AI-Suggested Webinar Topics

Q: How many topics should we shortlist per quarter?

Maintain a rolling backlog of 12–20 topics with evidence, and lock a monthly slate 4–6 weeks ahead based on updated scores and resources.

Q: Will this work with our webinar platform?

The approach integrates with ON24, Zoom, and GoToWebinar, plus analytics and MAP/CRM systems, to close the loop on registrations and pipeline impact.

Q: What if our audience is niche?

Train on your historical events and add SME qualitative inputs. Niche audiences often show stronger intent signals and community data that improve accuracy.

Executive Summary

AI suggests webinar topics by analyzing intent signals, historical performance, and market trends. Marketing teams replace 14–26 hours of manual research and alignment with 3–5 hours of AI-assisted ideation and validation—improving topic-to-audience fit and boosting registrations.

How Does AI Improve Webinar Topic Selection?

AI blends search intent, attendee behavior, content performance, and competitor momentum to surface topics with the highest predicted interest—then recommends title frames, abstracts, and promotional angles to lift registrations.

Within event & webinar strategy, AI agents continuously scan trending queries, persona pain points, and prior engagement to build a prioritized topic backlog with evidence, confidence scores, and projected performance.

What Changes with AI Topic Suggestion?

🔴 Manual Process (14–26 Hours)

Customer & attendee analysis
Product affinity & persona mapping
Timing & seasonality review
Opportunity scoring & shortlisting
Stakeholder interviews & approvals
Competitor & market scan
SEO & keyword research
Abstract and title drafting
Registration page copywriting
Campaign plan and channels selection
Pre-launch testing (emails/ads)
Launch & conversion tracking setup
Reporting & optimization

HEAVY COORDINATION & SLOW ITERATION

🟢 AI-Enhanced Process (3–5 Hours)

AI purchase/behavior & trend analysis
Automated topic scoring and shortlisting
Title/abstract generation with variants
Registration page optimization & preview
Performance tracking & iterative improvement

~80% TIME SAVINGS; HIGHER REGISTRATION YIELD

TPG standard practice: Start with persona KPIs and funnel stage targets, require evidence-backed scores for each topic, and route low-confidence recommendations for marketer review before promotion.

Key Metrics to Track

0.85–0.95

Topic Relevance Score

+20–40%

Audience Interest Prediction (vs. baseline)

±8–12%

Engagement Forecast Error

+15–30%

Registration Rate Uplift

Measurement Notes

Topic Relevance Score: Composite of intent match, persona fit, and historical performance similarity.
Audience Interest Prediction: Expected lift in clicks/opens/landing-page interest vs. last 3–5 webinars.
Engagement Forecast Error: Absolute variance between predicted and actual live+on-demand engagement.
Registration Rate Uplift: Change in registrants/landing-page visitor after title/abstract optimization.

What Signals Power AI Topic Suggestions?

Intent & Trend Data: Search queries, social chatter velocity, and content consumption spikes.
Historical Engagement: Past webinar attendance, watch time, Q&A density, and on-demand completion.
Persona Fit: Industry, role, maturity stage, and problem-language alignment.
Competitive Momentum: Over/under-served themes and whitespace opportunities.

Which AI Tools Enable Topic Suggestion?

ON24 AI

Recommends themes from attendee interest and engagement, with content personalization.

Zoom Webinar Intelligence

Analyzes registration & engagement signals to forecast topic performance.

GoToWebinar Analytics

Surfaces patterns in topic uptake and helps tune titles & abstracts.

These platforms connect to your marketing operations stack to continuously score topics and optimize registration paths.

Implementation Timeline

Phase	Duration	Key Activities	Deliverables
Assessment	Week 1–2	Audit personas, past webinar performance, and data sources	Topic scoring framework & data map
Integration	Week 3–4	Connect webinar platform, analytics, CRM/MAP; configure signals	Operational scoring pipeline
Calibration	Week 5–6	Back-test models, set thresholds, generate title/abstract templates	Calibrated model & content library
Pilot	Week 7–8	Run 2–3 AI-suggested webinars; validate forecasts vs. actuals	Pilot performance report & adjustments
Scale	Week 9–10	Automate topic backlog, approvals, and promotion workflows	Production-grade process
Optimize	Ongoing	A/B test titles, iterate scoring, expand to series formats	Continuous improvement plan

Frequently Asked Questions

How many topics should we shortlist per quarter?

Most teams maintain a rolling backlog of 12–20 topics with evidence, then lock a monthly slate 4–6 weeks ahead based on updated scores and resource availability.

Do we still need human review?

Yes. AI accelerates discovery and scoring, while marketers validate relevance, brand voice, and strategic fit—especially for executive or product-sensitive audiences.

Will this work with our webinar platform?

The approach integrates with major platforms (e.g., ON24, Zoom, GoToWebinar) plus analytics and MAP/CRM to close the loop on registrations and pipeline impact.

What if our audience is niche?

Niche audiences benefit from stronger intent signals and community data. Train the model on your historical events and use qualitative inputs from SMEs to boost accuracy.