Customer service remains a crucial and highly visible touchpoint between companies and clients. Yet many support operations struggle to meet rising ticket volumes while managing complex systems and databases — causing slow, inefficient resolution.
Robotic process automation (RPA) offers a solution. By deploying intelligent software bots to handle repetitive back-office tasks, companies can lower costs, boost efficiency, and enhance service quality.
This comprehensive guide examines RPA’s immense potential based on quantitative research and real-world case studies. You’ll learn:
- Key customer service benchmarks and how RPA delivers impact
- Common support scenarios ideal for automation
- Steps for building and launching customer service bots
- Emerging innovations like AI augmenting future capabilities
While well-suited for rules-based workflows, RPA also faces adoption barriers. We’ll cover considerations like change management, legacy IT constraints, and customer perceptions that need addressing for successful ongoing rollout.
Why Customer Service Needs Automation
Let‘s define essential contact center metrics and the pressing problems RPA solves:
CSAT – Customer Satisfaction: A score based on user ratings of support interactions, with top brands targeting 90%+.
FCR – First Call Resolution: Percent of inquiries fully addressed in the initial engagement, with world-class centers seeing ~97%+.
AHT – Average Handle Time: The typical minutes needed resolving an inquiry. 15 minutes is average.
Two trends currently threaten these core metrics:
1. Case volume growing 15-30% yearly
Customers expect seamless service across more channels than ever, driving surging ticket numbers.
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2. Over 30% of support work is repetitive administrative tasks
Data entry, report generation, information lookup and other basic work consumes staff hours without leveraging specialized knowledge or enhancing customer relationships.
RPA delivers a solution purpose-built for rules-based back-office work by deploying software bots handling thousands of cases without rest or downtime. The impact is transformative:
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Next let’s examine exactly how “virtual workers” deliver such dramatic improvements by looking at common customer service use cases.
High-Impact RPA Use Cases
RPA bots excel at repetitive, high volume tasks with clear business rules that rely primarily on system rather than human interactions. These traits encompass a significant portion of customer service workflows:
1. Account Data Updates
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Batch updating thousands of customer contact details, preferences or consent status across CRM and other systems
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Manual updates prone to human error from data entry, forgotten changes or delays
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RPA integrates systems via APIs to input updates instantly without mistakes
2. Order/Refund Processing
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Facing customer frustration if error-prone manual rework causes delays
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Bots can rapidly handle order changes, replacements, refunds by connecting related systems
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One bot manages as many cases as over 20 agents, with 100% accuracy
3. FAQ Response
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Over 50% of customer inquiries concern basic questions like store locations, shipping times or prices
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RPA bots find answers in knowledge base or documents matching query context
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24/7 availability and instant response for high user satisfaction
4. Report Generation
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Data analysts waste days compiling standard reports for cross-department sharing
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Bot triggers automated distribution on fixed schedules or by data threshold hits
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Analysis time saved allows more value-add insight discovery
These examples showcase simple opportunities to augment staff strength. But to truly transform operations, shared services automation delivers even greater rewards:
Shared Services Use Cases
While RPA often starts within individual departments, the biggest benefits stem from enterprise-wide “automation centers of excellence”. Centralizing bots serving cross-functional needs better amortizes upfront investment and IT support while ensuring consistency, access controls and governance.
High impact areas include:
Payment Processing
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Fragmented billing and payment systems often force manual workarounds causing delays or errors
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Bots seamlessly integrate APIs across finance tools for accurate, on-time customer and partner payments
Master Data Management
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Disjointed systems like CRM, ERP and analytics contain conflicting customer data
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Automated consolidation improves decisions and forecasting by establishing “one version of the truth”
Reporting and Dashboards
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Disorganized legacy reporting still relies on specialist staff pulling data separately into spreadsheets
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Slick dashboards generated automatically by bots give leadership real-time business insights
Now that we‘ve made the case for RPA and identified top use cases, what steps are involved in building and launching customer service bots?
