A data-driven mindset is about building skills, tools, and, most importantly, a culture that acts on data. Several companies throughout the world are realizing the transformative value of analytics and data. As a result, organizations are delving deep into the data at their disposal in order to study and apply it for growth purposes (O'Neal, 2014). This paper explains how I used the 10 business process framework outlined in data-driven decision making to solve a problem at the Bank of America where I work. In every business, data should be at the heart of strategic decision-making, because it provides knowledge, which helps the management to answer the fundamental business questions, especially on how to improve consumer satisfaction. By utilizing insights gained from the data, managers can turn them into actions and decisions, which enhance business performance (O'Neal, 2014).
The case problem discussed in this paper deals with the power of analytics in the Bank of America. The bank was struggling on how to counter the shrinking consumer base, whereby the management tried various retention techniques that focused on inactive consumers and these techniques failed to yield significant results. To solve the problem the bank turned to it data using machine-learning algorithms that predict which active consumers are currently likely to reduce their business operations with the bank. The analysis of the data provided relevant knowledge that triggered a targeted campaign, which reduced churn by 20% (O'Neal, 2014).
The ten simple steps
Start with a strategy
The primary strategic objective of the bank was to reduce shrinking customer base (Forbes, 2016).
Hone in on the business area
The most significant business area for the bank management to concentrate on achieving its objective is the customer (Forbes, 2016).
Identify your unanswered business questions
The key unanswered business questions relate to customers age, demographics, employment status, their needs and wants, lifestyle, the necessary programs for them in every category, and how new customers will be classified to reach them where they are through advertisement (Forbes, 2016).
Find the data to answer your questions
The best data to use in answering these questions is an internal and external data relating to the lives of these customers (O'Neal, 2014).
Identify what data you already have
Most of the data required to analyze the pressing issues are already available to the bank internally. The other data will be easily collected through a data collection system from the public domain established by the government (Forbes, 2016).
Work out if the costs and effort are justified
To analyze this data, professionals' data analyst will be required, machine-learning algorithm system, and data collection system that can access external data. However, the tangible benefits outweigh the cost, and thus the effort is justified (Forbes, 2016).
Collect the data
The bank utilized its internal data analyst and hired a data specialist to begin the processes of gathering the necessary internal data. The government institution will be paid the appropriate fee to access the needed external data (Forbes, 2016).
Analyze the data
After digging deep into the data, the analysts used a machine-learning algorithm to determine which active clients are currently likely to minimize their business with the bank (Forbes, 2016).
Present and distribute the insights
The bank marketers and advertisers used the insights they got from the data analysis to conduct a targeted campaign. They first decided to create different programs that targeted specific customers (in all classifications) to increase marketing effectiveness (Forbes, 2016).
Incorporate the learning into the business
The bank management decided to create different programs based on the customers' classification, whereby different tailored programs targeting specific customers were created and marketed continuously (Forbes, 2016).
Conclusion
The ten data-driven steps helped the Bank of America to segment or classify customers based on various characteristics they got after analyzing their data. This helped to remove the assumptions that the bank had concerning its clients. It helped the bank marketers and advertiser to change their marketing message and this reduced churn by 20% (O'Neal, 2014).
References
Forbes. (2016). Data-Driven Decision Making: 10 Simple Steps For Any Business. Retrieved from https://www.forbes.com/sites/bernardmarr/2016/06/14/data-driven-decision-making-10-simple-steps-for-any-business/#2cf6e9c45e1e
O'Neal, C. (2014). Data-Driven Decision Making: A Handbook for School Leaders. Eugene: ISTE.
