Top 7 RPA Use Cases in Customer Service in 2024

In 2021, it was reported that ~20% of organizations have adopted RPA for automating different back-office tasks. Since RPA bots can tackle rule-based repetitive tasks, they can significantly reduce the workload of customer service teams. This is done by fetching answers for customers from business databases, reporting customer inquiries and complaints, updating customer information, and routing calls to human agents. However, despite the benefits, RPA bots still face challenges as users may prefer live agents to handle their inquiries.

In this article, we explore RPA benefits, challenges, and use cases in customer service.

How does RPA improve customer service?

RPA bots can replicate human interactions with GUI elements and complete repetitive rule-based tasks such as answering FAQs, downloading customer emails, or fetching answers from the database (e.g. prices, shipments, troubleshooting). Therefore, RPA improves customer service by:

• Collecting data for analytics
• Ensuring consistent and fast responses
• Limiting data errors (non-updated information, file transfer duplicates, unverified customer data)
• Providing 24/7 service
• Reducing customer service team workload

What are some RPA use cases in customer service?

RPA can be used to automate numerous tasks in customer service, such as:

1. Aid customer representative agents

63% of customers expect customer service reps to know their unique needs and expectations, such as know who they are, and what they have purchased. To tackle this issue, an RPA bot can fetch the customer’s data, such as demographics, purchases, previous complaints or tickets and provide it beforehand to the customer service rep in order to help them anticipate customers’ inquiries, and navigate the issue quickly.

2. Create customer account

Each customer has their own account in a business CRM database which includes their name, customer ID, contact information, credit card information and purchase history. Customers typically create their account by speaking to a customer rep or a chatbot in a recorded conversation. The bot can automatically extract the relevant information from the recorded conversation and fill out the forms to create the customer account, validate the payment data against bank information, and notify the customers of account creation once its finalized.

3. Manage customer refunds

RPA bots can extract customer refund inquiries from emails, texts, or ticketing systems, and initiate refund processes without human intervention. The bot can also send a notification to the user via email or text to inform them of the refund completion.

4. Resolve rule-based issues

Issue information can be collected and input into the ticket system either via RPA bots, or by AI-based customer service chatbots. RPA bots can access the ticket database, and automatically resolve simple customer issues such as:

• Renewing customer password or login information
• Modifying orders (change delivery address, request return code)
• Modifying payment information (credit card number, pay-at-the-door)

5. Update CRM data

RPA bots can pull data from business databases (e.g. customers’ past purchases, interactions with customer service employees, cold calls and emails, documents and reports) and update CRM data (e.g. contact history, lead scoring, order history) with information from new emails, texts, online surveys, or filed reports.

Based on the priority level assigned, a workflow for issue resolution is created, and the customer is informed of the refund decision.

6. Report customer complaints

RPA bots can leverage NLP and OCR to understand customer complaints in emails or texts, extract complaint data (e.g. service downtime, package delay, wrong delivery) and input it into spreadsheets or text documents, and generate reports which can be:

• Sent to relevant customer support employees
• Used for detecting issue patterns
• Logged for compliance and audit

On the other hand, AI-enabled RPA can solve more sophisticated customer issues such as:

• Leverage NLP to detect customer intent in an email or text, and route them to the designated customer service rep or IT employee.
• Rely on ML classification algorithms to understand the importance of an issue, and prioritize technical support or customer reps’ calendar accordingly.

7. Automate email responses

RPA bots can pull data from different databases and generate emails in response to customer requests. For instance, bots can fetch data from:

• Logistics database to generate an email about shipment tracking or delay information.
• FAQ database to answer emails about shop or warehouse locations and opening hours.
• IT documentations to answer product troubleshooting inquiries.
• Marketing database to provide information about coupons and promotions.
• Finance database to generate invoices to customers and send them via emails.

RPA bots can also generate ticket closure emails and send them to customers who have filed a complaint or opened an issue ticket.

What are the challenges that face RPA adoption in customer service?

Many users still prefer speaking to a live agent and having one-to-one conversations to solve their problems instead of relying on a bot. This can be due to people’s belief that a human agent can better understand the problem.

Nonetheless, with more adoption of automation in customer service, users are starting to be more accepting of chatbots or RPA bots as they state that “they don’t care if they’re helped by an AI tool or a human, as long as their question gets answered.”

For more on RPA

To understand RPA technology in more detail, feel free to read our in-depth whitepaper on the topic:

To explore RPA benefits and use cases in different industries, feel free to read:

• Our in-depth article where we compiled 15 benefits from reputable resources.
• Our data-driven articles on RPA use cases in:
  • Healthcare
  • Finance and accounting
  • Cybersecurity

And if you believe your business will benefit from an RPA solution, feel free to check our data-driven list of RPA vendors, and other automation solutions.

And let us help you choose the right tool for your business:

This article was drafted by former AIMultiple industry analyst Alamira Jouman Hajjar.