How to use AI to analyze phone calls and improve your customer experience

So, you’re running a contact center or call center.

What’s the most valuable source of customer intelligence you have?

Most people might say customer feedback, or CSAT surveys, or NPS surveys, or some other metrics data.

And they’re not wrong. But at the heart of all of this, if you zoom out to the big picture, are your phone calls with those customers.

Every piece of customer feedback, every question, every complaint that your customers are calling in contains valuable nuggets of information. But how can you analyze all of those calls and pick out the useful bits?

It would take hours for someone to listen to every phone call recording from beginning to end, take notes, and sort all of that information—even if they were a fast typist!

This is where we use artificial intelligence, or AI, to analyze customer phone calls and uncover these insights. It’s automated, it takes way less time, and it allows us to make improvements and update training for agents much more effectively and efficiently.

I’ll walk you through how it all works, how we use AI to run our own contact center, and how we built a contact center AI to not only analyze calls, but also empower our agents and supervisors to perform at a much higher level.

Is it possible for AI to analyze calls?

Yes, it is possible for AI to analyze phone calls.

There are a few ways that it does this. As a first step, AI almost always needs to be able to transcribe the phone call or video call first:

With this transcription, the AI can then analyze these phone calls for things like sentiment, keywords and topics, and even CSAT scores.

How to use AI to analyze calls

Step 1: Have an AI-powered communications platform

To use AI to analyze calls, you first need to have an Ai-powered communications solution. Yes, there are tools that you can “stack” onto your phone system or cloud contact center platform, but the workflows tend to be clunky and data might not sync automatically because you’re still using separate tools at the end of the day.

Plus, there are solutions like Dialpad Ai Contact Center that are fully AI-native anyway, which means the AI is already built into the system and you don’t need to pay for any third-party AI vendors or integrations.

Step 2: Turn on AI and/or transcription

Next, you’ll need to make sure you can turn on AI-powered transcription so that you have data to analyze from your phone calls.

With Dialpad, for instance, you can turn Dialpad Ai on and off right from your call screen by hitting the “Ai-Enabled” button:

Step 3: Decide what you want to do with the data

This is one of the most important steps: you need to know what you want to learn about or understand from your phone call data.

Your customer conversations can cover literally anything, from complaints, to praise for your agents, to comments about your competitors, to questions about pricing.

So now, you need to figure out what specifically you want to analyze. To give you a hint, you could start by thinking about whether you want to analyze phone calls in real time or post-call, as there are different things you can do for both.

Live on-call analysis

AI can also analyze sentiment of phone calls in real time. With Dialpad Ai, for example, if a supervisor is overseeing tens or hundreds of agents, they can quickly see if any calls are dipping into negative sentiment territory:

If they do, they can open up the real-time transcript to get more context before deciding whether or not they need to help the agent or barge the call.

From an agent empowerment perspective, AI can provide real-time assists through call pops automatically when tricky questions come up. What’s special about Dialpad Ai is it can search all connected knowledge sources—even unstructured ones like PDFs and past customer conversations—for information to help agents live on calls:

Post-call analysis

In terms of post-call analysis, AI can also do things like help with suggesting dispositions for calls based on what was discussed, and even help supervisors with QA scoring since it can analyze the conversation. (Did an agent greet the customer? Did they close with a suggested cross-sell or upsell?)

Then there’s keyword and topic tracking. Every customer service AI platform is different in terms of how you can set this up, but in Dialpad as an example, you can set up “Custom Moments” to track whatever topics you like:

Say a supervisor wants to see how often customers are asking for refunds. With Dialpad’s AI-driven conversation analytics, you can create a Custom Moment to track when terms like 'refund,' 'money back,' and/or 'cancel' are mentioned during calls. If you notice any spikes or anomalies in your analytics, you can dig into the transcripts for more context.

The benefits of using AI to analyze phone calls

As you can imagine by now, there are many benefits of using AI to analyze phone calls. Here are the biggest ones, no matter what industry or business you’re in.

More helpful insights

By using AI to analyze your customer phone calls, you can not only gather more insights into what your customers are saying, but also more easily identify areas where you can improve the customer experience (through better agent training and even self-service AI options).

For example, one of the biggest challenges with gathering CSAT responses is that not a lot of people actually fill out those surveys.

In fact (depending on the industry and specific business of course), we've found that on average only about 5% of customers actually fill out CSAT surveys. On a related note, usually only the angriest—and happiest—customers actually bother to respond to these surveys, which means your CSAT answers are likely to be very skewed and not representative of how your customers feel overall.

Dialpad's industry-first Ai CSAT feature is designed to solve exactly that. Not only can Dialpad Ai transcribe calls and analyze sentiment in real time, it can also infer CSAT scores for 100% of your customer calls. The result? A much more representative sample size for CSAT scores, and a more accurate understanding of how satisfied your customers really are:

More productive agents

As we’ve seen, AI can give agents live coaching as they’re talking to customers. Whereas agents would’ve had to manually search an FAQ or Help Center to answer tricky questions before, AI can now surface those answers for them, instantly.

Not only that, AI can also help reduce after-call work (ACW) for agents by suggesting dispositions, as an example.

A better customer experience

This also ties into a better experience for customers, since they can get answers to their questions more quickly, and without having to constantly repeat their issue or question.

With a good omnichannel contact center platform that’s integrated with your CRM, this should pull up contact details automatically for agents so they have a full view of that relationship history on the call. Dialpad’s Salesforce integration, for instance, even pulls in real-time assists for agents right into the CRM so they don’t have to switch between tabs or windows:

Time and cost savings

Finally, all of these benefits of AI ultimately feed into overall cost and time savings.

Agents can spend more of their time and energy on high-value work like talking to customers, supervisors can QA calls more quickly… These automations lead to saved time, which translates into savings when it comes to staffing and fewer resources needed to do manual work like listening to call recordings and reviewing training materials because AI can suggest ways to enhance your customer resources.

Want to start analyzing your phone calls with AI?

If your team is already having conversations with customers on the phone every day, you’re sitting on a goldmine of customer intelligence that isn’t being used.

The thing is, getting started isn’t particularly difficult. Once you have an AI communications solution, it’s just a matter of starting to collect that real-time conversation intelligence. The most important thing to do is to determine what exactly you want to analyze with AI, given how many possibilities there are.

If you’re interested in seeing how other companies are analyzing their phone calls with AI, or how this works in a live demo, reach out to our team for a walkthrough!