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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”.
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.
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
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:
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.
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:
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:
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.
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:
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.
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.
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