What is sentiment analysis in marketing?

Sentiment analysis is as useful for digital marketers as it is for stock market investors and other finance-oriented professionals. The growth and popularity of machine-learning algorithms have made this form of analysis more accurate than ever, and it’s available to anyone with any budget.

This article discusses what sentiment analysis marketing is, why it’s useful, ways to use it, and the various tools it applies.

Sentiment analysis is a marketing tool that allows you to measure how people interact with your brand online. It’s a more comprehensive way to examine the effectiveness of your marketing efforts compared to traditional online marketing tracking, which evaluates online customer interactions.

When relying on sentiment analysis, you can categorize individual interactions as negative, positive, or neutral. When you determine and track these categories, you can use this data for different marketing purposes, including your online strategy.

Sentiment analysis is incredibly useful because more interactions do not always equate to better results. For instance, a social media post that receives ten positive replies is better than a post that receives 100 replies, ten of which are positive.

Ultimately, the main goal of sentiment analysis is to offer constructive feedback.

Sentiment analysis is essential because it offers insightful feedback on how customers perceive your products or services. Here are some of its tangible benefits:

Understanding your audience and defining your niche

With sentiment analysis, you can get a more granular perspective when examining your audience. This can help you quickly identify a market niche that suits your company’s products and services.

When you understand what your brand means to your current customers, you can swiftly increase its market share. For instance, a struggling ice cream parlor can learn about the types of flavors that people like or dislike. The business can then improve profits by heavily marketing popular flavors and lowering operating costs by discounting those that are less popular.

Managing PR issues and improving customer support

Many businesses rely on social media channels to offer customer support since these platforms are more personalized and immediate. Customer support staff can respond quickly to negative comments and help de-escalate situations before they grow and become less manageable.

For instance, if someone tags your brand in a post discussing their frustration about a defective item, you can offer a public apology. Then, you can follow up with them privately to further show your commitment to quality.

Handling negative sentiments effectively in public also shows other customers that your brand has great service policies.

Adjusting messaging and product development

When you know the aspects of your product or service your customers value most, you know what to emphasize in your adverts.

For instance, if one of your products suddenly experiences an unexplained spike in sales, you can examine your positive replies to get insights.

Identifying influencers

A sentiment analysis system can help you identify micro-influencers. These are social media personalities with a relatively small number of followers that are very active.

These micro-influencers are typically the most beneficial to your brand since they interact with their followers directly. You could find that one of your customers closely follows a podcast with about 20,000 subscribers and an audience that intersects with your own. You could then contact the podcaster and ask if they want to be sponsored.

Monitoring competitors

Both positive and negative comments can reveal valuable information about your company’s competitors. Evaluating how customers perceive your services and products compared to others can help you develop promotional materials that highlight your brand’s advantages.

For example, customers could post on social media that they love the discount codes and coupons a competing brand offers. This information could encourage marketers to put out adverts that emphasize more competitive sales offers.

Researching your target market

When planning a new marketing campaign around a controversial topic, it’s important to assess the emotions around it and ensure you want to be included in the discussion.

Sentiment analysis can also help inform your decisions about releasing a new product or entering a fresh market.

Supporting employer branding

Opinions about your business greatly influence your recruitment costs and effectiveness. By analyzing the emotions related to your brand from your applicants or on opinion forums, you can know how well your online presence complements your recruitment campaigns. For instance, you might discover that potential applicants are discouraged by what they read about your brand online.

Sentiment analysis doesn’t always work in the same way. Several different approaches use distinct algorithms and mechanisms, depending on the desired outcome and context.

The major subtypes of sentiment analysis are:

• Fine-grained sentiment analysis: when measuring polarity precision, you may decide to use clearer polarity categories. For instance, you might have “highly negative,” “slightly neutral,” or “somewhat positive” categories.

• Emotion detection: this form of sentiment analysis helps detect emotions such as sadness, anger, frustration, and happiness. Most emotion-detection systems rely on complex machine-learning algorithms or lexicons.

• Aspect-based sentiment analysis: this form of sentiment analysis is helpful when you want to examine sentiments of texts (such as product reviews) to know the various features or aspects of your product that people are mentioning in a negative, neutral, or positive way.

• Multilingual sentiment analysis: this is a highly complex form of sentiment analysis, as it involves a great deal of pre-processing and resources. You can access most of these resources, like lexicons, online. Others have to be created, such as noise-detection algorithms or translated corpora.

Sentiment analysis marketing relies on natural language processing (NLP), statistics, and machine learning to assess how people think and feel on a larger scale.

These tools process written content to assess its negativity or positivity. They mine content from various sources, including social media platforms, blog posts, chatbot conversations, third-party websites, emails, and support tickets.

Sentiment analysis is all about evaluating customer feedback via your product’s or brand’s perception in the media. The three major source types you should focus on are:

Social media

Most people turn to social media to express their feedback and talk about their experience with a particular brand or product. Therefore, these platforms are some of the best sources of discussion to assess.

Conversations with your customers 

Wherever you engage with customers is a great source of sentiment analysis. For instance, you might interact with customers using a customer relationship management (CRM) system, calls, emails, or even Facebook Messenger. Archive all these engagements and examine their sentiment to get a full view of your audience’s emotions.

Other media

While getting textual data from other media (such as newspapers, TV, blogs, and online forums) could be more difficult, the quality of data you obtain could be higher.

The major challenges of sentiment analysis include the following:

• Determining the tone of comments can be difficult, particularly when neutral content doesn’t offer much information. The algorithm might categorize a comment as either positive or negative or just leave it out altogether. For example, when someone comments that “the hotel is situated in a busy area,” it can be difficult to understand whether “busy” is negative or positive.

• Algorithms could mistake sarcasm for positivity or negativity. For instance, someone could criticize a marketing tactic using a sarcastic comment like, “What a great idea!”

• Technology may run into problems when tackling idioms, emojis, or even multiple languages. For example, when customers use slang or figurative language in their comments about your brand’s newest product, the technology might not be able to decipher the sentiments.

In sentiment analysis, sentiments refer to the feelings, emotions, attitudes, or opinions expressed in a certain comment. The sentiment could be negative, positive, or neutral.

“I absolutely loved the hotel we stayed at during our honeymoon. The rooms were exceptional, and the ambiance was perfect.”

This is a positive sentiment because it contains words like “loved,” “exceptional,” and “perfect.”

Numerous companies across various industries use sentiment analysis to gain valuable insights from vast amounts of textual data and improve their decision-making processes. These include companies in the following industries: finance and investment, e-commerce, market research, healthcare and medical, and travel and hospitality.