Marketing Intern
Project scope
Categories
Market research Competitive analysis Product or service launch Sales strategySkills
competitive analysis marketing collateral marketing value propositions sales research billing microsoft powerpointReporting to the CEO, you will design and develop marketing collateral to support the launch of Caddie Health’s billing solution. Specifically, you will create collateral to support the future sales team in the form of “battle cards” - details on competitors and how we will position and win business in competitive scenarios.
During the course of this project, you will complete the following:
- Align on the set of ~5 competitors to focus on and the key details needed for competitive analysis (e.g., feature sets, services, pricing, messaging / positioning, etc.)
- Conduct research on each competitor, online and by phone, to collect the data on each competitor
- Work with the Caddie Health team to brainstorm responses to competitor value propositions that will be compelling to customer segments
- Synthesize findings and recommended positioning and messaging into 1-slide PowerPoint summaries
- Share the outputs with the Caddie Health team to collect feedback and refine the materials, and identify future collateral that should be developed for Marketing and Sales success
You will be required to regularly meet with the CEO to provide updates on project deliverables and iterate based on feedback.
The Marketing Intern will work directly with me on all projects, collaborating and iterating on deliverables on a daily basis. Given we’re a small team, we’ll have the opportunity to work together to shape projects based on your specific interests and skillset.
About the company
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.