Deploying Customer Service Bots in 6 Steps
While exact techniques vary across RPA platforms, high-level phases are common when adding bots to your support workflow:
1. Define Business Requirements
- Which inquiries or processes cause the biggest waits, errors or staffing challenges?
- What available systems and data sources need integrating?
2. Develop Prototypes
- Model one highly repetitive task matching key criteria like high volume and clear logic
- Involve customer service agents for process knowledge and user testing
- Refine until model handles all scenarios accurately without human intervention
3. Create Production Bots
- Configure control room server and production licenses based on needed capacity
- Set schedules, alerts, dashboard reporting and monitoring for go-live
4. Integrate Analytics
- Connect bots to BI tools to track KPIs and optimize performance
- Create customer service scorecards and dashboards for leadership
- Identify emerging inquiry trends needing new script development or agent training
5. Apply Intelligence
- Boost language understanding with NLP for parsing unstructured data
- Integrate machine learning recommendations to improve routing and solution finding
6. Manage Change
- Reassign agents from repetitive tasks to higher judgement inquiries
- Offer retraining programs to build support analytics and customer empathy skills
- Sustain momentum via continuous process improvements
With bots handling high-volume repetitive tasks, agents focus on complex issues and relationship building. Now let‘s examine remaining barriers to enterprise adoption.
Overcoming RPA Objections
Despite proven benefits, some skepticism and objections around RPA in customer service remain:
Concern: "RPA seems risky with our complex legacy systems."
- Reality: RPA is simple to test and refine on existing tools via surface-level interactions without changing underlying code.
Concern: "IT already has a full schedule just keeping things running."
- Reality: Citizen developer RPA tools don‘t require specialized coding or drain IT resource. Some vendors even provide full management options.
Concern: "Agents will hate losing interesting work."
- Reality: Staff burn out on mundane tasks not leveraging their skills. Bots handle grunt work so talent focuses on higher judgement tasks and real customer engagement.
Concern: “Customers prefer human interactions.”
- Reality: Users prioritize correct and speedy resolution over talking to agents handling routine requests. And emerging tools like conversational AI provide ideal hand offs when emotion-driven connections matter.
The above can be summarized as:
Top Barriers to RPA Adoption & Mitigations
| Barrier | Mitigation |
|---|---|
| Legacy System Constraints | Bot testing causes no disruption |
| IT Resourcing Drain | Citizen development needs little/no IT support |
| Staff Resentment | Transition staff to value-add judgment tasks |
| Customer Discomfort | Prioritize correct and fast answers then leverage AI for complex engagements |
While overcoming change management hurdles takes thoughtful planning and communication, none present true technical constraints.
Now let‘s examine emerging capabilities poised to make already versatile RPA bots even more powerful.
The Future: Intelligent Customer Service Automation
RPA provides tremendous efficiency gains for rules-based service tasks. But integrating smarter technologies opens capabilities limited only by imagination:
Conversational AI: Chatbots and live chat with seamless, omnichannel hand offs to bots and agents
Process Mining: Models ideal customer journeys by analyzing real-world data then optimizes workflows
Customer Analytics: Sentiment analysis, predictive modeling and journey mapping provide complete personal understanding
Cognitive Expert Finding: Identifies specialists across the organization based on inquiry history and skill profiles
Voice Biometrics: Voice ID for account access, verification and fraud prevention while personalizing responses
Together these create a vision where always-available, instantly responsive and fully personalized bots become trusted advisors optimizing entire customer lifecycles. RPA provides the backbone for this digital transformation.
Are Your Customers Getting the Service They Deserve?
Doing more with less remains the mantra across customer service. Client expectations continue rising amidst flat budgets and staffing constraints.
RPA provides a path where augmented efficiency and relentless availability allow organizations to guarantee responsive, accurate and satisfying support.
The technology forms a foundation driving higher CSAT, lower costs and increased competitiveness — especially when integrated with AI and analytics.
Leaders recognize that instead of incrementally tweaking outdated support models, RPA enables rethinking and reinventing customer service for the modern digital age.
Are you ready to transform?