Write webinar reminder messages that raise show-up rates
A reminder sequence by email and calendar that gets more registrants to actually attend live.
The prompt
Write a reminder sequence that gets more of my webinar registrants to show up live. This is step 3 of a webinar pack; it works on its own, but paste your registration page or title and promise from earlier steps if you have them.
Webinar title, date, time, and join link: [DETAILS]
Takeaways and anything attendees get only live: [e.g. live Q&A, a template, a live teardown]
Registrants so far and when most registered: [NUMBERS - or "unknown"]
Channels I can use: [email, calendar invite, SMS (with consent), LinkedIn event]
Typical show-up rate for my past webinars: [% or "first one"]
Return:
1. Confirmation email (sent instantly): what they signed up for, add-to-calendar link, one thing to think about before the session, and a question they can reply with to shape the content.
2. Calendar invite text: title, join link, and one-line agenda in the description.
3. Reminder 1 (3-7 days before): a sneak peek of one insight or the live-only element.
4. Reminder 2 (day before): agenda and what to prepare.
5. Reminder 3 (1 hour before): short, just the link and the hook.
6. "We're live" message (at start time): one line with the link.
7. Optional SMS versions under 160 characters - only for people who opted in to texts.
Rules: Each reminder gives a new reason to attend; don't repeat the same text. Keep the join link in every message. Suggest 2 things that improve show-up beyond messages (e.g. a live-only bonus, asking registrants to submit questions).
Highlighted [brackets] are blanks to replace with your own details.
Fill in the blanks and use this promptMore Marketing & Brand prompts
What to fill in
- DETAILS
- e.g. live Q&A, a template, a live teardown
- NUMBERS - or "unknown"
- email, calendar invite, SMS (with consent), LinkedIn event
How this prompt is built
This prompt follows the Few-Shot structure (Few-shot prompting). A few-shot prompt includes one or more worked examples of the input and the output you want, so the model can copy the pattern instead of guessing it.
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