Getting Started with Customer Service Automation
In the last few years, interest in “Automation” and “AI” has grown, along with the volume of customer interactions. Many customer service teams have been looking into how they can automate more of their operations. But just how much should you automate, and what are some benefits of doing so?
In this Video
Over the last few years, interest in “Automation” and “AI” has grown, especially as the volume of customer interactions has grown - along with greater expectations of customers as well. Many customer service teams are already looking into how they can automate a greater portion of their operations. But just how much should you automate, and what are some benefits of doing so?
Why automate your customer service?
From the firm’s standpoint, customer service automation increases the productivity of your customer service agents, and frees up their time to focus on more sophisticated work, such as building trust and empathy. Automating back-end, administrative processes also reduces agent workload and decreases handle time.
The use of automation also increases overall first contact resolution (FCR) rate, leading not only to happier customers, but better performance overall. According to a whitepaper published by SQM Group, for every 1% improvement in FCR, there is a 1% improvement in customer satisfaction (CSAT).
From the customer’s standpoint, automation helps them get what they need, faster. Great customer service automation also guides and prompts customers towards resolving their query. This makes customers feel that their voices have been heard and grievances looked after, which builds trust for your brand.
So how much automation is needed? Should you automate 80%, 90% or even 100% of all customer service functions? At ultimate.ai, we believe customer experience should drive automation, rather than the other way around. We would argue that in most cases, it is enough to automate 40% of your customer service functions and let your agents establish rapport with customers for the remaining 60%.
Ultimately, you should take an approach that contributes towards the optimal customer experience. This means having simple queries resolved quickly - through automation - while leaving room for a human touch when it comes to more complex interactions. A smart blended approach will leave a more lasting impression than aiming for a 100% automation rate.
Learn how easy it is to get started with automation
The 4 pillars of customer service automation
What should you consider when implementing customer service automation? At ultimate.ai, our platform is built around 4 pillars of customer service automation. These are important pillars to be aware about - but achieving all four is easy, since ultimate.ai’s platform is no-code, user-friendly, and easy to integrate with your existing systems.
Pillar One: Building tailored, data-driven solutions using historical customer-agent conversations
The foundation of customer service automation is data. AI-driven automation makes informed decisions based on rich datasets of past customer interactions. Using raw, real customer data that resembles how your customers are likely to interact with you in future will produce more accurate results.
Pillar Two: Using Natural Language Processing (NLP) that understands how people communicate
Using natural language processing (NLP), ultimate.ai’s system detects various intents, or the reasons a customer contacts customer service, based on the raw customer data. Then, it identifies similar intents and groups them into the most common ones. The system can identify the same intent (such as requesting an order status), even if dozens of customers ask the same question in different ways or in many different languages.
Pillar Three: Designing conversations that are dynamic and personalized
Conversations are then designed to address the different groups of intents. This is where creativity and personalization comes into play. Conversations can be designed to deliver responses and solutions to the customer in a non-robotic, high-quality way, resembling a conversation with an actual human agent. The tone and language of the responses are important as well. The conversation has to make the customer feel comfortable while establishing credibility. In the conversation design process, back-end processes are also synced with the automation solution to seamlessly retrieve relevant information when requested by the customer.
Pillar Four: Launching and continuously improving the solution
Once you have set up your automation and designed your conversation flows, the next step is to launch it. But it doesn’t stop there. When the solution is live and conversations start to be automated, it’s important to constantly iterate on the process, taking into account the new data that is gathered. This means tweaking conversations or intent groups to address new trends as insights surface. As more data is fed to the solution, the solution becomes increasingly independent and can handle more queries without escalation to a human agent.
CEO and Co-founder Reetu Kainulainen outlines how to personalize chat experiences.
Customer service automation in action
The following scenario illustrates how ultimate.ai’s intelligent virtual agent might resolve a typical customer query:
Let’s take an example where a customer asks a virtual agent about his order status, providing some basic information to identify himself.
First, the virtual agent addresses him by name (Example: “Hi Umi!”), then retrieves and shares basic information of the order status based on the customer details provided.
If the customer has another question (Example: “Can I return my item?”), the virtual agent detects the intent (Example: Inquiring about return policy). Then, the virtual agent shares information on the return policy.
Finally, the virtual agent clarifies if any further help is needed - by asking “Do you need help with anything else?” and providing preset prompts for the customer to select (Examples: Yes / No).
This helps to close the loop with the customer and gives the customer an opportunity to seek further help if needed. Even if and when the customer deviates from the preset responses, the virtual agent can still understand the customer’s intent and respond dynamically. (Example: Instead of clicking ‘Yes’ or No’, the customer typed ‘Nope’. The virtual agent understands the conversation has ended, and responds ‘Great, have a good day ahead!’)
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What to keep in mind when designing automation
While the process above may take just a few minutes to complete, designing the entire conversation (Pillar 3) end to end requires taking into account a few key considerations:
Designing based on principles of human-like conversation
For a virtual agent to be effective, the customer has to trust the virtual agent. This is first accomplished through creating a persona that resembles an agent and using a friendly, human-like tone of voice that represents that persona. Once a customer trusts the virtual agent, the customer is more likely to accept support from the virtual agent.
Secondly, the responses of the virtual agent have to be dynamic and personalized. In the example above, the virtual agent addresses customers by name. Additionally, back-end systems have been synced through application programming interfaces (API) to seamlessly retrieve the customer’s information and transaction details. This saves human agents the hassle of having to retrieve this information or customers having to rummage through their inboxes.
Designing around the customer’s behavior
The conversation design also has to adapt to a range of customer responses. For instance, customers may not possess the order number on-hand and need a few moments to locate the receipt - the virtual agent can be programmed to recognize the customer intent and not terminate the conversation even if the customer takes awhile to respond.
Secondly, while some customers depend on prompts and guidance to navigate a conversation, many are also prone to ignore preset buttons. The virtual agent should be programmed to accommodate both button prompts and free text and recognize the intent accordingly.
Designing for a resolution-oriented approach
ultimate.ai follows a resolution-oriented approach in designing conversations - the end goal is to ensure customers’ queries are resolved. In the example above, at the first stage of a conversation, general information is shared with customers. Next, the virtual agent provides guided prompts to the customer - giving step-by-step instructions. It also uses back-end integration to bring up readily available information that is relevant to resolving the query. Finally, there is a check at the end of the conversation to ensure the customer’s query has been addressed. If the customer was not satisfied, the conversation would be escalated to a human agent to ensure maximum customer satisfaction.
Even when a query is escalated to an agent, the virtual agent can still add value by summarizing relevant customer information for the agent to quickly get up to speed with the conversation.
Discover the benefits of automation today
You’ve learned about how customer service automation can not only increase team efficiency, but also free up agent bandwidth to focus on higher value interactions. From stronger brand reputation to more satisfied customers, the benefits are plenty.
To start experiencing these benefits and more, contact us to learn how we can help.
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