Conversational AI strategy: A blueprint for success

An introduction to conversational AI and CX Maturity 

Building memorable experiences for customers is quickly becoming a standard practice that brands need to implement. But in a digital world where convenience is common, and customers are not easily impressed, brands need to focus on delivering easy, simple, and conversational experiences to make their mark. 

That’s where conversational artificial intelligence (AI) and customer experience (CX) maturity come into play. Conversational CX Maturity is a framework used to measure how effective a brand’s communication with customers is. It considers channels, use cases, software, technology, and integrations to evaluate how mature a brand’s conversational strategy is.

A big contributor to a brand’s maturity is their use of conversational AI. If a brand is able to implement a successful, cost effective, and ethical AI solution, it means that they are prepared to deliver great conversational experiences.

But how can brands prepare to implement conversational AI to improve their maturity? A strategy is a key component to preparing your brand for a conversational transformation. That means going from long wait times in call centers, impersonal or generalized marketing messages, and one-way interactions to fast, convenient, and personalized two-way conversations with customers.  

It’s not a simple or easy journey to successful implementation, but with a solid strategy and experienced partner, brands can build stronger relationships through conversational experiences, with the help of AI.

Why businesses should implement conversational AI

With more and more adoption of conversational AI, it is becoming more accessible to brands, and they are starting to see the benefits payoff in their day-to-day interactions with customers. A few reasons why this is important for business operations include:

24/7 availability 

Conversational AI can power chatbots or virtual assistants that can chat with customers any time of day – no more working-hours-only call centers. This boosts overall satisfaction with your brand’s customer support services and provides your audience with the convenient support they are looking for.  

Personalized customer interactions

A powerful part of AI is the ability to take valuable customer data and use it to create personalized experiences. This helps brands build a deeper and more loyal relationship with their customers by sending hyper personalized messages that take into account their demographics, behavior, purchasing history, and anything else relevant enough to tailor their experience. 

Immediate responses customer interactions

Conversational AI makes time to resolution much faster. Customers can get an instant response to a query instead of waiting for a response or in a long call queue. With self-service use cases, customers can resolve their own queries much faster than with traditional solutions. 

Automating routine tasks 

Most customer queries are often simple and routine that can be handled by a conversational AI chatbot. Things like FAQs, status updates, account information, or even booking appointments can be quickly resolved through a chat. Conversational AI automation allows brands to handle multiple queries at once, making the entire journey more enjoyable for end-users. 

Off-loading work from agents 

Live agent support is still a critical part of a brand’s customer support solution. Live agents are able to focus on the most complex and sensitive tasks that come through, while the conversational AI system handles the mundane and repetitive tasks that take up too much time. That means that customers can complete some tasks though a self-service conversational chatbot, while others with more serious or complicated tasks can connect with a live agent faster.

Insights from data

Conversational AI interactions help collect valuable data from customers. By understanding the types of queries customers have, the frequency, where the pain points are, how satisfied they are with the solution, and even behavioral data, important insights can be collected. This helps brands make more informed decisions with their strategic planning, ultimately giving them a competitive edge.

Example: A courier services uses conversational AI to understand the customer’s query, personalize the message, automate simple but important tasks, and streamline the delivery service for the user

6 key components of a conversational AI strategy

So, what makes for a successful strategy? There are six key components that help brands make a more impactful solution:

1. Natural Language Processing (NLP)

NLP powers conversational chatbots to understand human language. It’s important that for the solution to be truly conversational, the AI can understand things like intent, sentiment, and even slang.  

For example, a retail customer that is waiting for their delivery might ask: 

Thanks to NLP, the chatbot can understand the context of the order placement, the timeframe being last week, the sentiment that the customer isn’t happy, and the inquiry asking where the order is. 

Based on this, the conversational AI chatbot might respond with:  

2. Machine Learning (ML) 

ML algorithms teach conversational chatbots how to respond to prompts. Based on training data, chatbots can learn patterns which allow them to appropriately generate a response.  

For example, a telco customer might ask something like this: 

The context of the prompt is quite clear, but a system that has not been properly trained on sufficient data and scenarios might respond with something like:

This response would be even more frustrating for the user. Since the chatbot wasn’t trained properly, it failed to categorize this query as a technical issue and not an upsell use case. To form an appropriate response, a good machine learning system would pull the customers data as well as the context of the message to have the full context.

3. Conversational User Interface (CUI)

The entire point of this solution is to keep the customer journey and interactions conversational. That means that the customer can use natural language through texting or voice to communicate with an AI and get human-like responses. They don’t need to simplify their language or use specific semantics to get the answer they are looking for. They can interact with the AI chatbot as if they are talking to a live agent.  

