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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.

 

 

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.