What is call analytics & how can you make it work for your business?

If you’re reading this, the chances are your business doesn’t already use call analytics technology.

If this is the case, you might be relying on manual processes to monitor and measure the success of your customer communications. Think unwieldy spreadsheets, in-person call monitoring, and post-call scorecards that require hours of listening to recordings for every agent.

These methods may work if you’re only taking a few customer calls a day. But if you don’t, the likelihood is these efforts will take up a lot of your time and offer very little in return.

This is because they can be extremely time-consuming and can’t reliably capture or analyze call information, which results in patchy, untrustworthy data.

With sparse, low-quality data, you’ll struggle to perform effective analyzes, optimize your communications, provide good customer service, and close sales deals quickly.

So, how can you use call analytics to resolve these problems and improve customer communications throughout your business?

What is call analytics?

First, let’s summarize what we mean by the term “call analytics.”

From a business communications perspective, call analytics refers to collecting, measuring, and analyzing call data. Sometimes, you’ll find that call analytics is discussed in a more sales- or marketing-centric way, referring to tracking customer or prospect calls and providing useful attribution data to help establish which sales or marketing campaigns drive the most engagement.

However, this is only one subset of call analytics. Generally, a call analytics definition encompasses calls for other lines of business too.

Some Ai tools, such as Dialpad Ai, allow you to collect, analyze, and act on insights from calls across multiple business functions including—but not limited to—inbound, outbound, sales, and customer support calls.

How does call analytics work?

For marketing-centric activities, call analytics might require call tagging and attribution (so the business knows which campaigns are driving new business).

From a customer support perspective, call analytics includes collecting data such as disposition, account, call purpose, and notes. In the case of call disposition, agents can select the appropriate tag to summarize the category of the call.

Here’s an example of what a post-call form might look like:

Of course, as we stated earlier, some call analytics platforms provide more than just call tagging features. For more complex tasks, these tools capture and analyze data using the power of Ai.

But while Ai can capture numerical data—such as average handler time and the number of abandoned calls—the real value of Ai is its ability to collect and analyze qualitative information.

For example, some Ai contact center platforms can:

• Record calls and transcribe conversations in real-time

• Conduct live sentiment analysis of a customer conversation

• Highlight common objections your sales team hears from prospects

• Identify patterns in customer support calls (like which products customers are complaining about or what they need help with when troubleshooting)

How to deploy call analytics in your business

Now that you have a good understanding of what call analytics is and how it works, let’s check out some practical examples that illustrate how you might embed it in your business.

Analyze and optimize marketing campaigns

Your marketing teams undoubtedly run a number of different campaigns across a variety of channels. While it can be easy to attribute leads and sales to digital channels, the same can’t always be said for traditional phone calls.

If a sales agent doesn’t log the intent of a call—or indeed the reason behind a purchase—it can skew your overall marketing campaign metrics. In other words, a campaign deemed unsuccessful may have actually gained you a handful of leads or customers. (You just weren’t aware of it.)

If you run marketing campaigns where the CTA (call to action) is to call in, then call analytics would be helpful in improving the accuracy of your marketing attribution. This provides a more rounded view of your marketing efforts, which, in turn, creates a better springboard for campaign analysis and optimization.

Understand the voice of your customers

Your customers are providing you with valuable feedback every single day, for free. The problem is those insights are usually buried in hours of call recordings and transcripts that your supervisors don’t have time to listen to or read from beginning to end.

But uncovering and using this feedback isn’t just important for the sake of customer experience. It’s also incredibly helpful for shaping and informing your business strategy and product roadmap.This, in turn, can ensure you resolve any recurring frustrations before they snowball into larger issues.

This is where Ai really shines. It can analyze those call recordings and real-time transcriptions, capture the context of every customer call, and pull out patterns—saving your team hours in the process.

Improve the customer experience

Call recordings and transcriptions contain valuable insights about potential issues in your sales and customer service experience, but you still need a way to filter out those insights efficiently—without having to listen to every single recording from beginning to end.

This is where Ai can help, for example by analyzing those conversations for sentiment.

When you have many customer conversations happening simultaneously, it’s difficult for supervisors to keep an eye on every call. Ai-powered sentiment analysis can help highlight any calls that are dipping into negative sentiment territory and flag them for supervisors. Dialpad Ai, for example, can pick up on keywords (like “I’m frustrated” or “This is great”) to show positive, neutral, and negative sentiment for customer calls in real time:

Shorten sales cycles

This wealth of valuable insights into your customers’ thought processes and preferences also helps your sales team address objections and close deals more effectively. This will go a long way to shortening the sales cycle, as well as saving “at-risk” prospects.

One way to boost sales efficiency with call analytics is to use features like automated quality assurance (QA) scorecards.

Whereas traditional scorecards required supervisors to manually score each agent’s performance, automated or Ai-powered QA scorecards can assess 100% of your agent calls, usually in real time. Dialpad’s Ai Scorecards, for example, can analyze your sales team’s calls against your sales methodology (whether that’s BANT, MEDDIC, or SPICED) and speed up the grading process for managers.

This not only ensures better quality assurance, but also empowers supervisors to better coach struggling team members—ultimately shortening the sales cycles.

The biggest call analytics benefits for businesses

Here are some of the biggest benefits of using call analytics in different areas of your business:

Better ROI for marketing campaigns

Accurate marketing attribution and call tagging will result in more accurate campaign data. As a result, your marketing teams can better calculate the return on investment (ROI) of their current or historic marketing efforts that ask prospects to call in to make a purchase or learn more.

For campaigns that generate a higher ROI, your marketing teams can then assess what went well (and why) and replicate it during future campaigns.

Improved customer satisfaction

With the best call analytics software, you’re not just listening to your customers while they’re on the phone—you’re analyzing their feedback and sentiment post-call, too. This helps your agents and supervisors find patterns in customer communications and continually improve their services.

What’s more, with call tracking data, you can better anticipate peak calling periods and accurately allocate resources. This reduces wait times, which keeps customers happy—and reduces stress for your call agents, too.

Higher conversion rates and customer lifetime value

By using quantitative call analytics features to look at metrics like call times and handle times, alongside qualitative call analytics like conversational insights, you can help both your sales and customer success teams improve their performance.

For example, some sales Ai tools have call analytics features that help sales reps understand the most common objections prospects have, and pain points that make cross-sells and upsells more successful.

Leverage call analytics to boost the productivity of your marketing, customer service, and sales teams

If your agents handle hundreds, if not thousands, of calls each month, then they’ll benefit from having a call analytics solution.

Without this, it can be time-consuming and difficult for your team to take a targeted, tailored approach when they rely on manual call tagging, content gathering, monitoring, and analysis.

There aren’t enough hours in the day to transcribe entire conversations by hand—or crunch the numbers needed to establish peak call times, call lengths, and marketing attribution.

To boost the productivity of your marketing, customer service, and sales teams—without burning them out—why not try an Ai-driven call analytics platform?