For example, a healthcare patient might ask for an appointment in a few ways:  

The CUI will be able to understand all these queries as the patient looking for an appointment with Dr. Jones and it can respond accordingly.  

4. Knowledge base

Having a solid knowledge base is a crucial component of a conversational AI strategy. Without one, NLP, ML, and CUI can’t function properly. A knowledge base is what the conversational AI technologies use to understand situational queries and helps strengthen the algorithm to provide the right responses. It is also the key to improving response accuracy and limiting the risk of AI hallucinations. 

With a thorough knowledge base, artificial intelligence solutions can quickly retrieve the information they need about customers and scenarios to generate the right responses.  

For example, with a good knowledge base, the AI can provide different responses to the same query based on customer data:  

The response can change based on if the customer is new or existing, if they are a frequent traveler, if they hold a family plan, or how much data they use monthly. The information from this knowledge base can tailor responses to make them more relevant and useful to the user. 

5. Channels

Conversational AI wouldn’t be all that effective if it was on the wrong channel. A brand’s channel choice can have a huge effect on how successful the solution is. The entire point is for the interaction to be convenient, fast, and familiar. Using channels that customers are already on is a crucial part of the strategy.  

For example, WhatsApp, RCS, Viber, and Apple Messages for Business are excellent channels for a conversational AI chatbot. A web or app chatbot can also be a great option for customer support chatbots since it gives easy access to customers who are browsing your website or mobile app for answers.  

6. Analytics

Last but definitely not least, analytics play a key role in conversational AI strategies. Data collection and analysis helps brands understand the effectiveness of their interactions, which means they can make better, more strategic decisions in the future.  

• Time to resolution 
• The number of messages exchanged in a conversation 
• Customer satisfaction with the chatbot 
• Measuring user sentiment and emotions 
• Conversion rates 
• Improving personalization  

These are all ways that brands can use analytics to understand how helpful their conversational AI solution is and how to make improvements in their strategy for the future. 

Building a conversational AI strategy: Step-by-step guide 

A solid strategy is the key to building successful conversational AI system. Here’s a step-by-step guide on where to get started and how to plan your conversational AI journey. 

1. Define clear goals 

What exactly do you want to achieve with a conversational AI solution? Having a driving force behind your transformation effort will help your brand stay on track and understand how successful your implementation was. Here are a few examples of some objectives brands might have for using this technology:  

It’s important to be as specific as possible. Include specific use cases like customer support, lead generation, or personalized recommendations to make sure you are laser focused on what you can offer customers through AI. Ensure your KPIs for this project are measurable like response accuracy, CSAT scores, or time to resolution, this way you can easily understand how successful the solution was at the end of your timeframe.  

2. Understand your audience 

Brands should always be aware of who they are talking to. Sending poorly targeted or over generalized messages can quickly lead to your campaigns and efforts falling flat with your customers.  

Brands should do their due diligence and ensure the AI capabilities meet the expectations of customers and solve real problems they have. They can do this by analyzing demographics, preferences, communication styles, and preferred channels. Through customer data insights, surveys, feedback, and customer interaction history, brands can build detailed personas that help uncover the needs of their audience.  

3. Map out the customer journey 

Understand your customers’ experience with your brand. By mapping out end-to-end journeys, brands can better understand the pain points and opportunities of each touchpoint. This can help brands see where AI can be the most effective and have the greatest impact. 

There are multiple areas in the customer journey that could benefit from conversational AI. Each brand will have a different needs since this is a custom and unique solution that changes from scenario to scenario. Here are a few examples of how conversational artificial intelligence can be implemented in different stages of the customer journey based on the needs of the brand and their audience: 

Each of these touchpoints and the needs of the customers at these stages is unique to each brand. That’s why mapping the journey is so critical so brands can identify the right time and place for AI.  

4. Technology and integration 

The tools and software brands choose to build and launch their conversational AI solution can make or break their strategy. Brands need to choose tools that align with the objectives, goals, and infrastructure of the business.  

Brands should evaluate things like:  

• NLP / ML capabilities 
• Omnichannel support 
• Scalability  
• Analytics  
• CX consultants and AI professionals 
• Integrations with other tools and composability 

Your AI solution provider should be able to integrate into existing systems like CRMs, ERPs, CDPs, and other back-end systems to provide personalized and accurate responses. Full integration is ideal for the best results. With partially integrated tools, channels, and systems, brands will likely fall short of delivering the best experiences. 

The technology and the technological partners you choose should be able to support a custom solution, offer experienced support, and be flexible enough to build an AI solution that works for your specific brand. 

