How to Use Conversational AI for Customer Service

Aksheeta Tyagi

November 28, 20239 min read

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Natural conversations are what help us express ourselves the best. Whether we’re talking to people — or AI — we all want to be understood. And isn’t that what customer service is, essentially? Making it effortless and organic is what makes it great. Using conversational AI for customer service can help you connect with your users mindfully without weighing your service agents down.

But where do you begin? By the end of this blog, you’ll know what you need to implement conversational AI, how it works and how you can use it to deliver the service your customers deserve.

Table of Contents

What is conversational AI?

Conversational AI in customer service blends machine learning and artificial intelligence to mimic natural dialogs, akin to interactions with a real agent. This technology powers self-service tools like chatbots and Interactive Voice Response (IVR), enabling organizations to handle customer queries autonomously without human intervention. It analyzes the user's sentiment and intent, responding in a conversational, human-like tone. Conversational AI improves efficiency, reduces response times and enhances the user experience, leading to positive customer support experiences.

How does conversational AI work?

Conversational AI in customer service works in a sequence of these four steps.

Step #1: Data capture

This is where the journey begins. The system collects user inputs, whether typed messages, spoken words or images, setting the stage for meaningful interaction.

Step #2: Data interpretation

Here, the AI flexes its analytical muscles. It employs natural language processing (NLP) to decode text and automatic speech recognition (ASR) along with speech analytics to assess voice input, detecting the user's intent and entities. This step is pivotal in ensuring responses are accurate and resonant with the user's expectations.

Functionalities NLP uses to decode user input

Step #3: Response formulation

In this phase, the AI crafts its reply. Using natural language generation (NLG), it constructs responses that are coherent and contextually relevant, mirroring human-like engagement. It also employs dialog trees to guide its next steps based on user input, including asking follow-up questions, making API calls or transferring to a live chat.

Step #4: Adaptive learning

The AI then enters a phase of self-evolution. Through reinforcement learning, it assesses and learns from each interaction, continuously sharpening its accuracy and relevance and becoming more adept at understanding and responding to human queries.

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4 Benefits of conversational AI for customer service

There’s much to reap as an AI adopter. Check out these benefits of using conversational AI for customer service.

  1. Extended support hours
    Conversational AI bridges the time gap in customer service. It empowers businesses to offer support beyond usual hours, which is crucial for global operations across different time zones. This continuous availability lets customers receive help at their convenience, enhancing digital customer experiences.

    24x7 availability of conversational AI

  2. Immediate relevant response delivery
    Speed and context are key in customer service and conversational AI excels here. It delivers prompt answers, reducing wait times dramatically. This quick response capability is vital in keeping customers informed and engaged. Conversational AI stays current and changes tracks of a conversation instantly. See how it works for a user who wants to know about credit cards.

    Conversational AI detects intent and context switches

  3. Seamless scaling
    As business demands grow, conversational AI scales effortlessly. It handles multiple queries simultaneously, maintaining quality service without taxing your support team. Such scalability is essential for businesses experiencing rapid growth or seasonal spikes.

  4. Unified service delivery
    Conversational AI creates a seamless thread across all communication platforms. A customer’s journey, from an initial query on a chatbot to follow-up emails, is fluid and connected, ensuring no detail is lost and every concern is addressed with continuity.

    Conversational AI carries cross-channel context for unified service

  5. Cost-effective support
    Implementing conversational AI is a strategic move to optimize costs. It alleviates the need for a large support staff, managing routine inquiries efficiently, and delivering quality and efficient service with budget considerations.

    Automate Customer Care with Conversational AI bots

How to implement conversational AI in customer service

To get the most out of it, you need to create a solid conversational AI strategy in place. Here are the basic steps you need to follow to implement conversational AI in customer service.

Step 1: Chart goals and use cases

Identify specific, trackable objectives for your AI — for handling queries and beyond. Think in terms of enhancing user experience, personalizing interactions or innovating customer engagement. Tailor use cases to these goals.

