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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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Banking Tech – What is the new role of technology in banking?

A new age of technology exists, and things are moving very fast. Because of this, the banking sector has begun to utilize more of the modern technological advancements. Many of these advancements are leading banks to use data and artificial intelligence (AI) for higher levels of personalization and consumer fulfillment. These trends are only going to grow. Here is a look at the role new technology plays in modern banking.

Keeping Up With The Disrupters

While many in the banking sector were trying to maintain the status quo, disrupters turned up with digital banking and financial solutions utilizing modern banking tech and the mobile devices. These developments forced all banks to reevaluate and start to implement changes to how they service their customers. Now, most banks have applications that work fast and provide services people want their banks to provide.

Mobile technology is playing a significant role for banks, and fine tuning those digital offerings is something into which these banking institutions have put a lot of effort. Artificial intelligence (AI), big data, and predictive analytics are becoming the norm in the banking industry for these reasons.

Bringing Core Services Online

Along with the mobile push comes the push to put core banking services online in general. Banking portals of the past contained heavy limits and often did little to improve a customer’s experience.

Forward-thinking banks have started to utilize modern techniques to attract customers to their financial solutions, and allow customers to make banking decisions quickly and without hassle. Responsive design and a focus on turning technology towards customer service have created vast improvements for the banking industry.

Once again, a lot of this comes from investments in analyzing data and AI. This is especially true of online banking services that can help someone in real-time. By anticipating their questions, banks can provide automated and personalized solutions. Banking tech is assisting financial institutions to do more while saving money in the process.

Creating and Fostering Greater Levels of Personalization

People want to feel like their financial institutions know them, care about them, and are looking out for them. People interact with their banks and bank products all the time and with greater frequency. Each of these interactions is an opportunity for a bank to learn more about their customer.

For example, consider someone who regularly loads funds to their favorite coffee store mobile app every week for a year, from their bank account.  This transaction trend reveals one particular behavioral characteristic about the customer that, along with other data points, present your financial institution with information about the customer’s preferences and lifestyle.  Over time, as new transactions and transaction types are analyzed, and the historical amount of data available to analyze increases, new opportunities to cater to specific customer needs will be identified.

In the near future, a financial institution will be able to anticipate a customer’s needs and gain insight into what the bank can further do to retain that person and other people like them.  Advanced data analytic solutions make it easier for banks to identify trends and make individual suggestions for how to engage customers, that work on a more personal level.

AI, along with advanced data analytic capabilities, help banks learn more about their customers and how to take care of them on a personal level.  The fact that data analysis can create a more human experience for customers is one of those areas in which banks are finding a lot of value in, and is one area that will also create a healthy return on investment into the future.

Data Science and data analytics plays a crucial role for banks at every level and in every department. In this way, technology serves to unify many of the disparate banking departments so the bank can create a better, more secure, and personal experience for each of its customers. Financial Institutions who aren’t doing this, or planning to do this, will find themselves left behind. Reach out to our team at Insight Financial Marketing today to learn how your business can get started with the latest innovative solutions that will increase your bank’s ability to engage each customer with a more personalized experience.

 

A Quick Guide To Bank Deposit Growth

 

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