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Conversational AI Best Practices: Tips for Apps and Chatbots

This article was published on November 1, 2023

Following conversational AI best practices can help companies drive better results in divisions like customer success and ensure customers don't need to engage with (or wait for) a human every time they need to finish an automatable transaction. AI can enable organizations to build out complex-looking conversation and outcome flows quickly, as long as the platform contains easy low-code manipulation. It's easier to implement than you might think using your existing data.

Illustration of a hand holding a mobile phone; on the screen of the phone is a text conversation between Andy a chatbot; there is a chatbot standing to the left and behind the mobile phone, indicating it is a conversation between a human and a bot

5 Conversational AI best practices

Here's a glance at five best practices for conversational AI:

1. Keep your design approach flexible.

2. Make it easy to keep an ear to the ground.

3. Use context to break through the noise.

4. Remember contextual differences between channels.

5. Lean on existing strengths and build out.

Leaning on these practices can be a good way to avoid simple missteps that might otherwise slow your conversational AI rollout. Conversational AI combines various AI strategies to enable computers to communicate with humans like humans. This technology can analyze human speech and written text across different languages, interpret meaning and context, and respond in a way that mimics human dialogue. Many companies have already used conversational AI to great effect,  and there are probably several angles for your own company to explore and consider.

1. Keep your design approach flexible

Conversational AI exists to make chatbot and human conversations more flexible. They provide more options, outcomes, and possibilities to everyone involved in the exchange and, in a customer service or customer support environment, that creates a need for flexible design solutions that can account for a fast-changing workplace.

Customer tastes change. Departments change. Promotions, policies, and guidelines change. Whatever your company hopes to achieve from its conversational AI, the top best practice on your list should focus on flexibility and adaptability. The conversational AI platform you employ needs to make it easy to introduce changes both subtle and large, or your project's functionality risks being limited from the start. Low- or no-code solutions that encourage fast iteration and implementation are a good place to start in this regard.


2. Make it easy to keep an ear to the ground

By using one of your greatest data strengths — the employees and customers at ground level — you can make sure the artificial intelligence you put out remains relevant and highly functional.

For example, say managers in a contact center notice agents spend a disproportionate amount of time answering one specific question, which requires light inquiry into the account (think: tracking shipments). If the conversational AI they deploy is suitably flexible, addressing the issue is as simple as building the correct response into the workflow. If not, the amount of backend coding and modification needed might make the change impractical to implement.

The same notion applies to generating and collecting feedback. As a best practice, make sure your conversational AI gives you the ability to collect, centralize, and draw insights from the surveys and other feedback measures your customers generate, whatever channel they come from.


3. Use context to break through the noise

You might've heard of notification fatigue, a result of people being bombarded with not-always-relevant information on their devices. Now, here's how to overcome it: send notifications (aka, omnichannel messages) that grab their attention with context and the ability to interact.

That starts with personalization — even if the message has been sent to a large list on the company's side or the customer's query will require account-level insights and responses. Instead of mass messaging, you're starting a two-way conversation with the appropriate context, creating higher engagement numbers and building customer satisfaction instead of contributing to more notification noise.

If your business relies on bulk messaging, personalization is a strong way to stand out in a marketplace that's packed with means to steal customer attention. Engagement is the name of the game, and communications that can be automated and personalized at different scales are a perfect way to build it.

Vonage SMS API, Messages API, AI Studio, and Proactive Connect can give you the tools you need to turn every message you send into a 1:1 conversation — bulk or otherwise.

4. Remember contextual differences between channels

An SMS text is not a WhatsApp message, and a Messenger chat is not an Instagram DM. Customers typically come to companies via each channel for a different purpose. Use your present knowledge of customer activity and the combined data your conversational AI platform provides to tune-in workflows and put this knowledge to your company's advantage.

For a high-level example, a company with an ecommerce component might find that the majority of inbound Instagram messages coming through are sales-related queries, while customers chiming in via Messenger tend to have more operational or account-level questions (such as info on the returns process). When a dive into the data, powered by the same platform, confirms this notion, they're able to quickly make subtle changes to the associated flows, adding Instagram-only pricing promotions that trigger at just the right time.

If you have knowledge of the general motivations that bring customers to each channel, use it to start building up your conversation flows. From there, the insights you curate over time can help you refine the process further until the entire experience is connected and tailored to the individual channel and customer. It's a long-tailed conversational AI best practice but a smart one for companies investing in the conversational AI technology to follow.


5. Lean on existing strengths and build out

Coming into any artificial intelligence/machine learning project, keeping what works should be a continued focus. That includes the ability to keep interfaces customers are used to, channels they're used to communicating in, and tools and methods your business is already comfortable using.

Ideally, the conversational AI solution you choose will allow you to build out current customer-facing communications with new data — key to the flexibility mentioned previously. But beyond that, make sure any tool you consider has the ability to integrate with third-party sources, such as external CRMs. The more insights you can pull from more sources, the more value your AI investment can provide from the outset.

Conversational AI best practices build better engagement

Today's business landscape is dynamic, and the customer experience has evolved from one-way notifications to engaging interactions. Conversational AI is a remarkably fast way to enhance your existing communications infrastructure with tools that make interactions more natural, personalized, and successful.

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By Linda Yamanoha Zwickl Senior Product Marketing Manager, Communications APIs and Conversational Commerce

Linda Yamanoha Zwickl is a Product Marketing Manager at Vonage supporting the Communications APIs and conversational commerce business. She is an experienced product marketer and strategist passionate about bringing innovative business solutions to the market. Linda earned her Bachelor's degree in Computer Science from the University of California, Berkeley, and her Master's degree in Business Management from the London Business School. When she's not working on product marketing strategy, Linda is interested in cooking international cuisines and learning about wine.

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