Conversation analytics: How to turn conversations into insights

A small landscaping business increased their marketing spend on their best channels. Now their lead pipeline is fuller than it’s ever been. It’s their dream scenario, but there’s just one problem: those leads aren’t actually converting. 

Okay, two problems: they also have no idea why. Sound familiar? 

A lot of small and growing businesses struggle with similar situations. Something isn’t working, but you can’t put your finger on what. You feel like you’re flying blind. And you don’t have a system to understand why any of it’s happening.

The trick isn’t to invest in fancy enterprise data tracking software or hire an analyst. In fact, you likely already have all the data you need. What you actually need is a simple way to tap into it: Conversation analytics.

In this article, we’ll break down what conversation analytics is, why it matters, and how to do it — simply — for a small and growing team. No fancy software, no expensive analysts needed, but you still get all the benefits. 

What is conversation analytics? 

Conversation analytics uses artificial intelligence and natural language processing to analyze customer interactions. It looks at context, content, intent, and sentiment.

Basically, instead of manually listening back to calls or reading through transcripts, AI does it for you at scale. Then it surfaces insights like what people are asking, what’s frustrating them, where deals are stalling, and why.

Once you know this, you can take steps to improve sales, retention, and the customer experience.

The best news of all: these insights are likely available from data you already have. Calls and texts from your business phone, emails, social media, and website chatbots — all of these sources are hiding great insights. All you need to do is create a system to regularly uncover and implement them — and we’ll get into how to do that in a bit. 

What do growing businesses use conversational analytics for? 

The goal of conversational analytics is to improve your business performance. 

Here are some ways small businesses use conversational analytics: 

• Understanding and fixing customer frustrations. When the same complaint shows up across 10 different calls, conversational analytics sees that. You don’t have to wait for a bad review — or worse, “random” customer churn — to see what’s wrong. Instead, you can fix frustration early and improve the customer journey from the start.

• Converting more leads. When you can analyze hundreds of sales calls at once, you can see exactly why leads don’t convert. Maybe it’s pricing, or timing, or a competitor or alternative that keeps getting mentioned. Knowing the patterns of sales objections helps your team better prepare. That way, you can convert more leads and improve upselling.

• Fix confusion in sales and support processes. If your conversations involve a lot of back-and-forth, that’s usually a sign that something isn’t landing clearly for the customer. Conversational analytics help you identify where the confusion is coming from. Then you can fix it instead of letting the pattern continue.

• Coaching team members. Maybe patterns in how your team handles difficult conversations aren’t serving you well. Analyze objections, pricing calls, or troubleshooting with conversation analytics. This can help you create better examples and scripts for future call coaching. 

• Improving response times and follow-ups. How quickly does your team respond to inbound messages? At what point in the process do leads go cold? If there’s a pattern, conversational analytics can highlight it so you can improve your speed to lead.

How does conversation analytics work? 

You don’t have to be a machine learning specialist to benefit from conversational analytics. But here are a few supporting technologies to know about:

• Speech-to-text. The program first turns your audio files into text so it can read and analyze them. You’ve probably used this feature while texting on your phone or seen automatic transcripts of recorded phone calls. 

• Natural Language Processing, or NLP. This lets computers read and understand human language, especially the meaning and context behind it. Before, computers could only process structured commands. NLP is what makes it possible for AI to analyze a conversation much like a human would.
  
• Machine learning, or ML. Machine learning is a subset of AI. This is when the computer learns from its dataset to be able to have conversations or do tasks it wasn’t explicitly programmed to do. It also helps AI improve over time.

• Sentiment analysis. A computer program uses sentiment analysis to understand tone and emotion. For example, it might be able to flag whether a customer was frustrated, satisfied, or neutral.
  
• Intent detection. The model uses this to identify what customers want from an interaction. For example, it might flag if a customer wants to make a booking, get information, or process a refund. 

If your head is spinning slightly, let’s put it into perspective with a quick example of how these technologies work in practice: 

A customer calls your business phone number. That call is automatically recorded and transcribed using speech-to-text. The transcript becomes the raw material the AI works with. For example, you might have a program set up, like Quo’s AI call tags. The program uses NLP and sentiment analysis to tag the call by topic of conversation, sentiment, or keyword. 

