When you understand your customer’s feelings and emotions towards your brand, you have the power to make changes that are more likely to influence their behavior towards your brand. With customer sentiment analysis, you’ll get data-backed insights into your customers’ minds, so you can make customer-centric business decisions aimed at increasing loyalty and, ultimately, company revenue.
How can sentiment analysis be used to improve customer experience? We’ll go in-depth into this topic and share the metrics you can use to measure the success and evolution of your customer happiness and satisfaction.
- What is sentiment analysis?
- Collecting customer feedback
- Measuring customer sentiment using machine learning
- Benefits of sentiment analysis for businesses
- What are some examples of sentiment analysis tools?
- Create a proactive customer service strategy
- Monitoring social media and customer interactions
- Understanding sentiment differences across channels
- Using AI-driven text analysis to measure customer sentiment effectively
What is sentiment analysis?
Sentiment analysis in customer feedback includes analyzing written or verbal content to understand the intent or sentiment behind the words. Sentiments are categorized as negative, neutral, or positive (or varying levels in between). It involves looking beyond the words and attempting to understand the emotion or intent behind them.
In business, sentiment analysis helps you discover if customers are satisfied or dissatisfied with your offerings or brand. Insights from this analysis can be used to suggest changes to your business operations, processes, products, or customer service to increase customer satisfaction.
👉Read more about sentiment analysis in our comprehensive guide.
Is sentiment analysis also called “topic modeling?”
No. Sentiment analysis and topic modeling are two different text analysis methods. Sentiment analysis looks at the tone and customer attitudes behind the words. In contrast, topic modeling creates word clusters based on the most commonly used words and not the emotion behind them.
Only basic intelligence is required for topic modeling, but advanced human or artificial intelligence is needed to perform sentiment analysis as you’re analyzing the intent of the words, not the word choices themselves.
This is where AI-based software like Idiomatic can do a much better job of reading “between the lines” of customer feedback, compared to manual human analysis, especially for large amounts of data. (More on this below).
Collecting customer feedback
When doing a comprehensive sentiment analysis from customer feedback, gathering data from various sources is essential. This helps you obtain more accurate and statistically representative customer data from multiple perspectives.
For example, if you only perform a sentiment analysis on your help desk tickets, you’re likely to see a negative sentiment bias because people who contact the help desk are usually dissatisfied. For example, analyzing 150 help desk tickets with a negative customer sentiment doesn’t consider the thousands of other customers who haven’t contacted the help desk.
For this reason, always collect feedback from various stages of the customer journey and various customer actions and events. These events and actions may include:
- Post-purchase surveys
- Subscription renewal surveys
- Social media
- Help desk tickets
- Customer service call recordings
- Chatbot transcripts
Measuring customer sentiment using machine learning
An easy and accurate way to measure customer sentiment consists of using machine learning to analyze all your customer interactions across various feedback sources (More on this below). The machine learning algorithm assigns values to specific words and phrases ranging from positive to negative, and the overall score allows you to understand your customers’ general sentiment.
Measuring customer sentiment plays a key role in triaging tickets or acting swiftly when things are heating up. For example, an especially irate customer might be triaged to more experienced support staff, while high negative sentiment scores might point to a fundamental problem with your services or products.
Benefits of sentiment analysis for businesses
Sentiment analysis has many benefits for your business and your customers:
Improving product and service offering
A significant benefit of creating a customer sentiment analysis process is learning insightful information about your customers’ thoughts about your product and service offerings. This real-world information can teach you more about how customers interact with your brand and offerings so you can make changes or launch new products that better meet their needs or preferences.
Here are some examples of customer emotions and insights you can learn from your customers:
- Your customers may identify frustration due to a gap in your service offering. Based on this, you could create a new product or service to meet that need.
- You may notice an increase in customers contacting the help desk for help finding a resource on your newly designed website. Based on this, you may need to redesign your website’s navigation to make this content easier to find.
- Customers may leave comments about your products or features on social media. Based on these comments you can learn which features your customers most care about.
Personalizing customer communication
Did you know that companies that use effective customer personalization have the potential to generate 40% more revenue? You can learn more about your customers by analyzing customer feedback and sentiment to help create more personalization opportunities. By understanding your customer’s emotions, you can learn what drives them to buy or interact with your brand, allowing you to design marketing campaigns and business practices that are driven by your customers’ emotional habits.
Solving customer service cases faster and reducing help desk backlog
By understanding your customers, you can proactively address any common struggles or bugs early so that they can be fixed and reduce help desk calls. You can triage customer struggles with overwhelmingly negative sentiment for urgent bug fixes. With reduced preventable help desk calls, your team can solve backlogs and delays in providing timely support.
The key to identifying customer struggles right away is to get alerts when there is an upwards trend in negative feedback. Idiomatic can send you instant notification of negative feedback trends so you can act quickly to make changes or fixes before they impact your brand trust.
What are some examples of sentiment analysis tools?
