Customer Sentiment Analysis: What’s It All About and Why You Need It

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    Uncovering customer perception and decision making is one of the biggest challenges business leaders and customer success teams are facing today. Since reading minds is not possible as of yet, CSMs need to connect the dots manually and resolve dozens of disparate “clues”, things that are becoming increasingly difficult. Enter Customer Sentiment Analysis.

    What is Customer Sentiment?

    Before diving into the specifics of customer sentiment analysis, let’s touch on what sentiment actually is. It’s a unique blend of emotion and opinion. It answers two key questions – how do you feel and what do you think about “something” or “someone”. 

    Customer sentiment analysis - Staircase AI

    So what is that “something” or “someone” when it comes to online businesses? 

    It’s all about the offering or service and what sentiment it’s evoking. Is the customer happy and satisfied with the experience? How well does it work? The most common technique currently being used to gauge sentiment is the Net Promoter Score (NPS) survey. These surveys are easy to conduct, but suffer from inherent shortcomings like low response rates, limited information, beyond a number, it samples a person once in a few months at best and if that person is having a good or bad day that could affect the survey results.. 

    CS teams play a part too. Humans have an innate capability to pick up on various cues like facial expressions, voice tonality, body language, and the context of the communication to understand the customer’s state of mind. But this technique has its limitations. We are often mistaken due to various biases where our assessment or interpretation is skewed to what we want the outcome to be.

    Thanks to AI and other technological advancements, it’s now possible to collect and analyze a wide range of customer signals that can provide a real-time indication of a customer’s sentiment. However, even this often falls short due to lack of context, cultural factors for example, being overly polite even when unhappy, cynicism, regional styles of communication, professional conduct and expression and more . The multiplication of communication channels is an additional challenge.

    This is part of an extensive series of guides about AI technology.

    What is Customer Sentiment Analysis?

    Customer Sentiment Analysis is helping business owners and customer success teams extract customer sentiment  by analyzing conversations, communications, and  meetings. It helps understand how customers are feeling about the company’s product, how the brand is being perceived, the value they are offering, and satisfaction from the service being offered, amongst other things. 

    So how does modern customer sentiment analysis work? 

    This methodology is powered by machine learning and deep-learning models that are trained on large language corpora corpus to understand the nuances of a specific language. Next, these models are re-trained to understand sentiment, but here’s where things get tricky. The models need to be fine-tuned specifically for the field of interest to resemble human understanding and accurately assess and predict the state-of-mind. 

    More often than not, determining customer sentiment is not a conclusive process because different people will give you different answers. That’s why the data harvesting and analysis needs to be accurate  to minimize and eliminate any ambiguity. Furthermore, once the models work they need to be constantly tweaked and retrained whenever mistakes are found. 

    In some cases, several models will need to be deployed to provide a range of sentiment analysis and sensitivity to business situations. These teams will often need to calibrate the models to fit their specific circumstances and use cases.

    Related: Customer Relationship Score

    Relationships Matter: The Role of Customer Sentiment

    Every business to business interaction involves an exchange between the vendor and customer. The customer “hires” the vendor to help solve a core issue or a set of problems (the “value”) and in return is willing to pay for it. In essence, we have two groups of people interacting with each other on a regular basis to realize this specific exchange. But the dynamics are much more complex than it seems.

    We can break it down to two types of relationships:

    • The relationship between individuals. For example, you can have a person from the customer team engaging with a person in the vendor team.
    • The relationship between the companies. Simply put, this is some sort of aggregate of all the individual relationships. 

    It’s common knowledge that strong relationships lead to positive business outcomes – retention and growth. However, building and nurturing relationships is challenging and the essence of what Sales and CS teams try to accomplish on a daily basis. Data from written, spoken, direct, and indirect channels needs to be pieced together to understand how the vendor’s service, product, and brand is viewed.  

    In essence, all vendor customer success teams should be obsessing about their customers’ sentiment. But what exactly is this all about and how do you utilize it in the business world? You guessed right – customer sentiment analysis. 

    Customer Sentiment: Why Do You Need It?

    Sentiment is the closest thing we have to reading customers’ minds and quickly changing course to provide instant value. It is a crucial component of relationship strength which is one of the keys to sustainable business growth.

    Here are some key benefits of implementing customer sentiment analysis:

