Billing questions. Account access problems. Product setup. Troubleshooting requests. Many of these issues could potentially be resolved through better self-service resources or automation.
Ballet’s ready-to-use Contact Driver & Help Center Audit template helps you identify the issues driving support demand, uncover gaps in your help center, and find opportunities to reduce repetitive work.
Upload a support ticket export or provide your help-center URL. Ballet analyzes the available information and delivers a prioritized shortlist of improvements, including automation opportunities and estimated support hours saved.
The problem: High ticket volume makes it harder to see what’s driving support demand.
Customer support teams handle a steady stream of questions, requests, and issues.
Some require specialized knowledge or human judgment. Others are repetitive and could be resolved through self-service or automation.
But identifying which issues create the most work isn’t always straightforward.
Ticket categories may be inconsistent. Similar questions can appear under different labels. Help-center articles might exist but fail to address what customers actually need.
As ticket volume grows, manually reviewing conversations and identifying patterns becomes increasingly difficult.
Without a clear understanding of the most common contact drivers, teams may invest time in improvements that don’t address their biggest sources of support demand.
The opportunity is to use existing support data to identify recurring issues, improve self-service, and prioritize automation where it can make the greatest difference.
The play: Identify your top contact drivers and prioritize what to improve.
Start with Ballet’s prebuilt Contact Driver & Help Center Audit template.
Upload a support ticket export to understand why customers contact your team, provide a help-center URL to evaluate existing self-service content, or use both for a more comprehensive analysis.
Depending on the information available, Ballet can:
- Classify contact drivers: Group tickets by the underlying reason customers reached out.
- Identify recurring issues: Reveal which questions and problems generate the most support requests.
- Audit help-center coverage: Highlight missing, incomplete, or potentially outdated guidance.
- Find automation opportunities: Identify repetitive requests with clear resolution steps.
- Estimate time savings: Calculate potential support hours saved using ticket volume and handling-time data or assumptions.
- Prioritize improvements: Recommend which issues to address first based on impact and feasibility.
The result is an actionable shortlist of improvements, with each recommended automation providing a starting point for a relevant playbook in Ballet.
Customize the template with this prompt
Use the following instructions to tailor the template to your support data, help-center content, and operational priorities.
Use the Contact Driver & Help Center Audit template to analyze support demand, identify content gaps, and prioritize automation opportunities.
Starting inputs
Analyze the available inputs:
- Support ticket export: [uploaded CSV or spreadsheet]
- Help-center URL: [URL]
Use whichever input is provided. If both are available, compare recurring customer issues with existing help-center coverage.
Contact-driver analysis
If ticket data is provided, review available ticket subjects, descriptions, conversation content, categories, and resolution details.
Classify tickets by the underlying reason customers contacted support.
Apply consistent classification criteria across the dataset, even when existing ticket labels differ.
Identify:
- Top contact drivers
- Ticket volume by driver
- Recurring customer questions
- Issues suitable for self-service
- Issues requiring human assistance
Avoid double-counting tickets in overall volume calculations.
Help-center audit
If a help-center URL is provided, review accessible articles and support documentation.
Identify:
- Topics covered by existing content
- Missing or incomplete guidance
- Potentially outdated information
- Articles that could be clearer or easier to navigate
- Opportunities to improve self-service
When ticket data is available, compare common contact drivers with relevant help-center articles.
Distinguish between missing content, inadequate guidance, and issues that require changes beyond documentation.
Automation opportunity assessment
Evaluate recurring issues using:
- Ticket volume, when available
- Frequency and repetitiveness
- Resolution complexity
- Availability of reliable information
- Defined resolution rules
- Authentication, approval, or escalation requirements
Identify opportunities for automated responses, guided self-service, or workflow automation.
Flag issues that should remain with human support.
Time-savings estimates
For each shortlisted opportunity with sufficient data, estimate potential support hours saved.
Use actual handling-time information when available.
Otherwise, apply [average handling time in minutes] and [estimated automation rate] as configurable assumptions.
