Performing sentiment analysis for marketing campaigns can give a greater depth to any campaign by analysing the precise emotions, opinions, and attitudes of customers. However, many of the insights it provides can be subjective and easy to misconstrue. So, here’s how to do it properly, what tools to use, and how to collect sentiment.

What is Customer Sentiment Analysis?

Customer sentiment refers to the sum total of the customer opinions about a brand, service, or product. Sentiment comprises opinions, attitudes, emotions, and word of mouth about a company, which can be used to inform future business operations and strategy. Collecting customer sentiment, meaning gathering crucial information about what customers have to say about a brand or product, requires understanding the emotional tone of the texts. In essence, the content and context of the opinions matter.

The benefits of customer sentiment analysis include:

• Helps gather information that can lead to improvements and better customer satisfaction.
• Monitors sources of customer opinions to manage brand reputation better.
• Compiles what people like about your company and brand, and builds on it to improve customer relations.
• Creates a framework for understanding the strengths and weaknesses of your company in relation to competitors (which can be further supported with a competitor analysis).
• Measure the effectiveness of marketing activities.
• Building a useful structure to understand customer sentiment and behaviour can enable companies to better understand their audience and how to potentially serve those outside their current audience.

Types of Sentiment Analysis

There are 5 types of sentiment analysis.

• Fine-grained sentiment analysis
• Emotion detection
• Aspect-based sentiment analysis
• Multilingual sentiment analysis
• Intent-based sentiment analysis

Fine-Grained Sentiment Analysis

Fine-grained sentiment analysis utilises a wider range of options for customer sentiment, as opposed to simply the usual 3-point scale (positive, neutral, and negative). This can be a 5-point scale (very positive, positive, neutral, negative or very negative), or a 1–5 stars scale. This can be seen in product reviews and rating sites, which can be a great source for many companies.

This type of sentiment can offer wider depth and, with the combination of other datapoints, can allow for deeper analysis. For example, with age, gender, and other demographic data, you can divide up levels of enthusiasm across various segments.

Emotion Detection

Rather than measuring a numerical indication of quality, you can ask what emotions a brand or product evokes. This is also referred to as the lexicon-based method of sentiment analysis. Its main role is to help build a better emotional understanding of the company and its products. This can be especially handy in understanding the impact of brand stories and marketing messaging.

Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis relies on specific aspects of a product or service and how customers rate them. It’s more specific than an overall review, allowing companies to compare things like specs and performance in more detail. A PC manufacturer could test things separately, comparing battery life, screen sizes, or even customer support, to understand customer sentiment for each aspect.

Multilingual Sentiment Analysis

Multilingual sentiment analysis compares texts or content in different languages. It can be crucial for global applications and building a more global brand. Looking at different languages can also enlighten marketers about how different cultures receive their marketing messages.

Intent-Based Sentiment Analysis

Intent-based sentiment analysis looks at the intent behind a text or piece of content. This can be useful for seeing what stage of the sales funnel a customer is at by the way they talk about a product or which questions they ask in forums, for example.

How to Measure Customer Sentiment?

Companies often use a net customer sentiment score to calculate the overall opinion consumers have of them or their products. Here is the easiest method of calculating it:

• Net Sentiment = ((# of Positive mentions – # of Negative mentions) / Total mentions) × 100

The formula subtracts positive mentions from negative mentions, then divides that by total mentions, and is then expressed as a percentage. Customer sentiment metrics like net sentiment can measure broad issues like positive opinions about the brand or very narrow issues like how customers feel about a certain feature in a product.

Here are some other relevant metrics:

Other Customer Sentiment Metrics

• Sentiment Accuracy: This is the percentage of correctly identified sentiments in real-time analysis. It can be important because it leads to more reliable public opinion insights.
• Response Time: The amount of time it takes to analyse and report data, with faster response times enabling timely adjustments. This can lead to better messaging and strategy adjustments. It is measured in seconds or minutes per data point.
• Volume of Data Processed: This is the number of social media posts or comments processed per hour. Larger volumes provide a more nuanced view of customer sentiment trends. This is measured in posts per hour.
• Shift in Public Opinion: This is a measure of the amount of change in positive or negative sentiment after any actions that might affect it in real-time. It can be a handy way to measure the effects of real-time sentiment-driven campaigns. It is expressed as a percentage change in sentiment.
• Engagement Rate Change: The change in user engagement in terms of likes, shares, and comments after the application of sentiment analysis insights. Higher levels of engagement indicate better relevance and better alignment with public mood.
• Public Trust Index: This measures public trust in companies or brands using real-time sentiment analysis. It is crucial for examining responsiveness and transparency. It is best measured with surveys, ideally in 5-point brand trust scales.

