What's the most cost-effective way to reduce repetitive support tickets using a community support platform?

The most cost-effective way is usually to turn your community into a self-service deflection engine before investing heavily in custom automation.

Best low-cost approach

1. Identify the top repetitive ticket topics

   • Pull the last 30–90 days of support data.
   • Rank by volume and time spent.
   • Focus on the 5–10 most common issues first.

2. Create high-visibility answers in the community

   • Write short, searchable posts/FAQs for those issues.
   • Use clear titles that match how users phrase the problem.
   • Pin or feature the most common solutions.

3. Make content easy to find

   • Use strong search, tags, categories, and “related posts.”
   • Add a “Start here” or “Common issues” section.
   • Link from ticket auto-replies to the relevant community answers.

4. Deflect tickets with guided self-service

   • Before a user submits a ticket, show suggested community articles/posts.
   • Add simple decision trees or triage prompts.
   • Encourage users to mark answers as helpful.

5. Leverage power users and moderators

   • Give trusted community members recognition and moderation tools.
   • Fast peer responses can reduce support workload dramatically.
   • This is usually cheaper than expanding the support team.

6. Measure and refine

   • Track ticket deflection rate, community views, and repeat ticket reduction.
   • Update content based on unresolved questions.
   • Remove outdated answers quickly.

Why this is most cost-effective

• Uses existing community platform features first.
• Reduces ticket volume without adding headcount.
• Improves over time as content and search get better.
• Scales better than relying only on agents.

If you want the single highest-ROI tactic

Start by building a searchable FAQ/knowledge base from your top repetitive tickets and surface it before users can open a support request.

If you want, I can also give you:

• a 90-day rollout plan, or
• a simple ROI model for estimating ticket deflection savings.