Automatically Create Actionable Tasks from Customer Tickets and Forms

Is your team bogged down responding to piles of customer inquiry tickets and web form submissions? Do these vital customer interactions keep falling through the cracks while you handle hundred other tasks a day?

It‘s time to get out of this vicious cycle. With a little automation magic, you can start transforming all customer tickets and web form submissions directly into trackable tasks for your team – no manual effort required!

Why You Need Automation to Manage High Ticket/Form Volume

Call centers and customer support teams in enterprises handle 60-120 calls per day on average. Top-notch companies strive for first call resolution, but hits are often missed due to the speed needed.

According to 2022 customer service statistics from SuperOffice:

71% of customers	Expect companies to respond in 5 minutes or less on social media
50%+ drop	In customer satisfaction when answer times are longer than a minute

Simultaneously, other teams in your company are being bombarded by customer inquiries via web forms, support tickets, e-mails and more. No wonder this incoming flood of customer interactions keeps slipping through the cracks!

This is where intelligent automation comes to the rescue:

Tools like Zapier, Workato and Microsoft Power Automate can automatically:

• Convert form submissions/support tickets into trackable tasks in real-time
• Route tasks to appropriate teams with rules and conditional logic
• Pull in all details like names, contact info etc. from the form/ticket into the task

Now your teams get full visibility into open customer issues and can systematically work through resolution. Customers get quicker answers and you nip more cases of dissatisifaction in the bud. That‘s the beauty of automation!

Automation Efficiency Analytics

Once you implement task automation across different workflows, you gain access to a wealth of data in terms of:

• Customer response times
• Task resolution times
• Cases escalated
• Approvals completed
• And hundreds of other metrics…

Forward-thinking analytics leaders are now pulling this automation data into business intelligence tools like Tableau, Looker, Power BI etc. for deeper performance analysis.

Let‘s look at some examples of automation analytics in action across an enterprise:

Customer Support Optimization

The Director of Customer Support implemented Zendesk -> Asana task automation workflows last quarter. She‘s now pulling metrics into Tableau every week to check for optimization opportunities.

Some analyses include:

• Response Time by Channel: Comparing avg. response times across email, web forms, social media
• CSAT by Issue Type: Checking customer satisfaction ratings for technical issues, billing questions, general inquiries etc.
• Resolution Velocity: Analyzing days to close tickets by priority level

These insights help rapidly identify areas needing additional agents, updated workflows or AI tools to improve operational efficiency.

Predictive Analytics

The Head of Operations has the ambitious goal to make staffing completely dynamic – seamlessly adjusting call center capacity to manage inquiry volumes.

Rather than relying on simple seasonality models, he worked with data scientists to build a sophisticated predictive model pulling in dozens of metrics including:

• Web traffic data
• Ad campaign spend
• New customer registrations
• Search keyword rankings
• And more…

Feeding this model into their Tableau analytics suite generates rolling 4-week forecasts of support ticket volumes. Their automation platform automatically adjusts email reminders, escalations and agent assignments based on these variations in real-time.

Early results show a 5% uplift in customer satisfaction along with over 8% savings from demand-based staffing. The ops team cheers their weekly automation reports showing forecasts versus actuals!

Emerging AI Capabilities

AI and machine learning offer incredible opportunities to amplify the power of your automation workflows:

Smart Ticket Classification

Platforms like MonkeyLearn allow automatically classifying inbound support tickets via text analysis:

• Technical issue
• Account inquiry
• Payment help
• General question

This saves agents ton of time manually reviewing while allowing precise assignment to the right teams.

Predictive Churn Models

Analyze customer language, past interactions, usage data etc. to predict likelihood of attrition. Automated processes can now proactively engage customers showing churn signs via personalized discounts, service upgrades etc.

Conversational AI

Chatbots and virtual assistants can resolve common repetitive inquiries completely automatically via natural language conversations. 30-50% of all tickets can potentially be managed end-to-end without human involvement.

The possibilities are endless once you combine the parallel processing power of automation with the intelligence of predictive analytics and machine learning.

Comparing Automation Approaches

	Rules-Based Automation	AI Powered Automation
Key Technologies	Tools like Zapier, IFTTT	Machine learning, NLP
Setup Effort	Low code, Very quick	Detailed modeling, Slower
Accuracy	Limited by rigid rules	Continuously evolving
Data Privacy	Enterprise control	Potential external usage

Rules-based automation offers a simple starting point for teams without expertise in managing AI. As your analytics maturity grows you can consider selective insertion of machine learning models.

Hybrid approaches maintain control via rules while tapping AI‘s potential for handling language, deviations, unpredictability at scale.

Driving Enterprise-Wide Adoption

However, maximizing the return-on-investment from process automation requires getting both customer-facing and backend teams onboard:

Change Management Strategies

• Highlight benefits via metrics so teams buy into goals
• Incentivize usage through bonuses or rewards programs
• Solicit feedback early-on to address concerns
• Celebrate automation successes, eg. customer testimonials

This increases internal adoption critical for enterprises handling thousands of tickets via hundreds of agents daily.

Ongoing Training

Don‘t just train during initial rollout! Refresh teams on updated capabilities through:

• Regular newsletters showcasing new features
• Contests to suggest creative automation recipes
• Quarterly power user recognition

This drives engagement across the layers – ensuring you maximize value from existing workflows while unlocking new innovations.

Governance Guardrails

Balance empowerment with necessary guardrails via:

• Cross-team steering committee for automation initiatives
• Burst analysis to detect usage spikes
• Random audits for policy adherence
• Annual risk assessments

These governance mechanisms provide the structure for large, distributed teams to operate reliably at scale.