Step 2: Engage stakeholders creatively

Instead of standard presentations, use interactive sessions or demos to showcase the potential of conversational AI to stakeholders. Focus on demonstrating tangible benefits and potential ROI to gain their support.

Step 3: Budget and allocate resources

Beyond basic budgeting, consider creative funding options like partnerships with AI vendors or phased investment plans. Assess internal resources and the possibility of reallocating existing assets to support the AI initiative. For instance, a smaller team with low budget caps may benefit from a no-code conversational AI bot builder.

Step 4: Leverage existing infrastructure

Analyze your current tech stack for untapped potential. Find ways to integrate AI that amplify existing strengths or address known gaps rather than overlaying new technology. For example, if your team often deals with a lack of context on the customer during interactions, a conversational AI solution that builds a unified customer profile as a single source of truth might be the way to go.

Step 5: Launch and monitor

When launching conversational AI, initiate a controlled rollout, closely tracking response accuracy, customer sentiment and resolution efficiency.

Conversational AI tips to push customer service a leap forward

While you know the basic steps to implement conversational AI in your customer service, stay ahead with these conversational AI tips in customer service:

1. Upgrade it with generative AI

Generative AI can more precisely comprehend any user input than just conversational AI. When combined together, they can assess the query and the interaction’s tone and severity — to help agents adjust their demeanor in real time. Customers expect empathy, which makes it important for you to always stay in touch with the energy they bring to a conversation. Generative AI reads the room for you, so you always respond with authenticity and relevance.

Sprinklr AI+ collaborates with OpenAI to fuse human-like insight in all your customer service interactions. It can assess the conversation’s tone, surface the conversation’s key highlights and pull up relevant information from your help center. So, whenever you have to find a solution for your customer, you can be confident in what you convey and how you do it.

Read more: 7 Steps to implement generative AI in your customer service

2. Blend in brand governance

Your conversational AI should simplify brand governance for you. A true quality of a strong brand is the consistency in its speech and messaging — across all platforms and all contexts. Here are some ways it can make things simpler for you:

  • Consistent brand messaging: Conversational AI upholds uniform responses across channels, echoing your brand's voice and values consistently, which is vital for brand identity.

  • Controlled response quality: AI minimizes response variability, programmed to meet your set standards, safeguarding your online brand reputation in every interaction.

  • Real-time brand compliance: Conversation analytics uses AI-powered approval workflows to monitor conversations live, ensuring adherence to brand guidelines and swiftly correcting deviations to maintain brand integrity.

3. Use a conversational AI solution that has a use case library

While there are many contenders in the market for you to choose from, prioritize the ones equipped with comprehensive libraries of pre-defined intents. Such a library acts as a foundation to build bots steeped in your field's nuances. These ready-to-deploy bots are tailored to your industry needs, ensuring proficiency from the get-go.

Pro Tip: Ideally, a solution with an extensive catalog of industry-specific intents is a strategic move. It indicates a mature AI system. For example, Sprinklr’s conversational AI platform is equipped with 250+ bot templates with AI Intents across top industry verticals to give you a head-start.

Bot use case library in Sprinklr conversational AI platform

4. Give your bot a personality

Another best practice is to infuse your conversational AI with a voice that echoes your brand's personality. When you train your bot to speak in a carefully selected human voice, complete with nuanced modulations, you transform it from a mere responder to a relatable virtual agent.

Conversational AI adds human touch with customizable voice and personality

Use cases - Conversational AI in customer service for businesses

Conversational AI brings independence to everyone involved in a service interaction — customers and contact center agents alike. Here are the use cases it can solve for.

1. Self service

Conversational AI makes your customers adept at finding solutions independently, without delays or anyone’s intervention. AI-powered self-service can help your customers find answers and resolve many common scenarios. Here are a few. 

