As a business, you collect lots of feedback from your customers and even employees to learn how to improve your products, services, and operations. When it comes to analyzing this feedback, being able to parse qualitative data like reviews, surveys, and support tickets and uncover the overall sentiment in them is key to making big impacts at your organization.
Sentiment analysis helps you gauge the reputation of your brand and your customer experience, so you can make smarter business decisions. Of course, doing it well requires a bit of industry know-how. That’s what we’re here for!
Jump right into our beginner’s guide to learn:
- What is sentiment analysis?
- Why does sentiment analysis matter?
- What are the use cases for sentiment analysis?
- How do you approach sentiment analysis?
- What’s different about Keatext’s sentiment analysis?
1. What is sentiment analysis?
A sentiment analysis algorithm is based on a method called text analytics, which you can learn more about in our 101 guide to text analytics. In basic terms, it uses natural language processing (NLP), deep learning, and machine learning AI techniques to determine whether a comment is:
- Positive or negative
- Satisfied or unsatisfied
- A question or a suggestion
- Low priority or very urgent
We can define sentiment and apply these definitions in a lot of ways, but it all comes down to a simple truth: how customers feel. That is like liquid gold for your business – customer-centric organizations know this very well, and leverage AI solutions to ensure that customers have a seat at the table.
Behind the scenes, the best sentiment analysis solutions do a lot of work to present results in a practical way for these companies. Metrics like brand sentiment score or customer sentiment score are incredibly useful as quantitative ways to track and report on brand reputation and customer experience.
[Further reading: How does sentiment analysis work?]
2. Why does sentiment analysis matter?
Sentiment analysis is critical to business performance in a world turned digital. Customer feedback is easily accessible to companies of all sizes, and customers have high expectations for not only the quality of products but the interactions they have with brands.
We can define sentiment and apply these definitions in a lot of ways, but it all comes down to a simple truth: how customers feel. That is like liquid gold for your business.
Whether that’s through your help desk agents’ A+ service or a quick response to a negative review, customers value how they feel with your brand. Here’s what you can gain from implementing sentiment analysis at your organization:
- Higher quality of insight: Manually looking through a small sample size of customer feedback doesn’t match the level of insight that AI can give you from 100% of your customer data
- Powerful recommendations: Solutions like Keatext give you AI-based recommendations for top action items that have the greatest impact on customer experience
- Sentiment score metrics: You can supercharge your reporting when you put a number to customer sentiment trends over time or after making key product decisions
Of course, sentiment analysis has operational benefits as well:
- Speed: You can process qualitative customer data in a matter of minutes – in fact, 98% faster than through manual analysis
- Scalability: Large volumes of customer feedback are no problem for AI and even lead to better quality of insights
- Consistency: Leveraging AI yields the same analysis each time you process new data, avoiding the human error and bias that comes from multiple people working on manual analysis separately
- Setup (or lack thereof!): Not all, but the best sentiment analysis platforms are ready to go out of the box, requiring no complex training for new industries or use cases – AI is just that good!
- Integrations: Great solutions in this space provide direct connectivity to your tech stack like Surveymonkey and Zendesk
All this to say, there’s a lot of good that can come out of this approach to customer feedback analysis Next, let’s see in more detail how sentiment analysis can bring insights from your entire customer journey, across multiple channels and customer touchpoints, into one platform.
[Case study: How BRP turned 10 years of surveys into insights]
3. What are the use cases for sentiment analysis?
Your customer journey spans a series of interactions that define your brand in the eyes of your customer. In these interactions, customers will often provide feedback in some form or another, or talk directly to a representative at your company.
Sentiment analysis can bring insights from your entire customer journey, across multiple channels and customer touchpoints, into one platform.
Sentiment analysis has applications across the customer journey for distinct channels where customers engage with your brand. Understanding how to map emotion in your customer journey provides the critical context behind each stage of the customer’s path to purchase and brand loyalty.
- Reviews: This is the marketing and brand reputation use case. Online reviews are everywhere, and customers base purchase decisions on reviews like never before. Understanding their pain points in their own words contributes directly to brand health when you can act quickly to resolve issues.
- Customer surveys: This use case takes quantitative metrics like NPS or CSAT and connects it to qualitative customer feedback analysis. Understanding the “why” behind customers’ scores and responses with NPS analysis is worth so much more to your customer success team than the numbers alone.
- Employee surveys: Tapping into the voice of the employee is a big HR use case that enables businesses to improve corporate culture and see top trends in employee feedback. Tracking employee engagement through sentiment reveals the real weight of corporate decisions – whether it’s negative or positive reactions.
- Support tickets: Operations and insight go hand in hand in this use case. Sentiment analysis can help to better categorize tickets in your help desk for smoother experiences and faster responses, especially when you integrate your help desk. At the same time, it provides a high quality of insight into what issues customers are facing and the urgency with which your company needs to address them. Even further, another layer of insight is agent performance – support ticket conversations go two ways, and businesses can analyze their teams’ interactions with customers to help them succeed.
Leaving an honest review on a site like Amazon, sending you feedback about shipping in a post-purchase survey, or finding a solution to a product malfunctioning on your help desk – these are how customers truly interact with your brand. Sentiment analysis lets you tap into these interactions for incredible insights.
[Further reading: 4 industry applications of customer experience analysis]
4. How do you approach sentiment analysis?
Customer sentiment analysis is a very advanced field nowadays, and while “AI” often seems like a buzzword in tech, it makes for some very powerful and effective business results.
Working with unstructured data, it’s important to try to cover every potential circumstance in your data. While we would love it if this were the case, customers don’t always say exactly what they mean. Using sarcasm, irony, slang, and even emojis make it understandably difficult for a machine to pick up on this nuance.
Leaving an honest review, sending feedback in a survey, or finding a solution on your help desk – these are how customers truly interact with your brand. Sentiment analysis lets you tap into these interactions for incredible insights.
Thankfully, natural language processing has come a long way to the complexity achieved today. Automation is now the industry standard for sentiment analysis, whereby a machine learning algorithm is trained in a number of ways with different inputs, so that it identifies the correct sentiment for each comment. Over time, the algorithm picks up on specific features of the input that indicate the resulting sentiment.
[Related: Top 5 insights you can learn from sentiment analysis]
5. What’s different about Keatext’s sentiment analysis?
Our solution is built on an AI model that requires no training for your specific use case or industry. No dictionaries or lexicons needed here! Keatext works out of the box thanks to cutting edge NLP algorithms, so you can instantly identify customers’ positive and negative feedback, objections, and questions in real time.
This means that whether you’re looking at surveys, reviews, or support tickets, Keatext provides consistent and AI-backed recommendations for action items you can prioritize that have the most impact on your customer satisfaction. On our customizable dashboard, you can even benchmark and measure your progress with an overall sentiment score to connect your sentiment analysis insights directly to ROI and improve your bottom line.
