan applicants. Luckily, big data analytics can offer the balance for which lenders are looking.

Ideally, big data helps lenders, third-party data suppliers, and FinTech vendors to move past conventional fraud detection methods. These methods involved manual fraud detection processes and siloed data. Proper analysis of big data can limit the number of false positives in fraud detection and identify questionable transactions as soon as they are made. Artificial intelligence can help score the risk profiles of the different transactions against a number of variables. Although these analytics can reduce the cost of relying on conventional detections strategies, they require a complete change in how managers approach risk management.

Increasing Efficiency

Other than controlling costs and improving profit margins, the efficiency at which lenders can handle a loan throughout its entire life will have a significant role to play in how they generate mortgage leads and improve their customer retention rates. Data analytics can have a vital role to play in improving the entire loan application process, enhancing the customer onboarding process, and speeding up loan underwriting. With big data analytics and the consent of the customer, lenders can gain access to consumer data from third-party data providers. These data sources can include banks, employers, and credit bureaus- allowing them to form a better picture of the financials of their mortgage leads.

Machine learning can also be pivotal in preventing last-minute delays in the loan application process by flagging suspicious data points. For instance, if the suspicious activity is that the borrower had made large withdrawals or deposits into their bank account, the system will pick up on this and allow the underwriter or processor to request clarification. The customer can then send their feedback through the analytics application, making it easy to analyze their inherent credit risk.

With this better organized, more comprehensive, and easily searchable data, lenders can rely on the data points to provide high-quality customer credit files. Other than making the underwriting process smooth, these files can provide insights throughout the lifetime of the loan, offering ideas that can improve the experience of a borrower. Lenders can identify ways to improve their loan offerings, respond to customer feedback, and help customers out of tricky situations, all of which can improve their chances of them turning into repeat customers.

Big Data to Generate Mortgage Leads

Big data improves the scope and quality of insights drawn from borrowers’ data. With more emphasis on the analysis of data, lenders can both improve the experience they offer current customers and extend their services beyond the typical client base through the generation of quality mortgage leads. The onus is upon lenders to embrace big data analytics to be part of this remarkable revolution.

Reach out to our team at Insight Financial Marketing today to learn how your business can get started using big data to generate mortgage leads in a way that optimizes the engagement with each customer.

 

A Quick Guide To Bank Deposit Growth

 

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Predictive Analytics in Financial Services and Banking

A few decades ago, a simple financial transaction meant putting everything on hold and spending your entire day queuing at a bank. Fast forward to today’s world; you can now deposit, withdraw, and send money in only a few seconds while you go about your business. All this is thanks to modern technology. What’s riveting is that it only gets better. Financial institutions are now taking advantage of the 2.5 quintillion bytes of data created every day to improve service delivery. Through predictive analytics, for instance, banks can achieve a seamless customer experience while at the same time shielding themselves from risks and losses. Here’s what you should know about predictive analytics in financial services. Read more

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The Metrics Behind Digital Marketing For Financial Services

The digital migration is swiftly revolutionizing the way customers buy products and services. Now that digital banking is used by approximately 51 percent of the world’s adult population, financial institutions should focus on creating a sustainable digital marketing program for a fully digital world. This starts by understanding the applicable digital marketing metrics. The following are the six categories of metrics behind digital marketing for financial services:

1. Traffic Metrics

Traffic metrics are mainly measured and monitored during the traffic generation stage. They are very crucial for both SEO and Pay-Per-Click digital marketing strategies. There are several aspects to consider when evaluating traffic metrics, including site traffic and sources of traffic.

Site Traffic

Significant changes in the overall website traffic can give you an insight into how effective a particular digital marketing strategy is. When evaluating the overall site traffic, you should not only focus on the number of page views or hits your site gets, but you should also consider the number of unique visitors your website gets within a specific period. The more unique visitors your website gets, the higher the probability of acquiring potential customers.

Sources Of Traffic

Identifying where your website traffic is generated from and what specific keywords brought them to you can give you an insight on where you should focus your digital marketing campaigns. If search engines are the primary source of the most traffic, you should focus your efforts on SEO marketing. If most traffic is coming from social networking sites, you should focus more on social media marketing, and so forth. Be sure to explore other traffic sources that may prove to be beneficial for your business.

When evaluating the sources of traffic, it is important to assess both the number of mobile and non-mobile website visitors. As more and more people access the internet through their Internet-capable mobile devices, digital marketers must consider mobile traffic an important metric.

2. Engagement Metrics

Is your website content resonating with your website visitors? After reading your content, do they take any action and, if so, how consistently or regularly? Are website visitors downloading white papers and e-brochures or filling out forms?

There are various ways you can evaluate engagement metrics. One of them is by checking the number of clicks your pay-per-click ads receive. Another way is tracking the number of comments, likes, shares, and reposts on social media. You can utilize Google Analytics to track website and app engagement metrics, including page views, unique visitors, and the average time spent on your content.

To boost engagement, you should consider including at least one call-to-action on each of your landing pages, services pages, email, or any other marketing channels that presents a conversion opportunity. You should also review all of your communication channels so you can identify the ones that are generating your desired response. By doing so, you will be able to determine what to change and what to replicate in future digital marketing campaigns.

3. Retention Metrics

Retention metrics are all about establishing whether you are holding your prospects and customers’ attention beyond the initial contact. You should not only check the number of returning website visitors and social media followers, but you should also take note of the bounce-rate, opt-out rate, and the number of unsubscribes.

If the retention numbers outnumber the opt-outs, it’s a good sign that your marketing message is resonating with the target audience. If the retention numbers are decreasing, you should revise your messaging and align it with your target audience’s needs.

4. Conversion Metrics

While getting lots of traffic to your website is an achievement, it won’t mean much if your site visitors remain just that – visitors. The primary purpose of your digital marketing campaign is to convert website traffic into potential customers. As a financial institution, the conversion metrics you should pay attention to are the number of new account openings and new loan applications you get after launching your digital marketing campaigns.

5. Revenue Metrics

The success of your digital marketing campaign can be evaluated appropriately by revenue metrics. You can determine the Return On Investment (ROI) by assessing the website traffic that eventually converted into new business leads or paying customers. By evaluating this metric, you will be able to identify the areas in your digital marketing campaign that are driving sales and revenue.

6. Cost Metrics

This is where you evaluate the amount you spend to launch your marketing campaigns. You have to consider metrics such as the amount you spend on every direct mail campaign you make, every monthly blog post or newsletter you publish, etc. Be sure to determine how each of your marketing efforts is impacting the bottom line, and then use your findings to plan a viable strategy for future digital marketing campaigns and sales cycles.

Data Is An Asset

Everyone agrees that data is one of the most valuable assets any business can have. It’s not the data itself that matters, but what a company does with it. With lots of data at hand, financial institutions have to rethink the way they handle data to be more customer-centric, and, as a result, more profitable.

 

A Quick Guide To Bank Deposit Growth

 

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