How Customer Service Automation Works: Benefits, Practices & Processes
Large and small businesses have had to re-look at their support automation processes and technology investments, given the promise of Generative-AI for support automation. Organizations now have easy access to generative AI technology - via DIY open-source projects or secure Enterprise Support Automation platforms like IrisAgent. But how does an organization get its support automation strategy and implementation right?
This comprehensive guide discusses the key steps to implement a successful support automation strategy. It outlines four key steps to use Generative-AI, i.e., effectively.
Knowing how and where to start
Identifying what to automate
Key metrics to track progress
Establishing a continuous automation approach for long-term success
The process map below summarizes the steps above,
The following sections detail the steps above, the best practices, and tips the IrisAgent team has learned implementing support automation for clients such as Logz.io, Darwinbox, Agorapulse, Teachmint, and many others. Check out the Teachmint case study here.
Step 1: ANALYZE - Knowing how and where to start
Generative AI is powered by huge machine learning models pre-trained on vast amounts of data, referred to as foundation models (FMs). A subset of FMs called large language models (LLMs) is trained on a large number of words across many natural-language tasks. In the case of support automation, LLMs are trained on historical support data as well as institutional knowledge. Thus the first step in support automation is integrating key systems that hold this data. Following are the key systems to typically integrate with (Tip: take note of the uncommon, often missed out engineering systems),
CRM Systems: Starting with the obvious, most support organizations over the years have made significant investments in CRM systems, popular ones being Salesforce, Zendesk, Microsoft Dynamics, Intercom, ServiceNow, and Freshworks.
Best practice: Have at least one year of data made available to the LLM model in use.
Knowledge Bases/FAQs: These are either part of CRM systems, help documentation or articles available to support teams.
Best practice: Have at least one year of frequently accessed articles available to the LLM model in use
Engineering bugs: This area is often missed, but vast amounts of institutional knowledge are buried inside engineering bug-tracking software or project management software such as JIRA and Confluence by Atlassian.
Best practice: Have at least two years of product release-related information. We recommend more historical data access here as engineering systems often hold data that is a precursor or predictor of issues that can be anticipated and potentially automated.
Service Alerts: In the case of enterprise SaaS, it is common to share service alerts internally and with customers via tools such as pagerDuty.
Best practice: Have at least three months of service alert information.
A key point to note: While integrating into CRM systems is obvious and low-hanging fruit, integrating Engineering bugs and Service Alerts should be an equal priority as the most accurate institutional product knowledge often lies with engineering and DevOps teams.
Step 2: AUTOMATE - Identifying what to automate
Once the first step of integrating the institutional knowledge is complete and your LLM model has access to rich historical data, the second step is to systematically analyze data, categorize it and uncover what to automate vs. topics best left to human-assisted support. The following steps outline a reliable approach,
Auto-tagging - Helps classify content for keywords associated with root-cause analysis, correlations to existing issues, customer intent and sentiment. Accuracy in tagging will form the foundation of the following three types of automation
Full automation, i.e., no human intervention: Via bots, Intelligent Virtual Agents for Voice and digital support interactions. The frequency and recency of auto-tagging of content guide what should be fully automated. The IrisAgent platform specializes in auto-tagging, quickly highlights the top tags, and provides recommendations on what to automate.
Best practice: Questions related to the top 5 tags in the last 6-12 months are great candidates to start full automation. In our experience, automating these delivers 40 to 50% ticket deflection. This is a good place to start. Anything beyond the top 5 does result in false positives and, unfortunately, the undesired consequence of a “bad bot experience” by customers. We do not recommend over-automating initially.
Agent Assist: This is where AI helps an agent in real-time to address questions with potential answers. Agent Assist is designed to aid the agent rather than take over full automation.
Best practice: Take the top 10 auto-tagged content from the past two years and deliver that as agent assist capability. IrisAgent provides this as a side-by-side widget inside CRM systems.
Workflows and alerts: This is the category where items not automated in the aforementioned steps are seamlessly handled via intelligent workflows that alert the right support representative of subject matter experts. IrisAgent platform allows for easy configuration of alerts and notifications to cross-functional teams.
Best practice: Implementing cross-functional workflows. Often, engineering teams are disconnected from the frontline. Alerting them promptly brings in SMEs sooner for a timely resolution. The IrisAgent platform was built with the premise that pulling in engineering knowledge early and often can result in effective support. IrisAgent platform provides robust OOB cross-functional workflows and alerts, ready to use up deployment.
