Utilize Your FI's Marketing Data with Machine Learning.

As a subset of artificial intelligence, machine learning is a powerful tool that can be applied in many areas of a bank or credit union. Some of these applications include automation, improving customer relations, engagement, forecasting, risk management and more. Chances are, you’ve heard of – and even used, AI before. If that’s the case, then what is machine learning?

Machine learning is an application of AI. It involves models that improve AI’s performance when it’s given more data. Here are some ways machine learning can benefit your financial institution in the marketing space.

Machine Learning Can Identify Meaningful Trends.

Machine learning for financial marketing involves collecting and preparing relevant data and choosing appropriate algorithms and training modules to identify trends. A few steps you can take to help ML are:

1. Collecting Data – Gather relevant marketing and financial data. Whether it’s historic stock prices, economic indicators, customer behavior data, social media sentiment, etc. Make sure the data is consistent, clean and relevant to your analysis.
2. Model Selection – Choose the appropriate ML algorithm for your problem. For identifying trends, time-series models like ARIMA or machine learning models like decision trees, random forests, or deep learning models (e.g., LSTM or CNN for time-series data) are common choices.
3. Training the Model – Split your data into training and testing sets to evaluate model performance. Train the model on the historical data, using a portion for training and the rest for testing. Hyperparameter tuning can help optimize model performance.
4. Evaluation Metrics – Choose appropriate evaluation metrics. For financial trends, metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), or metrics specific to financial analysis like Sharpe ratio or Maximum Drawdown can be used.

Using machine learning in conjunction with domain expertise with help your FI make informed decisions based on current trends.

Automate Marketing Tasks and Campaigns with Machine Learning.

After collecting the necessary data, you can utilize ML to focus on and automate marketing tasks and campaigns for your FI marketing efforts.

• Personalized Content – ML can recommend personalized financial products, services and content to individual customers based on their past interactions and preferences, increasing engagement and conversations.
• Predictive Analysis – ML can predict customer behavior, such as the likelihood of account use or the potential for upselling or cross-selling. Marketers can use these predictions to proactively address customer needs.
• Chatbots – AI-powered chatbots can handle routine customer queries, provide account information and assist with basic financial transactions, freeing up human marketers for strategic tasks.
• Automated Email Campaigns – ML algorithms can optimize email marketing by determining the best times to send emails, subject lines that resonate with recipients and the content that drives conversations.

Utilizing machine learning for task and campaign automation not only reduces manual effort but it also enhances marketing effectiveness, improves the member/customer experience and, ultimately, drives revenue growth. However, it’s necessary to maintain oversight and regularly update models to adapt to the latest, ever-changing marketing conditions and customer preferences.

ML Can Unify Customer Data from Disparate Sources.

Unifying disparate member/customer data from disparate sources can enable marketers to create an accurate, comprehensive view of their customers. There are a few different ways machine learning can play a pivotal role in helping to unify this data:

• Data Integration – First, collect and centralize member/customer data from several sources, like CRM systems, databases, social media, transaction records, etc., to ensure that data is stored in a structured and accessible format.
• Data Mapping – Identify common identifiers or keys that can link records across different data sources. These keys could include customer IDs, email addresses, or phone numbers.
• Cleaning and Preprocessing – Cleanse the data to address issues like missing values, duplicates, inconsistencies, and data format discrepancies. Standardize data formats and naming conventions to ensure consistency.
• Continuous Learning – Implement a system that continually updates and refines the unified data as new data becomes available. Machine learning models can be retrained periodically to improve accuracy.
• Data Quality Monitoring – Establish data quality monitoring mechanisms to identify and address issues as they arise. This involves setting up alerts for data anomalies and errors.

ML can significantly streamline the process of unifying customer data from diverse sources, leading to a more accurate and comprehensive understanding of members/customers. This can enable organizations to make data-driven decisions, personalize customer experiences, and improve overall business performance.

Machine Learning Improves Personalization.

ML can help financial marketers tailor their products, services and marketing campaigns to individual member/customer needs and preferences. Here are a few ways to help achieve that:

• Customer Segmentation – ML can analyze customer data and segment clients based on factors such as demographics, behavior and financial goals. This helps tailor marketing campaigns to specific customer groups.
• Real-Time Personalization – ML can provide real-time insights into customer behavior. Marketers can use this information to personalize website content, email campaigns, and product recommendations while the customer is actively engaged.
• A/B Testing Optimization – ML can automate the A/B testing process, rapidly iterating and optimizing marketing campaigns based on real-time results to maximize their impact.
• Loyalty Programs – ML models can analyze customer behavior to identify loyal customers and offer them exclusive rewards, discounts, or benefits to enhance customer retention.
• Data Privacy and Compliance – ML can help automate compliance checks to ensure that personalized marketing efforts adhere to financial regulations and data privacy laws.

ML allows marketers to create a highly personalized experience for customers/members, as well as improve satisfaction and retention rates and drive revenue growth. It’s important during this process to maintain transparency and ethical practices when using customer/member stat to avoid privacy concerns and regulatory issues.

Financial institutions can use machine learning to offer better prices, mitigate the risk of human error, automate repetitive tasks, and better understand human behavior. Train yourself to better understand how ML can work in your favor and boost your marketing efforts when you partner with Epicosity. We are your full-service growth partner who specializes in marketing efforts, growth and taking your FI to the next level.