Calculate:
Estimated hours saved = Ticket volume × Estimated automation rate × Average handling time ÷ 60
Clearly label assumptions and the reporting period.
Do not present estimated savings as guaranteed outcomes.
If ticket volume or handling-time information is unavailable, identify the missing inputs rather than inventing a savings figure.
Prioritized recommendations
Produce a shortlist that includes:
- Contact driver or content gap
- Supporting ticket or content evidence
- Recommended improvement
- Automation suitability
- Estimated hours saved, where calculable
- Priority level
Recommend whether each opportunity should become a help-center improvement, a self-service experience, an automation workflow, or a human-led process improvement.
Organize the results by potential impact and feasibility.
Highlight the strongest candidates for activation as automation playbooks in Ballet.
How the workflow runs
01. Select the template and provide your support data
Open the Contact Driver & Help Center Audit template in Ballet.
Upload a ticket export, enter your help-center URL, or provide both.
A ticket export helps Ballet identify the issues customers contact support about. A help-center URL allows the workflow to review your existing documentation.
Using both inputs helps reveal where your help-center content may not adequately address common customer questions.
02. Classify your top contact drivers
When ticket data is available, Ballet analyzes the information to identify why customers reach out.
Rather than relying solely on existing ticket tags, the workflow can use ticket descriptions and conversation content to group similar issues under consistent categories.
For example, tickets labeled “login problem,” “can’t sign in,” and “account locked” may all belong to a broader account-access category.
This gives your team a clearer picture of which issues occur most frequently.
03. Identify help-center content gaps
Ballet reviews the accessible content from your help-center URL to identify topics that may need attention.
When ticket data is also available, the workflow can compare frequent customer questions with existing articles.
For example, a high volume of setup-related tickets may indicate that instructions are missing, unclear, or insufficient for a particular scenario.
The audit helps your team determine whether an issue calls for new content, better documentation, or changes to the support process.
04. Find opportunities for automation
Ballet evaluates recurring support issues to identify which ones are suitable for automation.
Straightforward requests with clear resolution steps are often stronger candidates than situations requiring complex judgment or sensitive account decisions.
The workflow considers the information needed to resolve each issue, the rules involved, and whether human involvement is necessary.
This helps your team prioritize automation opportunities that are practical to implement and likely to reduce repetitive work.
05. Estimate potential support hours saved
For opportunities with sufficient data, Ballet estimates how much support time could be saved through automation or self-service.
The calculation uses ticket volume, average handling time, and the percentage of requests expected to be automated.
For example, suppose your team receives 500 tickets per month for a recurring issue. Each ticket takes an average of six minutes to handle, and you estimate that 60% could be automated.
500 tickets × 60% × 6 minutes ÷ 60 = 30 hours
That’s an estimated 30 support hours saved per month.
Actual savings depend on customer adoption, successful resolution rates, and how the automation performs.
06. Prioritize improvements and activate the next playbook
Ballet organizes the findings into a shortlist of recommended actions.
Your team can identify which contact drivers deserve attention, where help-center improvements could help, and which requests offer the strongest automation potential.
For a shortlisted opportunity, you can select and configure a relevant automation playbook in Ballet.
This gives your team a clear path from identifying repetitive support issues to implementing targeted improvements.
What your team gets
For Customer Operations
A clearer understanding of support demand and a prioritized plan for improving operational efficiency.
For Support Leadership
Visibility into recurring issues, potential time savings, and opportunities to reduce repetitive agent work.
For Knowledge Management
Insight into help-center content that may be missing, incomplete, or difficult for customers to use.
For Customer Experience Teams
Opportunities to improve self-service and make common issues easier to resolve.
Customize the template for your support operation.
Start with the information you already have.
If your priority is understanding why customers contact support, upload a ticket export. If you’re evaluating your self-service experience, provide your help-center URL.
For a more comprehensive audit, use both.
You can adjust contact-driver categories, prioritization criteria, and time-savings assumptions to reflect your team’s processes.
Once the analysis is complete, review the recommendations and choose the improvements that offer the greatest potential impact.
