How a Growing Bank Leveraged Machine Learning to Increase Sales Pipeline Predictability
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.
