How customer sentiment analysis improves the customer experience
Companies are continually looking for ways to elevate their customer experience. Learn why customer sentiment analysis could be the key.
Last updated January 30, 2023
There are three key pieces to the customer experience puzzle:
A customer-first approach, a clear understanding of the voice of the customer (VOC), and a company-wide determination to excel.
In this article, we’ll explore how customer sentiment analysis helps boost the customer experience by elevating the second piece—VOC. You’ll come away knowing everything you need to get started with customer sentiment analysis and to provide an excellent experience for your customers.
- What is customer sentiment analysis?
- Why is customer sentiment analysis important?
- Why customer service data is crucial to understanding customer sentiments
What is customer sentiment analysis?
When your customers leave feedback, comments, reviews, or complaints, you learn what they like about your company, what they dislike about it, or what they would want to see improve.
Customer sentiment analysis is a vehicle that captures and analyzes these data points and presents them to you in the form of customer insights.
Essentially, customer sentiment analysis is the process of understanding how your buyers feel about their experience with your product, service, or brand.
Customer sentiment analysis is often automated, especially for mid-market companies and above, because it involves scanning through thousands of data points in the form of customer reviews, surveys, support conversations, social media comments, and so on. Analyzing such high volumes of data manually is not scalable, and it is often impossible.
Why is customer sentiment analysis important?
There are countless applications of customer sentiment analysis, such as understanding drivers of CSAT and NPS® survey scores, improving customer retention, or evaluating brand perception.
We will touch on three major ways customer sentiment analysis can enhance the customer experience.
1. Improves customer service
Bad customer service is often a deal-breaker these days, prompting support leaders to determine where they can improve and how to consistently provide high-quality support.
With customer sentiment analysis, you can understand exactly which parts of the customer service experience are frustrating or delighting your customers.
For example, if you discover that long response time is the number one reason why customers are having a bad support experience, you can take action to reduce it.
2. Informs product roadmap
By analyzing customer sentiments, you can assess how your product shapes up. There are three main product areas where you can utilize customer sentiment analysis:
- Quickly identifying and fixing product bugs
- Uncovering product areas that are sub-optimal
- Identifying new features your customers need
For example, if customers are unable to complete a task due to a product bug, you’d want to spot it fast, get to the root of the issue, and solve it swiftly before it affects more users.
Customer sentiment analysis helps you do this by identifying tickets relating to bugs in real time and alerting users with a tech skill set. It eliminates the need for manual logs to monitor particular issues, as this will be automatically available within your analytics dashboard—making it easier for your tech team to deep dive and find details about the issue.
3. Enables confident decision-making
Your customer service team may know which operational issues are plaguing your customers and therefore which ones need immediate attention. But without data, it is difficult for them to present these insights with confidence and for other teams to make assured decisions.
In such a scenario, customer sentiment analysis can provide the data needed, pointing you to the exact pitfalls that are negatively affecting the customer experience.
Imagine you’re an ecommerce company with a new delivery partner, and your data tells you that shoppers’ overall experience has been bad recently due to late deliveries. With this knowledge, you can communicate the issue to your operations team and make data-driven decisions on ways to solve it.
Why customer service data is crucial to understanding customer sentiments
It’s important to establish a holistic, democratized voice of the customer program that is accessible to everyone in the company. The program should analyze customer sentiments captured in customer support interactions, such as customer surveys, reviews, chats, emails, and calls. It should also include product usage data, interviews, and social media.
Take Gousto, for example. The UK-based meal kit retailer unified nine VoC channels and brought all its customer sentiment insights onto a single platform that all teams could access and use. As a result, the company was able to obtain an objective understanding of customer sentiment, remedy problems across the company, and boost its customer experience.
So, be sure to include customer support data while analyzing customer sentiments. Here are three reasons why.
Benefits of using customer support conversations in customer sentiment analysis
Provide more proactive support
Customers often raise tickets or reach out to customer support when they’re facing a problem. Between the phone calls, emails, and chats you receive, you will have real-time data that allows you to spot recurring issues quickly and fix them before they become unmanageable.
Contrast this with NPS® surveys, which are done periodically. It might be days or weeks before you become aware of the common issues that are causing a bad customer experience. As a result, you can’t take immediate action, which can lead to low satisfaction scores or increased customer churn.
The biggest difference between customer support conversations and other data sources is that customers willingly share their feedback and pain points—you don’t need to ask for them, so there is zero possibility of introducing biases in the data.
But with other customer feedback sources, such as customer interviews, you need to directly ask for their opinion, which can have some shortcomings.
For instance, customers only answer questions that are asked, like “Can you explain why you do not like the login page?” which are often presented by teams out of half-baked hypotheses. Even if the real issue is hidden away on the next screen, customers would not mention it without being asked.
One of the main advantages of customer support data is that it is all-encompassing. It consists of customer interactions from all customer groups throughout their customer journey—from the time they hear about your product until they make a purchase or leave.
This means customer support conversations can give you an overarching array of insights for you to dive into.
Suppose you find that customers are having a negative experience on your mobile app, leading to an overall bad customer experience. You may have gained this insight from anywhere—a user review, customer survey, support call, or customer sentiment analysis—but a bad app experience can be due to several reasons.
It’s only when you conduct granular, root-cause-level analysis that you figure out the one thing that’s creating a poor app experience. Then, you can create a plan to fix the problem.
Leverage the power of customer sentiment analysis
In this article, we explored customer sentiment analysis, why it’s important to CX, and how using customer support conversations to analyze sentiments can give you the best customer insights.
If you’re ready to take your customer experience to the next level by utilizing the power of customer sentiment analysis, check out SentiSum. SentiSum is an AI-based tagging and reporting platform for understanding customer trends and sentiments.
Learn how the Zendesk + SentiSum integration is helping companies like Hotjar, Gousto, and Ticketmaster analyze their customer sentiments, obtain granular customer insights, and improve the customer experience.