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Top 4 Use Cases of Sentiment Analysis in Marketing in 2024

“This was great!” 

“I had a horrible experience.”

The sentiments in these sentences can be inferred from certain words, such as “great” and “horrible”. By analyzing the sentiments of reviews, feedback, and other customer interactions, businesses can improve their marketing campaigns. In this article, we will explore four use cases of sentiment analysis in marketing.

1. Understanding your audience

Understanding your audience is crucial to have a successful marketing campaign. This can be done on several platforms:

  • Twitter, Facebook and Instagram can be scraped to check for sentiments and perceptions of people about your brand or products and services. People also often write blogs and review articles which can be of help too. 
  • Any conversations with your customers through emails, text messages or complaint/feedback forms can also be useful to check responses to your products and tailor marketing campaigns accordingly. 
  • Sources such as newspapers and online forums can also be scraped to better understand what people think of your business. 
  • These techniques can help you evaluate your reputation and customer demographics, such as age and gender, in the market so you can know your target audience and who you need to focus on in your marketing strategies. 
  • Another way to see customers’ reaction is also by scraping data from influencers’ pages to see what they and their followers say about your products. Research suggests that 81% of consumers surveyed bought a product by a link shared by an influencer. 

For instance, KFC, Pizza Hut, and McDonalds, use sentiment analysis with their customer feedback and food preferences. This helps them in improving customer experience and increasing sales.

Also, check our data-driven list of sentiment analysis services if you think your company can benefit from sentiment analysis.

2. Staying competitive

  • Sentiment analysis can also help in determining where you stand among competitors. You can check customer feedback also for your competitors to see how consumers are responding to their products and which aspects of their products do they find good or bad. 
  • All the tools which you use to gather data for your product’s analysis can also be used for your competitors as long as the data used is publicly available. For example, web scraping tools help businesses and individuals automatically extract company data from web sources. You can then use this data to check how consumers are rating their products and make a comparative analysis with your own products.
  • You can also include any non customer feedback, such as those of bloggers or people mentioning any reasons for disliking/avoiding your product. This information can help you prepare better competitive products. 


Bright Data’s Data Collector can help with scraping data from online platforms which can be used for sentiment analysis

Source: Bright Data

3. Making better advertisements 

  • You can also check for public reactions to certain controversial or trending topics and use that as an advantage to make better advertisements. 
  • One such example is of Coca Cola using a message of inclusivity in its Super Bowl ad of 2019. 
  • However, you need to be careful about sensitive topics. For instance, Pepsi’s ad of 2017 backfired as people thought it trivialized the protest movements for Black Lives Matter. 
  • Sentiment analysis can help you determine whether your marketing campaign is appropriate for different places and cultures. One place may take the ad positively while another group of the people may see it as harmful to their culture.

4. Preventing setbacks and crises

  • If your product is experiencing an impending problem and you can see customers talking about it, then it can be used for your benefit by taking swift action and fixing any upcoming crises. 
  • You can also keep a check on your customer service’s operation and success by seeing how people are providing feedback to you. You can make them fill anonymous customer surveys to make sure your customer services are helpful and quick. 
  • Research has shown that companies can lose money due to bad customer service, making customers switch to another brand. 

What are some challenges of using sentiment analysis?

  •  It can be difficult to determine the tone of comments as at times there can be neutral comments which do not give much information but can be mistaken as positive or negative by the algorithm or even left out. For instance, in the sentence “The restaurant is located at a busy place”, it’s difficult to understand whether being busy here is something negative or positive. 
  • Some comments are sarcastic, such as “What a great idea!” can be shared as a tweet with an absurd marketing campaign idea as sarcastic criticism which can be mistaken for a positive remark. 
  • People often comment with emojis or idioms, which again can be difficult to teach the algorithm to categorize. Similar problems can come with customers using more than a single language in comments. 

Further Reading:

If you have other questions about sentiment analysis in marketing, we can help:

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This article was drafted by former AIMultiple industry analyst Rijja Younus.

Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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