Step 3: Key Metrics
Most traditional support operations measure ticket counts, response times, and SLA attainment and also further drill down into product categories, severity, and regional performance in the case of distributed teams. With support automation, a new set of metrics has emerged. While we deal with information overload, we have identified the following key metrics,
Ticket Deflection Rate - i.e., how many customer questions got addressed without a human interaction Measures the effectiveness of automation via bots.
Mean Time to Resolution (MTTR) - The time it takes to solve an issue. This helps understand the efficacy of agent assistance and workflows implemented.
Escalations - Helps understand issues that remain unaddressed or indicate a broader CSAT problem.
Agent Performance - Helps understand if the agents find the real-time assist technologies useful.
Engineering and Product Health - Ongoing incidents, Product status, and Historical incidents - Provides insights into overall product quality and effectiveness of engineering processes for development and quality assurance.
The above reports should be incorporated into weekly or monthly metrics tracking. The IrisAgent platform makes these available as part of out-of-the-box dashboards and reports.
Best Practice: Not too many companies track Engineering Health, which often is a leading indicator for issues that can be anticipated. We recommend adding this metric to your overall reporting. IrisAgent delivers this unique insight out of the box.
Step 4: Establishing continuous automation
Once an organization has implemented the above steps, it is important to monitor metrics, and changes to auto-tagging data, and accordingly adapt the automation mix.
Best practice: Establish a review every six months and make appropriate changes, i.e., automate new categories and move older categories to agent assist or to process workflows.
Ready to Experience Automated Customer Service?
In conclusion, the aforementioned 4-step approach can enable your organization to implement a successful support automation strategy using Generative-AI technologies. The IrisAgent platform has been built around this 4-step approach and has successfully helped organizations implement Support Automation. We would love to help you get your GenAI journey started!
Get started with support automation the right way!
Automated Customer Service: Frequently Asked Questions
What is automated customer service, and how does it differ from traditional customer service?
Automated customer service refers to using technology, such as chatbots, virtual assistants, and automated phone systems, to assist customers in resolving their inquiries, issues, or requests without direct human intervention. Unlike traditional customer service, which relies heavily on human agents for interactions, automated customer service operates 24/7, providing immediate responses, consistency in handling queries, and cost-efficiency. While traditional customer service offers a personal touch and nuanced problem-solving, automated systems excel in handling routine, repetitive tasks, freeing up human agents to focus on more complex and value-added interactions.
What are the key advantages of implementing automated customer service systems?
Implementing automated customer service systems offers several key advantages for businesses. Firstly, it enables 24/7 availability, ensuring customers can get assistance anytime, enhancing customer satisfaction. Additionally, it reduces operational costs by automating routine tasks and queries, allowing companies to allocate resources more efficiently. Automation also ensures consistency in responses and service quality. Moreover, it can simultaneously handle a large volume of requests, reducing customer wait times. Automated customer service systems enhance efficiency, accessibility, and cost-effectiveness, making them valuable to modern customer service strategies.
What security measures should be in place to protect customer data when using automated systems?
Robust security measures should include data encryption in transit and at rest to safeguard it from unauthorized access. Access controls and authentication protocols should be in place to restrict system access to authorized personnel only. Regular security audits and vulnerability assessments can help identify and rectify potential weaknesses. Data anonymization techniques can also minimize the risk associated with storing sensitive information. Continuous monitoring of system activity for any unusual patterns or breaches is essential, along with a well-defined incident response plan to address any security incidents promptly. Compliance with relevant data protection regulations, such as GDPR or HIPAA, is crucial to ensure customer data's legal and ethical handling. A multi-layered security approach is vital to protect customer data in automated systems.
How can businesses measure the success and ROI of their automated customer service initiatives?
Metrics such as customer satisfaction scores (CSAT), Net Promoter Score (NPS), and customer effort score (CES) can gauge customer experience improvements. Tracking the reduction in customer service response times and call volume can indicate efficiency gains. Additionally, businesses should analyze cost savings from reduced labor and operational expenses. Evaluating the resolution rate of customer inquiries and comparing it with human-assisted service can reveal the effectiveness of automation. Finally, monitoring key performance indicators (KPIs) like conversion rates, upsell/cross-sell success, and customer retention can provide insights into the broader impact on revenue generation.