Exploring the Potential of AI in Customer Service – 4 Crucial Points to Consider

The integration of generative AI has become a buzzworthy topic among business leaders across various industries. As businesses are now eagerly exploring the potential of harnessing the power of AI, we, as a provider of an AI-assisted solution, want to share some practical insights that can steer your AI-powered journey in the right direction.

Automation: Look Beyond Quick Fixes

Don’t let the current AI excitement blind you. Keep in mind that AI’s best potential lies in how well it lines up with your overall business plans and objectives. So, before you dive in, really think about?why?and?how?you want to use AI.

Often, companies turn to AI to handle common customer questions in customer service. This can help out the customer service team in the short term, but it also might add more software to the support ecosystem. If you really want to make things better for customers, just directing them to AI-automated self-service might not be the best idea. For customers, it’s better if they don’t have a problem in the first place rather than having AI to solve it.

So, instead of directing your customers through various communication channels, consider leveraging AI to uncover the underlying reasons for their initial contact. Use AI to aggregate and enrich data, enabling you to identify areas of friction within your service process – all at a quicker pace than manual methods allow. While AI won’t make decisions on your behalf, it can provide you with data and insights to inform your decision-making process.

By pinpointing and tackling the real contact reasons, you can actually cut down on how many customers need to reach out. And that’s good news for both your business and customer happiness.

Source Data Quality: The Key to Success

Having good source data for AI to work with is incredibly important. Seriously, I can’t stress this enough. Attempting to extract valuable insights from poor-quality data is comparable to searching for a needle in a haystack. Since generative AI relies on transcripts of customer interactions, the quality of these transcripts, call or chat, make a real difference.

Based on what we’ve learned, here’s a few crucial points especially about call transcripts:

• Each person talking should be recorded on separate audio tracks, so you can tell who’s talking from the transcript.
• Data anonymisation is done carefully, yet tactfully – personal details should be removed, but things like product details should stay. This way, you get insights that are actually useful.

And here’s a big one – the call transcript must?exclude?any background chatter and lyrics from the waiting music while customers are in the queue (believe it or not, this happens a lot – and yes, it costs you extra).

Selecting a reliable transcript provider can make a notable difference, both positively and negatively. That’s why we recommend testing the quality of transcripts with a smaller dataset before committing to larger transcription services. And don’t forget to prioritise data security: inquire about where the data goes, especially the personal information, during the transcription process.

From a business standpoint, merely having transcripts of customer conversations often falls short. While AI can understand text, this might not be enough for meaningful business insights.. To truly unlock the potential of these insights, data enrichment is essential. This involves adding contextual details about who the customers are and the topics they discuss – metadata like segments, products, or services.

Tangible Metrics: Turning Insights into Action

Insights alone don’t drive change; you need actionable metrics to turn them into action. While finding out how people feel through sentiment analysis is interesting, but at the end of the day it’s the business numbers that really matter. People who make decisions need figures that match up with the choices they’re making.

That’s why it’s worth considering what kind of business metrics the AI service you’re thinking about will provide. Is it more of just nice-to-know information, or can it actually create measurable business impact?

Proof-of-Concept: Set Stage for Scalability

Think of a Proof-of-Concept (POC) as a way to test how AI works in your everyday tasks. Start by giving it a spin with a specific set of data to check if it actually adds value and functions well from a technical perspective.

However, remember that a successful POC is just the beginning. Consider what comes next to ensure ongoing benefits. Reflect on questions like:

• Can it handle more work and data as demand increases?
• How seamlessly can it fit into existing workflows?
• Is the long-term benefit worth the investment of time and money – the business case?
• Is the AI increasing operational efficiency while enhancing the overall service experience for your customer?

Taking the First Steps

In a nutshell, generative AI offers significant possibilities for businesses, and now is a fantastic time to start embracing it. Yet, similar to choosing between building from scratch or adopting a ready-made solution, careful consideration is necessary before deciding on software.

Keep in mind that AI automation should align with addressing root causes, data quality must be top-notch, actionable metrics should guide decisions, and the POC should set the stage for scalability.

By keeping these fundamentals in mind, AI can truly become a valuable tool for businesses to enhance customer experiences and boost operational efficiency.

Welcome to the future of customer service, powered by AI!