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How to use data analytics to improve project outcomes

Moira Alexander | March 21, 2017
Worldwide revenues for big data and business analytics is expected to grow to more than US$187 billion by 2019 and the project management industry is projected to hit US$5.81 trillion by 2020. Here is how some organisations are leveraging data analytics to improve their project performance.

Jason Levin (WGU) says that "probably the most successful project to date has been the Leadership and Communication course designed to educate students along the affective domain. Using quasi-experimental methods, we demonstrated significant improvement in retention and credit accumulation. Based on that research the course was implemented in the undergraduate Health Professions programs, which now serves about 1,000 students per month."

When it comes to the Chicago Bulls, Matthew says, using fan level and transactional insights to do an initial customer segmentation of their ticket buyers was a top priority over the last year. "We wanted to understand whether we had any vulnerabilities across segments and any gaps in our product portfolio." Specifically, he says they identified opportunities to further develop fans that fall into the young professionals and families segments and took fan level insights to further build out personas for these segments to help functions understand how to engage them."

Further, the Bulls used these consumer insights to accomplish the following:

  • Identify opportunities to further develop each of the segments
  • As the functions built out strategic plans, the Strategy and Analytics team was able to partner with them to establish metrics to evaluate success
  • Develop a new charity event targeted at young professionals and,
  • Make modifications to ticket products with a greater emphasis on creating Bulls Snapchat content

At a tactical level: "We used a subset of fan level insights to evaluate the likelihood to buy for potential ticket buyers. We use available demographic information combined with prior purchase history and digital engagements to evaluate a customer's purchase intent and the product that would best fit their needs," says Matthew. Using this information, their group was are able to significantly increase efficiency with sales reps and deliver the products customers desired.


What are the limitations of working with data analytics?

The Chicago Bulls Strategy and Analytics team learned two important lessons.

  • They had to begin with "why." -Why do we want to capture certain data points and what are the resulting use cases. "We have very limited opportunities with our most important fans to capture data. We need to ensure that we are capturing data that will advance our consumer insights and provide opportunities to more personally engage our fans in the future," says Matthew.
  • Finding the right time to use technology to sustain and accelerate a process. "We have found that leading with technology results in lower adoption and force fitting the technology into a less efficient process. By outlining the process and bootstrapping an analytical solution, we are better equipped to evaluate technology options and select one that really pushes the organization forward."

"There is a quote that has been attributed to Albert Einstein that says 'not everything that can be counted counts, and not everything that counts can be counted,'" says Jason Levens, of Western Governors University. "In education this is very true. Understanding what is going on with student and faculty psychology is critically important but difficult to measure. This is especially true if you are trying to measure these concepts in real-time and not relying on survey instruments. It is clear, by the research generated by scales like grit or mindset how important these data are to educational outcomes."


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