Cape Breton University
Log in Sign up

Customer Profiling & Segmentation

Closed
Caddie Health
Caddie Health
Kingston, Ontario, Canada
Phil Ballyk
Phil Ballyk
CEO
1
Project
Academic experience
80 hours per learner
Learner
Anywhere
Intermediate level

Project scope

Categories
Market research Customer segmentation Lead generation Product or service launch Sales strategy
Skills
customer profiling sales prospecting customer data management internet research sales billing
Details

Reporting to the CEO, you will develop an prospect customer data base to support the launch of Caddie Health’s billing solution. Specifically, you will conduct online research and direct customer outreach to profile and segment Family Health Teams across Ontario to support the future sales team.

During the course of this project, you will complete the following:

  • Align on the key data points needed to profile and segment the ~200 Family Health Teams in Ontario (e.g., associated clinics, number of and names of doctors, electronic medical record platform used, etc.)
  • Conduct online research and phone calls to prospective customers to augment our existing Excel-based customer database of Ontario Family Health Teams.
  • Analyze and synthesize customer database findings, and recommend the highest priority customers to target at launch
  • Share the outputs with the Caddie Health team to collect feedback and identify recommendations for the Caddie Health sales team

You will be required to regularly meet with the CEO to provide updates on project deliverables and iterate based on feedback.

Deliverables
No deliverables exist for this project.
Mentorship

The student will work directly with the CEO for guidance, and will meet at least 2x per week to align on the deliverables, track progress, and confirm next steps.

About the company

Company
https://www.caddie.health/
Kingston, Ontario, Canada
Technology, Hospital, health, wellness & medical

Caddie Health is a pre-commercial start-up focused on developing AI-powered software to reduce or eliminate the administrative workload of physicians. We are building an automated billing software as a service (SaaS) that integrates with existing electronic medical records (EMRs) in Canada. The program uses a combination of machine learning (ML) and natural language processing (NLP) to analyze physician notes to predict the optimal diagnostic and billing codes for patient visits. The solution saves physicians time, increases their billing revenues, reduces errors, and provides a real-time view into their billing performance.