5. Design your conversational experience 

Now it’s time to create your conversational flow for your use cases. These flows are designed to ensure customers are satisfied and to reduce friction during their journey. It’s important to remember that these flows are designed for the benefit of the customer, so some user-centric design principles include: 

Make sure to test and refine your chatbot to ensure the best flow possible. For example, an insurance chatbot should be able to guide customers through claim submission with step-by-step instructions, answer questions, and keep customers updated to keep them satisfied. 

6. Train the conversational AI  

AI can sometimes get a bad reputation for hallucinating or providing false information. That’s why training data quality and accuracy is of the utmost importance. In combination with being specific with use cases and flows, training data is what makes the conversation feel human and relevant to the user.  

High-quality data ensures the AI understands context, intents, and variations in user input. Brands can use historical data from chat logs, customer support tickets, or FAQs to train the AI. Data should be continuously updated based on new queries customers have and new updates to your brand’s products and services. Natural Language Processing and Machine Learning enable AI to understand common language, abstract ideas, and slang so that customers can truly have a conversational experience with a chatbot.  

For example, a conversational AI chatbot should recognize both of these messages as a shipping issue:

7. Test and optimize

Now that your AI solution is set up and trained, it’s time to test conversations and identify gaps, bugs, or limitations before it’s deployed to your audience. Brands should conduct user testing with their target audience and gather feedback, test for ambiguous queries or out of scope requests, as well as measure the performance of the chatbot based on the predefined KPIs you established in step one.  

Through feedback, brands can learn about new pain points in the conversation and areas for improvement. Updating the knowledge base with new data, feedback, and old conversations will give the AI solution better context and expand its training data to refine intent mapping. 

Customer needs and expectations will change over time, which means the AI needs to adapt to these changes. Adding new features like voice capabilities or multi-language support can optimize your solution. You can expand use cases and scale the AI deployment to include additional channels or regions as you grow. 

Creating a conversational AI strategy: Best practices and tips 

To make the most of your strategy, here are some actionable tips to develop a successful solution:

1. Start with a pilot project: A pilot project allows you to test, learn, and refine your approach before committing to a full-scale launch 
2. Prioritize user experience: Design simple, clear, and goal-focused conversational flows using natural language and personalization to make interactions relevant and engaging 
3. Foster a culture of continuous learning: Review conversation history regularly to identify gaps, update AI knowledge base with new data, and encourage users to give feedback 
4. Ensure data privacy and security: Ensure you are compliant with data protection laws like GDPR, CCPA, or even HIPPA. Use encryption and anonymization to secure user data and be transparent with customers about how their information is used. 
5. Enable easy hand off to agents: Conversational AI assistants can handle a wide range of queries, but make sure you provide easy hand off to a human agent to provide a truly well-rounded experience and avoid pain points. 
6. Measure the right metrics: Track KPIs like response time, resolution rate, user satisfaction (CSAT), and fallback rate. Monitor conversation abandonment rates to identify pain points and use A/B testing to optimize conversational flows. 

Considerations around designing an effective conversational AI strategy 

Careful planning and design should be at the heart of your strategy. It’s not enough to follow the steps, cross your fingers, and hope for the best. Each step of your conversational journey should be well thought-out and planned based on brand and customer needs. 

A good starting point is mapping out conversational flows. These flows are like blueprints for how the AI responds to user inputs, guiding the conversation in a logical and intuitive way. Flows should anticipate user intents, offer relevant follow-ups, and include fallback paths for when the AI cannot understand a prompt. Tools like decision trees or visual flowcharts can help structure these pathways for clarity and effectiveness. 

The tone of voice of your AI assistant is a big aspect of what makes the experience conversational. The tone should reflect the brand, while also considering who you are talking to. For example, a bank might prefer to have a more formal tone, but when running a campaign targeted to their Gen Z customers, they might consider adding a more relaxed and friendlier approach to the conversation. It’s also important to remember what channel you’re using to connect with customers. WhatsApp users are accustomed to chatting with friends and family over the app, and so robotic or over-generalized language won’t land with them. Personalization and tone of voice can go a long way to ensure the AI chatbot is effective with users.  

Errors and mistakes can happen, but how your AI is trained at dealing with that will impact the customer experience. If it cannot understand a user query, it should be able to guide the conversation back on track using clarifying prompts, alternative suggestions, or escalating to a human agent when appropriate. Incorporating multimodal interactions, like voice, text, or images can enhance accessibility and flexibility, ensuring users can engage with the AI in a number of different ways. 

Finally, design the artificial intelligence to support users with diverse needs by incorporating features like text-to-speech, visual aids, and compatibility with assistive technologies. Ethical considerations, including transparency and fairness, should also be prioritized. Let users know how their data is used and ensure the AI is unbiased, providing equitable treatment to all users regardless of demographic or background.

