Senior Data Analyst - HR, LG&E and KU Energy
This session will look at how to apply the marketing concept of customer journey mapping to the employee experience. Whether we are looking at the new hire journey or the career journeys of our tenured employees, using journey mapping techniques can help us identify bottlenecks, target inequities, and ultimately introduce more transparency into our HR processes.
How to Map the Employee Experience using Customer Journey Analytics
The cross-fertilisation of ideas is important, and HR analytics has the opportunity to borrow techniques from other areas (in this case, marketing analytics.)
Additionally, being aware of the “customer journey” within our own employee base can highlight problems and pain points that we might not have considered before looking at the data (for instance, when we look at our hiring funnels, are there areas where a disproportionate number of minorities are falling out? Are there some job groups where the competition is higher for female applicants than male applicants, based solely on the kinds of jobs they are applying for?)
The concept of journey mapping with employee data can be used in the hiring process or when looking at career growth across the whole company, two uses that I will highlight in this session.
This session will explore:
- What is customer journey mapping?
- How can we apply customer journey mapping to the hiring process?
- How can we apply customer journey mapping to employee growth and development?
- What tools can we use for employee journey mapping?
- Introduction to a novel way of thinking about the “employee journey”
- Ideas about tracking employee growth and development
- Understanding how DE&I measures can be layered on top of the overall “journey”
- Introduction to some statistical tools (Python, PowerBI, SQL).
Share this session:
My passion lies in the intersection between the numeric and the human side of today’s pressing problems. I am a world traveler and a polyglot — data is just one of the languages I speak. I relish the challenge of communicating complicated information in a simple, straightforward manner, whether that is in writing or in presentations for audiences ranging from freshman college students to data experts. I work with big data sourced from corporate, academic, and NGO clients using a variety of tools to make data-driven recommendations and bring added value to business decisions.
Areas of Expertise: Data Visualization, Communication, Reporting, Data Analysis, Survey Data, Experimental Design, Data Collection, Data Science, Machine Learning, Applied Statistics, Statistical Programming
Statistical Methods: Generalized Linear Models, Nonparametric Statistics, Ridge and Lasso Regression, Factor Analysis, Network Analysis, Supervised and Unsupervised Learning, Machine Learning, Deep Learning, Natural Language Processing
Applications Used: Python, SQL, Stata, SAS, R, Excel, Tableau, PowerBI