  • Account inquiries

  • Password reset

  • Order tracking

  • Billing questions

  • Basic troubleshooting

  • Updating personal information

  • Appointment scheduling

Conversational AI retains information from customer interactions and structures it neatly for your team to find it just a hop, skip and jump away.

Interesting read: Customer self-service and its top types

Gartner stat on self service


2. Bespoke recommendations

Conversational AI can be a catalyst for tailored shopping experiences. Let me lead with an example.

Frankie, a budget-conscious music lover, is searching for headphones under $100 with 2-day delivery. Entering her requirements, conversational AI springs into action. It combs through options, balancing cost with quality and quickly presents her with the perfect find — affordable, high-quality headphones available for swift delivery.

Conversational AI can work as a personal shopping guide — here, understanding Frankie’s urgency and budget, ensuring she doesn’t just make a purchase, but makes the right one.

Conversational AI makes personalized suggestions based on user needs

3. Trendspotting and automated workflows

Conversational AI can analyze customer dialogs to build trends around common queries. It can then suggest users with clear workflows that are automated to guide them to solutions easily.

For example, let’s take Matt’s new speakers that are buzzing annoyingly.

He reaches out to customer service, unaware that his issue is part of a larger pattern. The company's conversational AI has been tracking similar issues, revealing “Faulty Equipment” as a frequent customer complaint. This trend, now visually mapped out, alerts the company to a broader problem. With this collective insight, the AI offers Matt a direct path to resolution, alongside others with the same issue, ensuring a swift, unified response.

Conversational AI helps in surfacing trending intent clusters and sentiments

4. Use it to manage incidents

You can even use conversational AI to mitigate crises and incidents. During the critical time window, it can activate pre-defined protocols specifically designed to handle sensitive or high-stakes scenarios.

Here’s how conversational AI can lead the entire incident management for you.

Phase 1: Trigger identification

AI detects crisis indicators like negative sentiment spikes, specific complaint keywords or abnormal query volumes using advanced text analysis and voice analytics.

Phase 2: Protocol activation

On trigger detection, AI shifts to crisis mode, employing pre-set, empathetic responses that reflect the brand's crisis communication management, ensuring on-time, mitigative messaging.

Phase 3: Escalation mechanics

AI can be programmed to automatically escalate to human supervisors when encountering issues beyond its handling capacity, ensuring that complex situations receive appropriate attention.

Read more: Your guide on how to manage escalations

Phase 4: Adaptive response

Throughout the crisis, AI dynamically adjusts responses, using machine learning to refine its approach based on real-time data and evolving scenarios.

Phase 5: Post-crisis insights

Post-crisis, AI provides detailed analytics on customer interactions and response effectiveness, building future strategies and improving AI crisis handling.

5. Collect feedback swiftly

Conversational AI can collect customer feedback by naturally blending the mechanism into conversations. While customer surveys can be a great way to gauge their sentiment, users often face survey fatigue when they are made to fill out repetitive, impersonal studies.

Learn more: 5 Solid ways to measure customer satisfaction without surveys

Conversational blends feedback systems naturally in conversations

Vivo + Sprinklr’s conversational AI = Customer delight

Conversational AI can help businesses settle into consistency. Where 75% of customers expect uniform experiences across channels, conversational AI ensures just that.

When Vivo faced a mountain of a million messages a year on social media, it turned to AI. And not just to chip away at the volume but to add value to each interaction. Sprinklr AI helped Vivo hit a sweet spot between automated smarts and the human touch.

The approach was multi-faceted: AI sorted messages, prioritizing those needing human attention, and suggested quick, on-brand responses to common queries. It even got smart about customer satisfaction, flagging messages that hinted at discontent for priority attention. More than just a traffic cop for chats, Vivo’s AI upheld its brand reputation, watching for sensitive issues.

And the payoff was huge: The telco company slashed response times by 65%, boosted positive feedback by 137%, and their AI became a regular feature in 70% of replies.

Reflect these numbers in quarterly meetings with your CEO too. Take Sprinklr conversational AI for a spin for 30 days.

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