What is conversation analytics?
Conversation analytics is the process of evaluating customer conversations. This can cover customer interactions such as telephone or chat conversations with your call center, social conversations, third-party reviews, and more.
How does it work?
Conversational intelligence (or conversational data) is gathered through the use of sophisticated artificial intelligence (AI), machine learning, natural language processing, and algorithms.
This technology is applied to transcribe phone calls and chats, review posts, and more to get insights into customer behavior. It analyzes these conversations for customer sentiment and finds patterns that can be useful for a deep understanding of customer behavior.
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What type of feedback does conversation analytics cover?
When brands think about gathering feedback, surveys are often the first tool to come to mind. While surveys can be incredibly useful, they only cover explicit feedback, where brands ask a specific question in return for a score.
Implicit feedback covers everything else – and this is where conversation analytics comes in. Implicit feedback provides clues to how customers think, feel, and how they behave.
The feedback brands gather can be divided into four types, with crossover:
Structured, solicited data covers feedback you gather from CSAT, NPS, CES, and more. This is very useful for developing benchmarks and getting top-level information about how your brand is performing.
Structured, unsolicited data encompasses operational data. You don’t ask your customers for this information, but it was gathered internally during the course of your customer interactions.
Unstructured, solicited data includes text comments and social responses that you receive through your surveys and social posts.
Unstructured, unsolicited data is where conversation analytics comes into play. This type of feedback covers the difficult-to-gather information, such as:
- Social mentions: When your customers mention you on social media but aren’t directly talking to you, what are they saying?
- Customer phone calls: The exact wording of your customers’ discussions with your customer service agents. Speech analytics can even go as deep as non-verbal analysis (how fast do customers speak, how often they interrupt, the tone and volume of their voice, and more)
- Chatbot conversations: How your customers interact with your brands’ chatbots
- Chat conversations: Your customers’ interactions with your human customer service agents
- Third-party reviews: When customers talk about your brand in posts on third-party review sites, how do they portray you?
This conversation data can be very useful for understanding customers’ true sentiments and the drivers behind them. It covers the aspects of your customer experience that you don’t think to ask about, but ones that feature on your customers’ minds. Unless you gather unstructured, unsolicited data, you might not be getting the whole picture.
Why is it important to analyze customer conversations?
Analyzing conversations helps you to get the nuanced story
With over 50% of customers across all ages using their phones to reach out to a customer service contact center, analyzing speech is vital for getting a comprehensive view. Your solicited, structured data can only give you snapshots into customer behavior and sentiment – with real-time conversation analytics, you can identify patterns forming and take action.
You reduce the skew on your feedback
Only a certain proportion of customers will ever respond to your requests for feedback because even for a simple rating request, there is an element of customer effort.
Those customers that do respond will be highly motivated to do so, either because of a great experience or a negative one. As a consequence, the results you gather through solicited customer feedback are likely to be skewed to the extremes. Weighting this feedback is one solution – but it won’t be as accurate as getting more information.
It’s the customers’ own words
Solicited, unstructured feedback such as open text comments are useful, but it’s difficult to analyze them accurately for sentiment due to their brevity. There’s an additional cognitive load for customers to think about what they want to say, which can be off-putting.
It’s best practice to get the most information you can
Customer experience best practice involves using a blend of all types of feedback, allowing you to see not only what customers are saying, but what they’re not saying as well.
You reduce customer burden and improve the customer experience
The best part of conversation analytics is that there’s no extra burden on the customer. All the information you glean, you gain through conversations the customer themselves has initiated – and you’re not asking them to interrupt their customer journey to give it to you.
Conversation analytics use cases
The conversation data you gather with this type of analytics can help you gain insights that are actionable, which can help you improve your metrics and propel your business forward.
Below are some use cases for conversational analytics.
Trialing products and services
Rather than hoping to get feedback on products and services after committing a large spend on a new launch, you can try creating a small test sample with conversational analytics applied to monitor feedback.
A small sample size means you’re unlikely to get enough customer engagement for solicited feedback to understand your success. However, by using conversation analytics, you can gather data you might have lost otherwise. You can save money on a potentially disastrous rollout – and get a good grasp of what customers will respond well to.
Getting accurate feedback and finding root causes
As mentioned, customers might not be telling you the whole story – or not telling you a story at all.
For example, your CSAT scores might be doing well – but maybe you’re not seeing customers come back to purchase more. Conversational analytics can help you narrow down the causes of discrepancies, creating connections between what customers are actually saying (to your customer service agents, on review sites, on social media) and their behavior.
In this case, it could be that your products are great and customers are satisfied – but your payment process is too complicated, and it leads customers to contact your agents. You won’t know unless you get as much information as you can.
Predicting future behavior
Using metrics is useful for understanding the here and now – how customers feel about specific interactions, for example. However, when it comes to predicting future behavior, you might need conversational analytics to drive business decisions.
For example, say you’re planning on launching a new product – and as far as you’re aware, the last product was well-received and conversion rates have been good. However, a user has spotted an uncommon issue with your existing product that you weren’t aware of – and this issue will be part of a big feature in your new product.
Without social listening, you wouldn’t know that this user’s observation has spread like wildfire across social media, putting off potential buyers for your new product. You wouldn’t be able to tackle the problem, because it hasn’t cropped up often enough in your customer service tickets for you to notice – and as far as you’re aware, your existing customers are satisfied.
Instead of finding out the hard way that your marketing and sales team will have a hard time getting leads, you can keep an eye on feedback that’s not given directly to you and make changes before disasters happen.
Improving customer service
Your customer service agents can always benefit from coaching – and conversational analytics can help you to train staff to respond more effectively to calls.
For example, say your customer service employees are struggling to de-escalate difficult conversations before they become heated. By using conversational analysis, you can find out if there are certain triggers for customer ire, or if there are signs that a customer might not be ready to cool off.
These insights can then be passed along to your employees through training, improving your customer service, and customer experience.
Conversation analytics case study: JetBlue
Example 1: Airline JetBlue’s promotional marketing declared that it offered free baggage – but after doing research using Qualtrics’ solutions with conversation analytics, it found that 82% of their passengers did not consider this offer as a reason to fly with them. As a result, the company rolled out different pricing options which were much better received.
Example 2: Conversational analysis also flagged to JetBlue that a specific airport gate was the subject of negative commentary alongside a lower NPS score. After investigation, the issue turned out to be a simple speaker fault, meaning passengers couldn’t hear announcements. An automated alert was set up to ensure fixes were made swiftly – and the NPS score improved.
Though the individual changes to how JetBlue operates might seem small and distinct, the overall cumulative customer experience results are significant. As demonstrated by this case study, conversation analytics can find the simple tweaks needed to radically change customer opinion.
Turn conversation data into insights with XM Dscvr
Even with the best will in the world, your employees can only report so much in real-time. With sophisticated conversation analytics technology, you can monitor customer interactions (solicited or unsolicited) all at once. You can also overlay your data with other metrics and information that you gather to create an accurate, live picture of how your customers feel and think.
Sophisticated conversation analytics technology adds a new element to customer experience solutions. Discover it for yourself now.