New Era Technology

Challenge According to the CDC/NCHS National Vital Statistics, banks lose about 10 percent of their account deposits due to customers closing their account. While half of that loss is attributed to factors out of the bank's control, such as death, divorce, or displacement, the other half of loss is attributed to the customer's dissatisfaction with the bank's fees, rates, products, or lack of convenience. For a bank that handles nearly $700 million in deposits annually, losing $70 million due to customer loss will seriously curb their ability to grow. A regional bank wanted to reduce customer attrition and learn how to predict which customers were likely to close their checking accounts within 90 days. This would give them the opportunity to take action to retain the customer. Their traditional analytics tools didn't have the capacity to uncover patterns, and human analysis certainly won't work to analyze billions of data points in real time. The bank knew they could use machine learning to analyze their existing data to reveal trends and insights and predict future behaviors and outcomes, but they hadn't used it before. For expert help with this project, they turned to New Era Technology. Our team had worked with them several times in the past and knew how to help them get the insight they needed. Solution Most machine learning projects take several months before seeing results. However, our client wanted to see a proof of concept quickly to determine how this would work and whether it would help them fulfill their goals. With a New Era Technology Machine Learning Jumpstart, we could help them meet this objective. Machine Learning Jumpstart During our Machine Learning Jumpstart, we assess the key business problem, assess the data, and build a machine learning model that delivers answers fast. To do this, we follow a specific, four-step plan that includes: Use case identification. Prior to beginning our technical work, we explored several use cases in a workshop with the bank’s business and technical stakeholders. Our team rated each potential use cases on different criteria, including the complexity, availability, and value impact of the data. We agreed upon the deposit attrition proof of concept, deciding it would drive maximum predictive value with minimal risk. Data processing. We inventoried and sourced the existing data, then cleaned and loaded it in the target on-premises environment where the models would be developed. We provided the option of loading the models in the cloud to enable additional ML models and more complex computations. Machine learning model development. Within three weeks, we began engineering the machine learning models, choosing the subset of data most relevant to the question, “Which checking accounts are likely to close in the next 90 days?” We selected the machine learning algorithms, then trained and tuned the model. Model insights integration. With a model in place, we met with with the bank’s stakeholders to present metrics to measure the model’s success, focusing on KPIs. The model returned valuable insight into the accounts at high risk of closing which allowed the bank an opportunity to refer customers to the bank’s retention team This end-to-end process generates daily predictions using real-time data in less than an hour with the bank's existing infrastructure. Educating the Team While providing the machine learning model was our highest priority, we also wanted to help educate the bank's team on key elements of machine learning, such as the process for training models and the process for generating predictions. This would occur organically by working side by side with their team. Optimizing Results The bank wanted to use machine learning to predict customer attrition more effectively than how they could do so through traditional analytics. They also wanted to understand the key metrics for evaluating machine learning models. In the secondary phase, we could optimize the model to capture model efficacy. This will allow the bank to optimize and expand the use case or use the model as a template that can be modified for one of the other use cases. The entire proof of concept took eight weeks, and the bank is now in possession of machine learning models that can be implemented in marketing campaigns in the next phase.

Solution The bank’s leadership needed to understand who their ideal customer was, how to leverage their data, and how to strengthen their sales pipeline. Knowing they needed help from a team of experts, they reached out to Fusion to assess the situation and help them achieve their goals. We knew machine learning would be the best way to identify their ideal customer and find new opportunities in the market. This data science method analyzes historical data to predict or forecast future outcomes, behaviors, and trends. The data learns from itself without human bias, preconceived notions, or explicit instructions. Also, because the volume of data is often massive, machine learning can find the patterns that a person would likely miss. With a plan in mind, we began the process of leveraging machine learning to increase their sales pipeline predictability and stability. Our Goal Define the Ideal Customer First, we needed to define exactly who the stakeholders believed was their ideal customer and determine aligned attributes they identify with their target audience. This insight would allow us to select high-value use cases for the machine learning models. Our Solution Sat down with stakeholders throughout each department of the organization to discuss characteristics of their ideal customer. Brought stakeholders together in a workshop to get everyone on the same page as to how they would identify their ideal customer Selected high value use cases Our Goal Understand their Data Machine learning initiatives are only as successful as the quality of the data. We needed high quality, valuable data that would support the questions we were asking and give us accurate, reliable predictions we could use. So, before we developed the models, we needed a solid foundation in place. Our Solution Identified all available data and performed an analysis to assess data quality and completeness to support the defined objectives. Identified where to find the best sources for data Analyzed information to determine where data needs to be improved or where gaps are located Our Goal Develop a Machine Learning Model With characteristics identified and clean data, we developed a model that leverages the significant characteristics for use against prospects or existing customers. During this step, we allowed customer data to speak for itself. Our Solution Identified data elements that should be input for the machine learning model based on the data profiling Provisioned a cloud environment and developed data ingestion Defined and developed machine learning predictive models that supported the defined use cases Executed the model against real data and assimilated the output to graphically show customer segmentation Our Goal Finalize the Ideal Customer Definition We used stakeholder inputs, data profiling outputs, and machine learning to let data and actual outcomes influence the definition of the ideal customer. Our team explained what the model said and the characteristics of the target customer and how that compared to the stakeholder's thoughts. Using our information, we could create criteria or attributes and the bank could use those to build customer personas. Our Solution Took data from the model to share insight into the true characteristics of their ideal customers. Built machine learning models that would score customer and prospect lists against the customer model Operationalized the model to use in marketing campaigns At the end of the project, we developed the prospect target list and scored them based on the machine learning insights. The company can use this to execute a more informed marketing plan and grow their customer base.