Customer Sentiment Analysis: Leveraging AI to Extract Actionable Insights

“The customer is always right” – a century-old saying that’s guided business decisions. But these days, we can do better than just taking their word for it. Customer sentiment analysis provides the lens to analyze not only what customers say, but how they feel.

Think of sentiment analysis as an investigative journalist for customer feedback. It looks for trends and patterns in customer behavior rather than surface level praise or social media rants. With the rise of machine learning and natural language processing, businesses of all sizes can now quantify and analyze customer sentiment at scale.

In this article, we’ll explore examples of customer sentiment, best practices for measuring customer sentiment, and how to use it to better satisfy customer needs.

But first, let’s breakdown the basics of customer sentiment analysis (or skip straight to the AI section):

Customer Sentiment Analysis 101

Sentiment analysis requires a healthy volume of customer communication to source feedback and ultimately be able to draw confident conclusions on overall sentiment.

Customer Sentiment Examples

The first step in analyzing your customer experience is gathering feedback. Gather feedback through several channels:

• Social media comments
• Customer reviews
• Surveys
• Appointment or booking recordings
• Customer support recordings

Ideally, you want to use a mix of these sources to gauge customer satisfaction across the entire customer journey.

Best Practice: Downloading these into a .csv or .txt file will make analysis easier.

Sentiment Polarity: How Customer Sentiment is Measured

Sentiment classification is the process of categorizing sentiment into emotion, known as sentiment polarity:

• Positive Sentiment (above +1.0)
• Neutral Sentiment (-1.0 to +1.0)
• Negative Sentiment (below -1.0)

Customer reviews are categorized using key words that express feelings. Think ‘horrible’ or ‘exceptional’.

Feedback is then measured using qualitative and quantitative analysis:

1. Identify important words that express feelings
2. Assign numbers to words (-2 for very negative, +2 for very positive)
3. Add up numbers for each review

Each review is then deemed positive, neutral, or negative depending on their aggregate score.

Analyzing Customer Sentiment

Analyzing customer feedback can take several forms, each just as important as the next when drawing sentiment insights.

Sentiment Score Distribution

Looking at sentiment data in aggregate allows for better analysis of the customer experience. This provides unique customer insights by determining what parts are viewed positively or negatively.

A recent study I conducted returned the following results:

• Positive: 25%
• Neutral: 35%
• Negative: 40%

We identified that the majority of customer feedback was actually negative or neutral. This prompted us to look further.

Opinion Mining

Shifting to qualitative analysis, we analyzed the negative feedback to better understand customer expectations. In doing so, we grouped common complaints into categories to identify future content topics, better known as opinion mining.

Through this exercise, we observed the following recurring patterns in negative sentiment:

1. Questions about product durability
2. Frustration with price transparency
3. Health/environmental concerns

These became topics to address in future creative and landing page edits, saving us time and energy in compiling new test ideas.

The same process can be applied to positive sentiment to better understand what makes happy customers. Finding areas where your brand excel

Finally, you’ll want to monitor how customer sentiment changes over time.

Sentiment Trend Analysis

Sentiment analysis is not static – it changes as your brand evolves. This means it’s important to keep tabs on how customer opinion of your brand changes over time.

Trends don’t just show overall sentiment, but help understand brand reputation. Furthermore, it’s an opportunity to learn how changes to your strategy are working.

However, like all organic channels, customer sentiment won’t change quickly (unless there is a lot of paid media in market).

Analyzing customer sentiment on a quarterly basis to start is a good cadence, and can be scaled up or down depending on how active marketing is at pushing new content.

AI in Customer Sentiment Analysis

Sentiment analysis tools like Brand24, Brandwatch, or Talkwalker have created a business around always-on sentiment monitoring. Their key value propositions are broken down into three areas:

1. Data collection: Direct integration with many major social media platforms
2. Sentiment scoring: Classification of sentiment polarity
3. Trend analysis: Graphs & charts which trend change over time

These customer sentiment analysis tools have trained AI models to detect emotion in language and translate it into a scoring and analysis system.

However, with the rise of natural language processing tools like ChatGPT, Claude, or Google’s NotebookLM, a new host of competitive products has entered the market, at a fraction of the cost.

How AI is Changing the Game

Where AI models used to be expensive to access, they are becoming more and more accessible by the year. Not to mention, the quality of models trained on the open internet far surpass those that were trained on limited datasets.