This way, you can quickly identify and sort ongoing trends in your business based on customer calls. Or you might have call tags set up that use intent detection. This could highlight common support inquiries, allowing your team to take care of them faster.

You can also use an AI model, like Claude or ChatGPT, to evaluate your call transcripts. For example, you could ask, “What were the most common reasons leads didn’t book last month?” The AI model will use NLP and ML to “read” the transcripts and highlight patterns. 

So, now you know how it works. Next, let’s figure out how to make it work for your business needs. 

How to turn your conversations into structured insights 

Remember, the goal of conversational analytics isn’t more information. It’s structured, actionable insights you can use right away to improve. 

We’ll show you how to do that using Quo, an AI-powered business phone platform, as an example of what this looks like in practice. 

1. Know what you’re looking for

Sometimes, the best data lives inside conversations you wouldn’t think to look at. Start by identifying one or a few key levers in your business. What one or two improvements would make the biggest difference right now?  

Then you want to find the signals that correlate with those problems. Here are a few problems, along with what signals to look for: 

If your leads aren’t converting, look for: 

• Sales objections. Is there a pattern of hesitation with pricing, timing, or something else?
• Coaching signals. How does your team handle objections, pricing conversations, or difficult calls? If there’s a pattern of lost leads or poor lead qualification with one of these topics, it can signal a need for more training. 

If you have a lot of customer churn, look for: 

• Competitor mentions. Are customers comparing you to competitors before booking, or as a reason to leave? The context can give you clues to how you’re losing them. 
• Retention signals. What do customers say right before they churn? Patterns of similar frustrations, complaints, and tone shifts can be helpful data for identifying warning signs. You can also look at what makes your most loyal customers stay. Then try to replicate that across the board.

If you’re struggling to improve customer experience, look for: 

• Confusion. If a lot of customers are asking the same follow-up questions, it’s a sign to train your team to address them up front.
• Common frustrations. If there’s a pattern of frustration or complaints about the same topic, identifying that can help you address them before customers churn. 

2. Look at your phone analytics to spot what’s off

But where do you find all this data? The first place to start is with your phone analytics. 

A business phone system like Quo will give you a call analytics dashboard, which is a great entry point for conversational analytics. This dashboard can highlight patterns, anomalies, and trends to keep an eye on or investigate further.

Here’s what to look at: 

• Call outcomes, which is answered vs missed calls or voicemails left vs calls abandoned. When are calls not getting answered? How many customers are abandoning vs leaving a message? High abandonment or missed call rates on specific days are a red lag worth investigating. 

• Talk time distribution, which is how long your calls last. A sudden shift toward short calls can mean that your team is becoming more efficient or that leads are hanging up faster. A shift toward longer calls can mean that leads are becoming more engaged and interested or that there’s confusion in the sales process. Either way is worth investigating. 

• Message volume and unique conversations. Is your reach growing or shrinking over time? Are the same customers reaching out repeatedly? They could be doing that to book more jobs or because something isn’t clear. 

• Call tag trends. If you’re using AI call tags in Quo, you can see how each tag is trending against a selected date range. For example, you might see a negative sentiment tag increasing, which is a red flag to look into.

All of these metrics are best tracked over time. You can use Quo Analytics’ period comparison feature to compare any metric to the same previous period. This way, you have context to understand if you’re seeing a trend or an outlier. 

Once you do spot a trend, you can drill down into any chart to get more detail. For example, if you notice a lot of missed calls every Tuesday, click that bar in the chart to see the actual calls and transcripts. Who called, and why? What inbox did they come to? What time was it? These details can help you get the data you need to find insights and action items. 

3. Use AI to surface insights at scale

As you scale, it’ll get harder to pore over these details individually. You can connect your phone system to AI to analyze calls so you can save time.

This doesn’t require expensive enterprise software. You can connect Quo to Claude via a built-in Claude integration. This lets Claude read your call transcripts and messages and surface insights for you.

Here’s how to set this up: 

1. Open Claude and go to Settings. 
2. Click Customize, Connectors, and Connect your apps. 
3. Search for Quo, then click Connect on the Quo connector. 
4. Sign in to your Quo workspace and authorize Claude to access your Quo data. 

