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Connecting with Customers through Data Mining

MSMR candidates work diligently for 10 months with a variety of projects both in and out of the classroom. This semester, the students are working with a compounding pharmacy in Federal Way, WA. This community-focused pharmacy aspires to increase a positive emotional connection with clients.  The MSMR students are tackling this project through data mining and customer relationship management (CRM) techniques.

“Conducting research for this pharmacy is both challenging and rewarding. We are able to use tools presented to us in a classroom setting and apply them to actual data sets. Our group is working with the pharmacy on two separate projects allowing us to take part in the organization’s culture and their business endeavors which I feel is setting the project up for great success,” says Courtney Flaten, MSMR candidate ’17.

Data mining includes collecting customer data through archival and survey research, building and evaluating models and creating effective recommendations to meet the organization’s goals. For this particular project, the MSMR cohort is divided into two groups, each with different research approaches and goals. One group’s aim is to increase word of mouth (WOM) awareness in customers and the other is to uncover the most effective communication channels for retaining prescribers.

The compounding pharmacy holds true to their values both with customers and behind-the-scenes.  One of the company’s goals is for the network of individuals involved with them to see the organization has a dedicated group of people who want to create a healthier community.

At the end of the semester, both groups are to present their findings of both supervised and unsupervised modeling found through their data mining research. Unsupervised modeling derives patterns, summarizing the underlying relationship between variables. For instance, when using unsupervised models one can identify products that are purchased together in order to inform the client of bundling or placement opportunities. Supervised modeling is used to predict the value of a specific attribute based on the value of other attributes. For example, this type of modeling can predict churn rate in the coming years based on trends from previous years and an analysis of possible factors. With a detailed strategic plan, the client can use the information from both reports to move closer to their business aspirations.

“Working with real clients has been an invaluable experience. Not only are we helping to solve real business problems, but we are also creating great networking opportunities for ourselves. Everyone wins!” says Nicole Wassynger ’17.

This is just one example of the MSMR program in action. In this year’s cohort, candidates have had the opportunity to work with at least three real-world clients. Not only is this great resume-building material, it is setting each candidate up for successful futures and careers.

To learn more about the MSMR program visit www.plu.edu/msmr or contact Mari Peterson at petersme@plu.edu.