    • Tracking Trends Over Time – Human emotion isn’t constant. It can vary over time and require CSMs to tweak their playbooks. Even if you manage to gauge customer sentiment manually, there’s no way you can do so over a long(er) period of time. Analyzing trends can help identify key factors to focus on  to come up with an effective retention and growth strategy. For example a negative sentiment in the early stages of onboarding may be correlated with early churn. While a negative sentiment trend later in the customer journey may be less likely to end with churn since strong relationships have been developed.
    • Connecting Sentiment to the Product and Stakeholders – Understanding sentiment is important, but CS professionals also need to know what’s triggering it. Customer sentiment analysis helps associate sentiment to specific product features, onboarding experiences, and other customer journey aspects. For example, a new buggy feature will definitely spark negative sentiment.The same applies to specific team members and stakeholders. A specific account can have multiple personas that are in touch with you. Customer sentiment analysis helps you analyze things on a personal basis.
    • Comprehensive Mapping and Actionable Insights – As explained earlier, traditional methods like manual reachouts and NPS surveys are limited and paint a partial picture. Customer sentiment analysis is a real time signal (think of it as a pulse or “instant NPS” micro-moment) that may act as an early warning signal vs a lagging indicator. This is  a comprehensive way to map all stakeholders (vs only those that are replying to a survey) and gain a bird’s-eye view of each and every account on an ongoing basis.Detecting at-risks accounts becomes more predictable and earlier on as well as understanding what went wrong when analyzing outcomes in retrospect.. But it doesn’t stop there. You can know who your true advocates are and where you need to focus your efforts for creating more impact. It’s a true customer success game changer.

    How to Collect and Measure Customer Sentiment?

    Collecting customer sentiment is not an easy task in 2023. There are more and more channels and verticals where customers are voicing their opinion. Here are just a few of them you need to track and analyze for accurate sentiment analysis.

    Sentiment scores should take the following into consideration:

    • Ticketing Patterns and Tendencies – When customers are in trouble, they usually contact support via emails, chatbots, or websites. There’s probably no indicator that’s more direct than open tickets. The more customers are opening support tickets, the more likely that they are going to churn. CSMs need to keep track of this key aspect at all times and address the pain points.
    • Email Communications – Dozens of emails are being sent by customers on a daily basis. More often than not, the context of the communications goes unnoticed. CSMs simply don’t have the bandwidth to analyze each and every sentence. Needless to say, frequency fluctuations in email communications can also say a lot about the customer’s current state of mind.
    • Review Websites – Review websites are also clear cut indicators of customer sentiment. Most leading websites today feature detailed (first-hand) feedback about key aspects like onboarding, customer support, pricing, and more.
    • Social Media – Social media is also a big “ customer playground”. They can communicate with other customers (community), express their emotions, and respond to the latest posts from the company. But monitoring so many social media platforms is becoming increasingly challenging. Manual social media monitoring is simply becoming an unrealistic task.
    • Product or Service Usage Trends – Last but not the least, you also have product usage frequency and engagement with specific features. Not monitoring this customer behavior can lead to what customer success executives dread the most – churn. This aspect is extremely important while rebranding your product or launching a new feature.

    Companies also may prioritize one communication channel or opt for a combination of many. Some primarily use Slack with other messaging apps, while others prefer engagement tools that are supplemented by education, and community platforms. While these variations create different sets of challenges from the CS standpoint, they all encapsulate customer sentiment in their own unique way.

    Customer Sentiment Analysis Tools

    There are many approaches when it comes to performing customer sentiment analysis and keeping track of what customers are feeling. CS teams usually calculate their sentiment scores in rigid and inflexible ways. Also, the vast majority of CS tools still depend on human intervention (documenting, analyzing, etc), something that eats up valuable time and resources that should be used elsewhere.

    Here are just a couple of them:

    • Net Promoter Score (NPS) Survey Tools – Despite their reactive nature, these are one of the most popular ways to gauge sentiment. Survey tools are usually the first ones to make the CS tech stack. It’s important to mention that no sentiment score is reached automatically with this method. CSMs need to collect and integrate NPS scores into their sentiment formulas manually.
    • Support Tools – Most modern support tools come with analytical capabilities that help CS teams feel the pulse and understand what’s going on during important events like feature releases, major UX updates, or rebranding campaigns – How often are tickets being opened by specific customers? What are the current trends? What issues are causing the most friction?

    While all of these solutions are worthy additions to the modern CS tech stack, the bottom line is that manual work is needed to orchestrate everything and reach actionable insights. AI-centric solutions are trending up for a reason. They analyze all communications, automate documenting tasks, provide in-context insights in real-time, and free CSMs to go proactive and attack new growth opportunities.

    Customer Sentiment Analysis: Are Humans Redundant?

    The answer is no. Customer sentiment analysis is not going to replace humans altogether, but is now required to help them stay objective and enhance their insights. Humans are relatively good at analyzing the present, but achieving historical accuracy, conducting trend analysis, and making forecasts is not easy in today’s dynamic digital space where companies are also scaling up faster. 

    This new methodology can both help with reviewing longer time frames and analyzing trends across different customer accounts. Customer sentiment analysis should be viewed as a supplementary tool to keep everything in check and deal with bias. But also understand it has limitations since just like a group of people might not always agree on someone’s sentiment so will AI often come up with results that may seem different than what someone thinks…

    To sum it up, my recommended approach is to use both AI and human insights. This combined approach should help you achieve a more proactive approach allowing for shorter reaction times, overall better results  in the form of sustainable growth.

    See Additional Guides on Key AI Technology Topics

    Together with our content partners, we have authored in-depth guides on several other topics that can also be useful as you explore the world of AI technology.

    Customer Success

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    Machine Learning For Business

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    Large Language Models

    Authored by Swimm