What is Data Science? Or Advanced Analytics? Or Data Mining? And why should I care and why do people keep trying to tell me about it?
Sound familiar? You may not have asked these questions yourself, but I bet you know someone who has. To establish context, consider that all decisions are made based on an analysis of available information. Even when doing something as mundane as driving your car down the highway, you visually gather data about the location of the cars, orange cones, and traffic signals around you before you make a decision to change lanes. Data Science is nothing more than an extension of that natural human behavior. Specifically, data scientists gather, analyze, and display information in a way that augments our ability to make better decisions. With the boon granted by drastically improved record keeping and data generation, this task is impractical, if not impossible, without the structure and power afforded by modern computing and analytical constructs.
All of the algorithms and processes and techniques and hardware developments are really focused around that one core human attribute: people can make better decisions when they can consume better information. So remember, when you hear, “Data Science can help you be successful,” what that really means is, “You can improve your business decisions by leveraging additional information.” This is a derivative of the principle that accurate information allows for better decisions and the certainty that your competitors are trying just as hard as you to find an edge to steal market share.
As an example, here is a case study on improving marketing ROI.
Business Problem: Phil is a marketing executive responsible for the implementation and outcomes of his company’s direct marketing efforts. Phil wants to understand and improve his team’s ability to engage with customers to drive business.
Phil’s company engages its customers in a number of ways including direct phone calls, promotional emails, and webinars. Each month Phil has a budget for his marketing spend. His team needs help identifying which customers in its database to target and how to approach each individual. To accomplish this goal, Phil has enlisted an analytics team to help him process all of his customer data.
Step one: Business Intelligence
Phil commissions a dashboard with KPIs and performance trends that that allow him to track the success of each type of marketing interaction across time and customer. He includes various filters such as industry and region that allow him split his customers and view distinct trends across dimensions. Phil and the other executives that he works with are pleased with their new ability to track the ROIs of their marketing spend. Before long, however, Phil is eager to move beyond the descriptive nature of Business Intelligence and start predicting what his customers will do.
Step two: Using Data Science to Predict
Phil reasons that if he can predict how his customers are likely to react to different marketing strategies, he will be able to improve the outcomes of his team’s efforts. He returns to his analysts and asks them to predict what his customers are likely to do when issued a phone call, promotional email, etc. By leveraging statistical models and machine learning, the analytics team is able to deliver the marketing department a tool that displays the predicted percentage of a positive outcome for each and every combination of customer and engagement type. Phil and his team are now able to create much stronger calling and mailing lists for the individual campaigns that they run. Phil is now getting much higher returns from his marketing investments and his efforts are beginning to clearly bolster his company’s sales numbers. Phil loves the advancements that have come with his use of data but knows that he lives in the real world and that his spend must conform with budgets.
Step three: Using Data Science to Optimize
In the marketing team meeting, Phil spitballs the idea of incorporating the campaign budgets into the analytics model that he uses for the company’s marketing. Phil runs the idea by the lead of his analytics team who agrees to create a solution for the marketing team. Using the predicted values, the marketing budgets, and the customer lifetime values, the analytics team delivers a tool using linear programming to Phil that will automatically plan out the marketing team’s best course of action each week. The algorithm feeds the marketing outputs into both the source customer database and a dashboard for Phil to monitor. Phil is now able to maximize the value that he gets from his customer data and has automated the previously time consuming manual efforts of creating marketing campaigns for each week.
Phil’s story is one example of how an enterprising business leader can independently progress along the analytics maturity curve and improve his company in a very meaningful way.
Come and meet Nathan at the Axis Group’s booth (#701) at Qonnections 2017, to discuss how you can implement data science in your organization to achieve better outcomes.
Nathan is an experienced analytics professional with in-depth knowledge of data science, business analytics, and business intelligence. He leads the Data Science Practice at Axis Group that focuses on solving business problems by providing business partners powerful, data-driven solutions and actionable insights by leveraging advanced statistical models, machine learning, operations research, visualization, and business expertise. Nathan is a two-time graduate of Georgia Tech with a BS in Economics and an MBA with a concentration in Business Analytics.