Call transcripts from recorded customer conversations, meetings with prospective customers, research interviews, or even conference calls are a great source of insights. Whatever your role, there’s a good chance you’d find it valuable to be able to identify patterns and themes from calls at scale.
Whether you’re a customer support lead, product manager, marketer, sales manager, or C-suite executive, a great way to become more customer centric is to tap into insights found in your call transcripts.
There are valuable insights to be found in:
We’ll elaborate on just the first three to cover customer feedback analysis. That said, the same principles apply to conference calls and earning calls analysis.
Customer support calls. If you’re a customer support lead, you might be tasked with reviewing call transcripts from customer support inquiries. Insights and patterns from support calls can help you:
Sales conversations with prospects. Imagine you’re a product manager or marketer and you would really like to understand a segment of the market that your company is not serving very well yet. You know you get a lot of inbound inquiries and your sales counterparts talk to this segment regularly but many of those end up as Closed Lost opportunities.
Still, the call transcripts exist. Why not tap into these transcripts to dig into why that segment of the market reached out but didn’t end up buying? It might help you improve your product or positioning to better reach that segment of the market.
In other words, sales conversations can be a great source for information for product management teams in particular to:
Customer meetings. Whether you’re a UX researcher, product manager, or customer experience professional, you’re always looking for ways to improve the experience for users of your product. The more information you have from them, the better. Recorded conversations are some of the richest sources of insights about who you’re serving. Recorded and transcribed customer conversations are valuable for:
Many tools exist that facilitate call recording and transcription. Tools such as:
There are many more. A lot of these apps offer more than just call recording and transcribing. Some offer basic analytics, coaching modules, pipeline analysis, or integrations with other apps.
You probably don’t have the time or resources to review hundreds of calls consistently. You might be able to review a handful of call recordings in depth on a regular basis. But relying on just a handful of calls means you’re missing out on the majority of what your customers are saying on these calls.
Most calls don’t get analyzed in depth for patterns, ideas, or insights because it’s time consuming, manual work to parse out the transcribed text from calls. This is the same challenge companies face with other qualitative datasets when doing customer feedback analysis.
What makes call transcripts more challenging for analysis than other qualitative datasets is that 1) it’s not always clear who’s talking 2) a speaker often speaks for a period of time longer than most written formats and 3) what’s being spoken isn’t always accurately transcribed into text. A lot of cleanup has to be done at the transcript level before analysis can be done.
Most analytics tools specifically designed to analyze call transcripts stop short at delivering surface-level analytics about the call format. They don’t deliver sufficiently meaningful insights about the content of the conversations in an accessible way.
Other analytics tools, such as text analytics tools, might give you sentiment analysis and keyword or keyphrase categorization on the content of the calls. That still doesn’t address the problem of understanding what customers complain about most, their top requests, or the top questions they have during their call interactions. Someone still has to go through all those keywords and try to make sense of the context.
Call transcripts analysis should allow you to treat transcribed text like any other data set. As a dataset, call transcripts can be analyzed for patterns about top friction points for customers and prospects, ideas for positioning and messaging, competitive intelligence, and more.
Unlike quantitative data, however, you should look for solutions that automatically tell you the story of what customers or prospects are saying; ideally it's in a format that anyone on your team can grasp (such as plain, natural language). For example, don’t consider sentiment analysis alone. Sentiment analysis will tell you whether a statement is positive, negative or neutral but it won’t tell much more than that.
Instead, look to analyze call transcripts (or any other qualitative data source) in a way that gives you the greatest amount of context. For instance, chances are that customer statements fall into one of these feedback types: complaint, compliment, question, or request. These are the four most common types of feedback from customers and provide more context than sentiment analysis.
Look for as much context as possible from recorded conversations in an accessible format. Instead of classifying portions of call transcripts by keywords or key phrases, get your insights delivered as summarized analysis of themes.
Let’s say customer cancellations are a common theme in customer call transcripts. A summarized analysis of that theme might look like this:
Customers are cancelling their accounts because they don't like the products, they don't need them any more, or because of poor service. Customer support agents are working to retain customers by offering discounts and other incentives, but many customers refuse these offers.
To apply this at scale across hundreds or thousands of calls consistently requires solutions that can go beyond analytics and focus instead on giving you true call transcript analysis.
If you’ve got hundreds or thousands of call transcripts you’d like to analyze, we at Viable can help. Reach out to us for a conversation to explore your call transcripts use case.
Reach out to explore your use case.