From here, you can ask Claude questions about your Quo data in natural language. For example, you can ask about: 

• Top sales objections and how your team handles them
• Why leads aren’t moving forward
• What competitors are being mentioned and when 
• Themes from unhappy vs happy customers
• What themes come up before customers churn 

What you focus on should stem from your goals from step one. If you’re focused on sales and conversions, ask about objections and competitor mentions. If you’re focused on retention, ask about frustrations and churn signals.

4. Put your conversation analytics on autopilot

Once you start seeing success with your conversation analytics, it’s time to remove the manual effort wherever possible.

Start by creating scheduled Claude tasks. You could set up an automated Claude task to run a recurring analysis of your most important Quo data. Have it run at a set time, then deliver the results directly to you or your team.

You can also integrate Claude with your team’s communication tools, like Gmail, Slack, or Notion. This way, your whole team benefits from seeing the insights. 

For instance, you can set up Claude to share a weekly report of top objections across all sales calls in your sales Slack channel. Then have your sales team review them together each week and workshop better ways to approach them. 

7 Conversation analytics metrics to track

Keep in mind, when it comes to conversation analytics metrics, you don’t have to track everything at once. Go back to your goals. Which are the most important for the outcomes you’re focused on? 

Track those first; you can expand later as you solidify your process.

Here are several more core metrics to track based on various goals:

Core metric	What it is	Track this if
Sentiment trend	Measures whether the overall tone of customer conversations improves or declines week-over-week	You have high churn rates or are dealing with lots of negative reviews
Theme frequency by topic	Measures how often specific topics come up across all conversations	You need help prioritizing your highest volume problems to fix first
Theme trend velocity	Measures how fast a specific topic is growing or shrinking week-over-week	You want to catch new issues early or confirm that your fixes are working
Churn signal rate	Measures how often customers say things that signal they’re about to leave	You’re struggling with retention and want to identify opportunities to follow-up before it’s too late
Competitor mentions volume	Measures how often specific competitors or services come up in conversations	You’re not sure why leads aren’t converting and need more insight into who you’re losing to and why
Talk-to-listen ratio	Measures how much your team talks vs how much the customer talks in a given conversation	You want to improve sales and service quality
Repeat contact rate	Measures how often and how many times the same customer reaches out about the same issue	You want to improve resolution quality and customer experience

5 Challenges of conversation analytics you should know

Before you dive in, make sure you account for the challenges of working with conversational analytics. For example:

• Data quality. This is often the biggest challenge because your analysis is only as good as your data. If calls aren’t recorded consistently or accurately, you can easily draw inaccurate conclusions. Use a business phone system like Quo to automatically record and transcribe every call by default.

• Ambiguity of natural language. AI systems often struggle with accents, idioms, or colloquial language. Make sure you keep a human in the loop of your process to catch possible issues. This is especially important when the AI’s insights seem unclear or confusing.

• Privacy and compliance. Recording calls and handling customer data, especially with AI involved, have legal requirements. Make sure you legally record phone calls by letting customers know the call is being recorded and getting their consent. You should also stay up to date on federal and state regulations and consult a legal professional if in doubt.
  
• Analyzing too much at once. It’s easy to get excited about the possibilities and then try to analyze everything at once. What usually happens in that case is the project is abandoned before it ever gets off the ground. Instead, start with one question, data source, or metric, and grow slowly from there. 

• Not acting on what you find. Analysis is easy; change is hard. For your one question or metric, make changes and take action first before you add more analysis. Then make sure that someone specific is in charge of implementing action items and measuring them so they don’t get lost. 

Quo: The easiest way to get conversation analytics for growing teams

Your conversations are already telling you what customers want, need, and feel. You just need customer intelligence software to help you track and analyze them.

But this is just the starting point. Want to turn what you’re hearing into a repeatable system that tracks trends and drives decisions across your whole business? The next step is to run a Voice of Customer analysis.

If you want to start with your phone system data today, try Quo for free for seven days. You’ll see what’s logged, transcribed, and surfaced every time a customer reaches out. 

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