Customer sentiment analysis is easier (and more accurate) when you use online tools or software to help you categorize and understand the data. Here are some sentiment analysis tools you could use:
Idiomatic helps you classify and analyze millions of customer comments from multiple sources in minutes. It’s used by large national and international brands like FabFitFun, Instacart, and Pinterest to get actionable, meaningful customer insights to boost customer satisfaction and positive sentiment scores.
This customer sentiment tool combines user issue and sentiment analysis to show you the problems and customer behaviors that are contributing to negative customer experiences. It can also alert you to real-time changes in sentiment so you can take action quickly, whether it means fixing a minor code bug or contacting a customer directly to solve their problem.
Talkwalker helps you conduct customer sentiment analysis in social media. This software analyzes comments and engagement on social media websites so you can understand how happy your customers are.
Critical Mention analyzes news, publications, and TV for mentions of your business. This software is excellent at media monitoring and gathering mentions across various sources like social media, broadcasting, online news and podcasts.
Hubspot’s Service Hub
Hubspot is one of the leading CRM platforms, acting as a one-stop shop for marketing, sales, and content, and customer service departments. With Hubspot, you can conduct sentiment analysis on qualitative survey data. It integrates directly with their full suite of cloud-based marketing and sales tools.
Rosette uses multilingual sentiment analysis in over 30 different languages. This tool can be helpful if your business is international with customers who speak languages other than English.
You can also import your customer feedback data into an excel spreadsheet for manual sentiment analysis. This is much more time consuming and we don’t recommend this if you have large amounts of uncategorized data.
Create a proactive customer service strategy
Your business should maintain a proactive customer service experience. This means identifying customer service issues early and proactively working to resolve them before they negatively impact how your customers feel about your brand.
Creating a proactive customer service strategy involves the following:
- Collecting customer feedback from a variety of sources
- Analyzing customer feedback for sentiment and actionable insights
- Acting on insights to improve the customer experience
- Creating a cyclical process of feedback collection, analysis, acting on insights, and repeating until customer satisfaction is achieved.
Your proactive customer service strategy should also include setting up alerts to get instant notifications of changes in customer sentiment early before they affect a large portion of your customers.
Idiomatic constantly reviews customer feedback and will alert you if sentiment is trending negatively. Customers using Idiomatic have seen a 40% reduction in top customer issues, a 49% decrease in customer complaints, or a 35% increase in positive CSAT scores.
Two other potentially rich sources of customer feedback for customer sentiment analysis are social media and live customer support interactions. Both are good ways to understand how customers feel about your brand.
Many customers go on social media to talk about their experience with your brand. Some will tag your company’s social media handles or use a branded hashtag. These can be monitored using social media monitoring software and used in customer sentiment analysis. Using AI, you can more accurately sort and categorize the user intent of the social comment (including sarcasm or hyperbole, both commonly used in social media posts).
Your organization can also save recordings and transcripts from customer service conversations. These are valuable sources of detailed information about your customers and their struggles. With a chat transcript, you can discover the problems and often the root causes and details of their struggle, including how they tried to solve the problem or other factors that affected the issue or resolution.
When you use voice of customer feedback from social media and customer support conversations, you can identify areas for improvement so you can take steps to enhance customer satisfaction.
Understanding sentiment differences across channels
When reviewing customer sentiment, it’s essential to understand the sentiment differences in different channels. The underlying base sentiment will be quite different in a customer help desk conversation (likely more negative) compared to a social media post (that could be overly exaggerated towards the positive or negative). Understanding the biases in tone and sentiment between the channels can help set more realistic expectations in sentiment analysis.
Take a help desk conversation, for example. The underlying reason someone contacts your helpdesk is frustration. Rarely (if at all) will someone contact a help desk to praise a product. This means you can expect an overly negative sentiment from this channel.
On the other hand, a satisfaction survey sent to a customer after their initial help desk conversation should be more positive because you expect that your employee was able to solve their problem. You should aim for a higher percentage of positive sentiments from this channel (and if not, dig deeper to see why these customers are still unhappy).
Using AI-driven text analysis to measure customer sentiment effectively
Successful customer sentiment analysis has three phases: data collection, analysis, and action. This process is repeated to ensure any changes you make have the desired effect. But, sentiment analysis is just one metric that helps you measure and understand your customers better.
Using Idiomatic, you can import customer feedback, including customer survey and conversation data (through integrations). Idiomatic’s AI-driven platform will look for real-time trends in your customer feedback data, sensitive to the differences in typical sentiments in each channel. It can then notify you of any potentially alarming trends and give you the valuable customer sentiment insights you need to make the changes your customers need.
You can classify millions of customer comments and feedback in minutes to better understand customer sentiments. Idiomatic helps you unlock the “why” behind customer feedback using AI that’s customized to your business.
Request a demo today to learn how Idiomatic can help you supercharge your customer insights with a sentiment analysis.