7 Proven Strategies for SaaS Teams to Reduce Support Tickets by 70%

Support tickets increase quickly as your customer base grows, especially during product launches, new feature releases, and onboarding spikes. When many users ask the same questions, support teams get overloaded.

Studies show that self-service and automation can reduce up to 70% of calls, chats, and email requests. By giving customers clear ways to find answers on their own, companies can reduce ticket volume and allow support teams to focus on issues that truly need human help. This shifts support from a cost burden into a strategic advantage.

Each support ticket has hidden costs beyond agent time. High ticket volume causes slower response times, burned-out teams, and frustrated customers waiting for answers. When ticket volume is not controlled, customer satisfaction drops and churn increases.

Companies that reduce support tickets report 30–55% lower support costs, better team morale because agents spend less time on repetitive questions, and higher customer satisfaction because self-service answers are faster than waiting for email replies.

A knowledge base is one of the most reliable ways to reduce support tickets because it lets customers find answers on their own without waiting for support. Research shows that companies using self-service knowledge bases see around a 23% reduction in support tickets after implementation

The first step is reviewing existing support tickets to find repeated questions and common problems. These issues should be documented first. Articles must be written in simple, clear language at a 7th-grade reading level, avoiding technical jargon.

Step-by-step instructions, screenshots, and short videos help users understand faster and reduce confusion. Adding AI-powered search allows users to type questions in plain language instead of guessing exact keywords, making it easier to find the right answer quickly.

BunnyDesk AI reduces maintenance work by automatically updating documentation using code changes, support tickets, and product updates, preventing knowledge bases from becoming outdated due to manual effort.

Even with strong documentation, customers still raise basic questions such as account access, setup steps, or simple troubleshooting. AI chatbots reduce this load by answering common questions instantly and directing users to the right help articles when needed.

Well-configured chatbots handle repetitive requests, share relevant documentation links, and escalate issues only when human support is required. This improves response speed while keeping complex cases with trained agents.

Customers will also be comfortable using chatbots for simple issues and often prefer instant automated answers over waiting in a support queue. As AI-driven support models mature, analysts note that AI-first approaches can significantly increase ticket deflection and improve overall support efficiency

Waiting for customers to run into problems and submit support tickets creates frustration before help even begins. Proactive support reduces tickets by addressing issues early and communicating clearly before users feel blocked.

When bugs, outages, or performance issues occur, customers should be informed right away through in-app messages, status pages, email, or social channels. Clear communication about what is happening, who is affected, what steps are being taken, and when a fix is expected helps prevent confusion and repeated questions.

The same approach applies to planned changes like feature removals or interface updates. Informing users in advance and explaining what is changing and why builds trust and prevents sudden spikes in support tickets caused by surprise or misunderstanding.

Many support tickets are created not because something is broken, but because users are unsure how to use a feature or navigate the product. This problem is most common during onboarding and after new features are released. Contextual in-app guidance reduces these tickets by showing help exactly when and where users need it.

Tooltips, walkthroughs, and in-product messages can explain features as users interact with them. New users benefit from guided tours that walk them through key actions step by step, while existing users need clear explanations when features change.

When help is available inside the product, users can solve issues immediately without leaving the interface or contacting support, which significantly lowers tickets related to confusion and usability.

New users generate more support tickets because they are unfamiliar with the product. When users sign in for the first time without clear guidance, they often submit basic “how do I start” or setup-related tickets instead of exploring the product on their own.

Personalized onboarding reduces these early tickets by guiding users based on their role, goals, or use case rather than showing the same content to everyone.

Using simple welcome questions allows teams to deliver role-specific walkthroughs, highlight only relevant features, and surface the right help content at the right time. This helps users reach value faster and reduces confusion during the first sessions.

Not all support tickets require the same expertise, but when tickets are sent to the wrong agents, resolution times increase and customers wait longer for answers. Manual routing often leads to repeated transfers, duplicated work, and slower response times. Skill-based routing fixes this by automatically assigning tickets based on issue type, priority, and agent experience, ensuring the right agent handles the issue from the start.

Automation further reduces ticket load by merging multiple tickets from the same customer into a single conversation and categorizing issues without human input. Research says that support teams using AI for ticket classification and routing save up to 45 minutes per agent per day, reducing backlog and speeding up resolution without increasing headcount.

Ticket deflection rate measures how many customer issues are resolved through self-service instead of reaching a support agent. It is calculated as:

Ticket deflection rate = (self-service interactions ÷ total support interactions) × 100%.

This metric helps teams understand whether their help center, documentation, and automated tools are actually reducing ticket volume.

High-performing support teams typically aim for 20–40% ticket deflection, while organizations with mature self-service and automation programs can reach higher levels. Teams that review failed searches, high-traffic articles, and repeat ticket topics are better able to close content gaps and prevent tickets before they are created.

Reducing support tickets by 70% is about enabling faster self-service, not reducing support quality. Most tickets are caused by unclear documentation, repeated questions, and a lack of timely communication during product changes. These can be addressed by maintaining accurate documentation, using AI chatbots for common issues, and proactively informing users about updates, outages, and new features.

A scalable support system requires consistent self-service content, in-app guidance, structured onboarding, efficient ticket routing, and regular review of support data to identify gaps. When documentation stays current, users resolve issues without creating tickets. BunnyDesk AI supports by keeping documentation aligned with product changes automatically, reducing the manual work that often causes support optimization efforts to fail.