ThrillOut Accelerator
The program include a series of webinars, lectures and mentoring on how to use the ThrillOut platform and Lean business planner for musicians.
Course Outline
- Who is your fan, based on parts of the MIT's Entrepreneurship roadmap for startups - Who is your customer. (Week one)
- Business model for musicians "Direct-2-fan" (Week one)
- Inbound Marketing for musicians
- Personas / Fan - your buying personas, who is your optimal fan (Week 2 exercise)
- What is Inbound marketing for musicians. (Week 3)
- Attract strangers and visitors to website and social media profiles
- Convert visitors to leads (potential fans)
- Close fans to loyal fans
- Entice your fans to become your promoters
- Activities: (Week 4)
- Blog with content, Video content,news, social media, keywords
- Forms, Call-to-Action and landing pages - traffic analysis
- Email and workflows, analysis
- Events, motivate fans to become your partner and promoters of your campaigns, gigs and releases.
- Lean Business planner for musicians (week 5)
- Create a lean business plan for musicians / artists / bands (Week 6)
- Build, measure, learn (test new material among fans)
- ThrillOut Academy - Use of the ThrillOut platform (Week 7)
- Collaboration room
- Projects
- Build teams
- Build your ecosystem (fanbase)
- Direct-2-Fan
- Live streaming ThrillOut Live
- Catch fan data
- Intellectual Property Rights (IPR) - your most important assets. (Lectures with legal experts, Week 8)
- Distribution of music, streaming, Royalties (Week 9)
- Lecture with experts (Phonofile, Tono)
- New possibilites with Kobolt, AWAL, UnitedMasters
- Funding of projects (Week 10)
- Fan funding (Crowdfunding) of projects
- Equity funding, let fans hold shares in your band- / artist company
- Lectures (invited mentors) Week 11
- Music management
- Music business strategies (music schools)
- Success stories bands
- Exam (Week 12)
- Mentor program for hire (selection)
Future topics:
- General Data Protection Regulations (GDPR), Big Data, Blockchain and smart contracts, AI, Machine learning, and more...
- Motivation, Failures.