Anyone with access to ChatGPT or Claude has a de facto customer sentiment analysis tool with capability that rivals enterprise tools.

Let’s take a look at how sentiment analytics can be run through a language model.

Using AI for Analysis

Collect Feedback

Following the process outlined earlier, we first need to collect feedback. To do this, download customer data into a .txt or .csv file.

• Facebook comments can be downloaded through the Accounts Center page. See a full explanation here.
• Reddit data can be downloaded into a text file using a Python script. See how to scrape Reddit comments in 4 easy steps.
• Download reviews and survey feedback from a CMS or CRM platform.
• Download transcripts or audio files of customer phone calls recorded and saved in a call tracking software.

Note: All customer interaction should be recorded!

Convert call recordings into text based files using transcription software – if your call tracking software can’t do this natively, there are a host of tools that can do this for free.

Once customer data is compiled, it’s time to craft a prompt in your preferred AI tool.

Prompting For Sentiment Score

Make sure to provide instructions following a methodology similar to that outlined above.

Here are two examples of prompts you can use. Note, analysis was conducted through Claude AI, but any LLM will work.

Natural language processing is quite adept at emotion detection, so simple prompts like above can work well.

However, for larger datasets including long transcripts or multiple data sources, a more explicit instruction can help.

Here’s an example of the output. The content analysis will contain insights for the customer sentiment data you provide.

Another option for free analysis is Google’s NotebookLM. While each chat is limited to 50 data sources, NotebookLM can to ingest PDF and .txt, as well as Audio (mp3).

This means that any recordings through customer service or leads can be easily uploaded and analyzed using this tool.

With the data analyzed, it’s time to draw insights and actionable steps to take moving forward.

Sentiment Insights: How to apply Sentiment Analysis

Customer sentiment analysis aims to answer questions like:

• What builds customer loyalty?
• How can I improve my customer experience?
• What is my brand reputation?

Each of these questions can be answered through sentiment analysis.

Customer Loyalty

Customer loyalty is built through customer satisfaction. 

In the example shared earlier, many comments expressed praise for the product quality, aesthetics, and purchase process.

However, there were an equal share of comments expressing frustration with parts of the customer experience, particularly as it pertained to a warranty program.

Many customers were forgetting to submit a warranty card, but expected the full coverage of the warranty that was included with the product.

We developed a series of emails to better educate customers of the warranty program, including reminders to fill out their warranty cards as soon as possible. This led to a 30% increase in warranty card submissions 3 months after its roll out.

Customer Experience

The customer experience is a measure of customer interaction.

Wherever a customer engages, you want to exceed their expectations. In our previous example, many comments expressed frustration around the pricing model.

Many people were browsing online, then purchasing in store where prices were different. This created confusion around the actual price of the product.

To alleviate this, we removed the price from the website, opting for a quote to better align with pricing differences by region.

Brand Reputation

Brand reputation is derived from customer emotion.

Measuring negative vs positive sentiment in aggregate gives insight into how likely customers will recommend the brand to others.

In the previous example, sentiment was skewed 25% Positive vs 40% Negative. It’s unlikely those 40% with a negative emotion will be a brand advocate.

However, as it turned out most of that 40% had similar concerns around product durability. We created specific product care instructions and maintenance content to inform customers of the best practice in keeping their product in good condition.

Addressing those concerns head-on restored customer confidence in the brand, and actually improved positive sentiment in the months following the launch of this material.

How to Implement Sentiment Insights

Improvement is the name of the game, and customer feedback only fuels this. Analysis will inform:

• Product improvements
• Website content (landing pages or blogs)
• Ad creative topics
• Customer journey mapping
• Customer support improvements

And this is just scratching the surface. The more feedback you capture, the more areas there are to improve process.

Bonus: See how customer feedback could apply to the Landing Page Best Practices I reviewed last week.

Customer Sentiment Analysis: Why It Matters

“The customer is always right” is truer now than ever before, with unprecedented access to customer data. As consumer behavior becomes more complex and competition increases, obsessing over the customer experience becomes ever more important.

Customer sentiment analysis provides the lens through which to examine this. Through robust customer data collection and AI-powered analysis, even the smallest teams can turn customer feedback into actionable insights – reducing time and energy spent on strategic planning while increasing the chance of success. A true win-win.