Customer Sentiment Analysis Tools

There are several tools that help with customer sentiment analytics:

• Qualtrics: Qualtrics’ Text iQ is a sentiment analysis tool with NLP capabilities that can analyse unstructured data. These can gather data from various sources, including social media, surveys and customer support interactions. One great feature is the automatic categorisation, which can split up information into themes for easier classification. It also assigns sentiment scores on its own, making quantitative work much easier.
• Sprout Social: Aside from being a standard social media analysis platform, Sprout also has customer sentiment analysis AI programs that can transform raw data into usable information. Sprout Social is fantastic for gathering social media analytics across all platforms and channels. It can gather posts on socials, online reviews, and forums. It has some AI-powered sentiment tools, smart features, and automation tools.
• Chattermill: A unified customer intelligence platform that employs AI for feedback analysis. It transforms surveys, reviews, support conversations, and other types of communication inputs into powerful insights. The cross-channel data collection provides a unified view of brand sentiment in one easy-to-read dashboard.
• Buffer: The classic multichannel tool, traditionally used for posting and scheduling, offers features that can help with sentiment analysis. The ability to tag sentiment in posts as ‘negative, ‘question’, or ‘order’ helps brands sort through conversations. This allows for better planning and prioritisation of responses and information categorisation.

There are also numerous AI customer sentiment analysis tools within LLMs, since they can analyse a large chunk of text instantly. While there are many AI tools out there, freeware and standard LLMs can be great for analysing text. Other AI tools may be a valid option for more advanced features, if that is something your company requires.

Gathering Info & Customer Feedback For Analysis

If you’re looking to go through the data, here are a few easy methods:

• Text Analysis: Here’s where those AI programs can come in handy. Scraping and compiling customer reviews, social media posts, and survey responses is usually the way to go. Analyse keyword patterns and use contextual analysis to determine how customers view the company and/or brand. You can also build a customer testimonial form to gather in-depth information yourself if you have the capabilities.
• Speech Analytics: AI tools help analyse voice recordings, providing a lot of possibilities that may not have been possible in prior generations of technology. These can be utilised on customer service calls, sales conversations, and virtual assistant interactions. These can be good at picking up tone, stress levels, and vocal cues as well, although current models can vary in quality. It’s important to examine emotional states and note them down. These insights can provide ample material for future training in emotionally intelligent marketing messaging and outreach. It can also be fantastic for reiterating on customer service processes and making more informed decisions in customer communication.
• Facial Recognition: Monitoring facial expressions in video interactions can aid in assessing emotional responses as well. This technology is increasingly used in customer experience research and product testing. Businesses can evaluate real-time reactions to advertisements, product demos, and sales pitches.
• Social Listening: Monitoring online conversations throughout social media platforms, forums, and news sites provides ample material. It can be a great bellwether for public perception, making sure the brand or product can be identified and addressed proactively. Social listening tools like Sprout Social and Chattermill can build out a structure for continuously analysing changes in sentiment and tracking them.

Biases to Avoid in Sentiment Analysis

Customer sentiment data can be misleading if you don’t analyse it properly. Many companies miss the forest for the trees and go running off into unfamiliar terrain without the appropriate lay of the land.

• Data Source Evaluation: Always keep in mind the origin of the data and the context it originates in. For example, going to a subreddit with sarcastic takes or comedic content can lead to bad data or at least reverse the direction of sentiment.
• Algorithm Transparency: Algorithms can blur the context of specific content. For example, the most prevalent opinion online can be the most hostile or eye-catching one, but it may not reflect the majority opinion. This is related to exposure bias, where being exposed to certain things can make you believe they are more prevalent than they are.
• Linguistic Ambiguity: A core challenge is ambiguity in language since words can have different meanings based on context. This can lead to incorrect interpretations of sentiment and make analysis more difficult.
• Cultural Bias: Cultural differences can be an impediment to sentiment analysis when expressions of sentiment vary across demographics. This bias can manifest as treating different countries or languages with inconsistent or inapplicable standards, or even between age groups and subcultures.