Predictive sales: Managing sales leads for an Online University. Get in touch. Call us at or write us at

Transcription

1 Predictive sales: Managing sales leads for an Online University. Get in touch. Call us at or write us at

2 What if you knew which sales leads to focus on for the best results? CAN s job is to solve our clients most difficult, complicated, and messy problems. They expect us to conduct experimental projects, turn around departments in trouble, and expose uncomfortable truths. They know that we care about their success, and that we love a challenge. CAN uses predictive analytics and data science to find patterns in data which help our customers predict what will happen next. The results are focused efforts and better decision making. Our solutions help companies improve sales, marketing, customer service, management, and strategy. Working with Top 10 Online University, we were able to help them push the boundaries of their success. They asked us to help improve their recruiting process that was bogged down by the number of requests for information. Contemporary Analysis Page 2

3 The Problem In a recent study of 1,000 Online Universities, our client s ability to engage students ranked them in one of the coveted Top 10 spots. Engaging each student when they first inquire about enrolling helped them attract and better manage students through their time at the University. However, this presented a challenge. The University receives over 100 requests a day for information from students that are interested in enrolling. Day after day, the requests keep coming. However, not everyone that requests information is a true prospect, and there is not enough money or time to build relationships with every person that requests information. A decision had to be made: Which prospects should recruiting focus on? The Solution After discussing the solution with the University s Analytics and Recruiting teams we decided that the best way to deliver the solution would be to sort prospects into 3 categories: 1. Prospects that are likely to enroll without extra attention 2. Prospects that are likely to enroll if given extra attention 3. Prospects that are not likely to enroll To find the best model we built hundreds of models and data visualizations. The best model found the following variables to be significant predictors of enrollment: 1. Educational Goals 2. Military Experience 3. Employment History 4. Employment Status 5. Age 6. Zip Code 7. City 8. State Contemporary Analysis Page 3

4 After presenting our results and discussing how to interrupt the model, we worked with the University s Analysts, Recruiters, and Executives to make sure that the results of the model were not only mathematically sound, but also reflected reality We verified the accuracy by running the model against a validation set of data not used in building the model. Our testing proved that the model was able to accurately predict the right outcome 84% of the time. The Result Once we verified the accuracy of the model, we worked with the University s IT, Analytics, and Recruiting teams to implement the results. We translated the mathematical model into a database script that ranks incoming prospects, prioritizes them, and adds tasks to the University s Customer Relationship Management system (CRM). Now incoming prospects are ranked and recruiting staff can feel assured they are reaching the potential students most likely to enroll. Conclusion With our help, recruiters at this Top 10 Online University have increased their close ratio of inquires because they can focus on the students who need extra aid in making the final decision and not chase students not interested in enrolling. This helped them improve what they are already great at engaging students and helping them succeed. Using predictive analytics, CAN helps companies sort through opportunities. This focus helps our clients make the best use of their scarce resources. They are better able to invest their time and money on the prospects that are most likely to close. We give them the ability to plan and gain control over their sales process. Contemporary Analysis Page 4

5 Get to know us. Learn more about CAN. Since 2008, Contemporary Analysis has used predictive analytics and data science to help companies of all sizes work smart. Our five products use data to help our clients improve their sales, marketing, customer service, management, and strategic plans. Our solutions are used by fast-growing technology companies, Fortune 500s, as well as small- and medium-sized organizations. Our clients are in a variety of industries including construction, insurance, education, healthcare, government, not-for-profit, software and engineering. Our vision is to make predictive analytics simple and affordable because all companies, not just the largest, should be able to benefit from predictive analytics and data science. Our principles: 1. We care about business. Each business deserves a custom solution. Problems are our passion. 2. We solve core business problems. We make a big impact quickly. Value is our focus. 3. We don t have all the answers. We help our clients make better decisions. Less wrong is the goal. 4. We are technology agnostic. We focus on the solution. Technology is just a tool. 5. Our job is to solve problems, not introduce complexity. Our solutions are simple because our clients are busy. Contemporary Analysis Page 5