How to implement omnichannel into a conversational AI strategy 

Omnichannel capabilities and conversational AI combined can offer customers the smoothest experiences. An omnichannel plan allows customers to move between channels and continue the same conversation, giving the AI chatbots or live agents all the contextual information needed for the conversation to flow naturally. That’s because omnichannel systems are integrated with other tools and software, making it easy to share data and information that allow conversations to flow.  

Imagine the frustration of chatting with a technical support chatbot on WhatsApp, not resolving the issue, and then being transferred to a human agent on a phone call who asks you to repeat the issue again. 

With an omnichannel platform or solution, all the contextual information from the chatbot conversation is sent directly to the agent, so they can review and help the customer without asking any repetitive questions – keeping this pain point at bay.  

An omnichannel platform allows brands to centrally manage all customer communication. All information, data, detailed profiles, and conversational history is stored and available in one place, where all tools are integrated and connected. That includes: 

• All channels needed for communication 
• Customer engagement tools for marketing 
• Customer data platform 
• Cloud contact center 
• Chatbot builders 

Brands first need to determine what channels their customers prefer to use, and ensure they are fully integrated into their platform or existing systems. Then, depending on the channel, the conversational AI tools can differ. The conversational flows or chatbots that are built on WhatsApp can look very different from ones on RCS, Viber, or web chat. That’s because each channel carries its own specific features and capabilities that can enhance the conversation. As time goes on, it’s important for brands to continuously monitor their omnichannel flows and conversational assistants to improve the experience of the customer as their needs evolve and as the business grows. 

How to measure the success of a conversational AI strategy 

There are many ways brands can tell whether their strategy is paying off – here are some critical metrics and evaluations brands can monitor to measure their success: 

Overcoming challenges in conversational AI strategy implementation 

Creating a conversational AI systems is no easy task. There are multiple challenges and obstacles brands need to be aware of and navigate to ensure their solution is a success.  

Data quality can make or break your ability to build an AI solution. Cleaning data is an essential part of updating and building knowledge bases, training NLP and ML, and making sure interactions are relevant to users. Disjointed and disorganized data makes it much harder to build and train AI to resolve use cases.  

Similarly, poor integration makes conversational AI implementation much more difficult. Part of your strategy should include ensuring your systems and channels are properly (and fully) integrated. Poor or partial integration means that you will have a harder time building flows, sharing data, and allowing conversations to flow from one use case to another.  

User adoption is another challenge that brands often face. You have to onboard users to the channel and chatbot, as well as ensure full transparency on how their data is being used, what the conversational AI can accomplish for them, and that the conversation is fully secure to gain their trust.  

Lastly, each brand will face different compliance and regulatory laws depending on where they operate. When building the conversational AI, make sure your solution is fully compliant to avoid being blocked or blacklisted from using certain channels or being flagged as spam. Compliance laws around user data should be at the forefront of your strategy – one slip up with these regulations can lead to a bad reputation and monetary fines depending on the region. 

All in all, being proactive with improvements and updates will allow brands to stay compliant, secure, and innovative with their conversational AI. 

Conversational AI tools and resources

So, where to start? 

You’d want to find conversational experts to help you on your journey to building transformative experiences. The tools and resources provided by these professionals will help unlock the capabilities of conversational AI.

Strong partnerships  

Look for a provider with a robust partner network to streamline implementation and ensure your solution can grow alongside your business needs. Infobip has strong partnerships with a wide range of brands – which means integration with your existing systems, or building custom solutions is made smoother. 

Composable platform 

A composable platform is another essential tool for brands to prioritize when building conversational AI. Composable platforms give you the flexibility to build and customize your solution using modular components, which means you can add or modify functionalities as your business evolves. This approach ensures brands are not locked into a rigid system and can adapt to changing customer demands or technological advancements. 

Unified platform

Using a unified conversational platform is another great option for brands. Infobip offers one single platform that carries all the features and software needed to build, launch, monitor, and optimize customer experiences. A unified platform eliminates silos, ensuring a cohesive experience for brands and customers. By centralizing data and operations, it allows for a more efficient deployment of AI tools while enabling seamless collaboration across departments. 

CX and AI experts 

Beyond the tech, brands need experienced professionals to help build their conversational strategy and solution. Infobip offers in-house customer experience (CX) experts and AI specialists that work together to design custom solutions for each industry and audience. They are experienced in guiding brands on how to use conversational AI to its full potential, whether by optimizing chatbot workflows, crafting personalized messages, or identifying high-impact use cases.