Sentiment analysis examples: How marketers are unlocking consumer insights

We’ve all heard the phrase “the customer is always right”. The standard brick-and-mortar shopping experiences of the past made that a pretty straightforward idea. Today, consumers can share their opinion anywhere—in store, on review pages, on their personal media profiles, on your brand’s social media profiles. The list goes on.

Advancements in sentiment analysis technology have made it so businesses can keep up with their customers at scale by quickly synthesizing audience thoughts and feelings about brands, industries, trending topics and more.

In this article, we’re sharing four sentiment analysis examples designed to help you think bigger about its potential role in your business strategy.

Why is sentiment analysis important for businesses?

By understanding the sentiment behind customer interactions, businesses can learn what they are doing well and what they need to improve. This insight enables teams throughout the organization to make better decisions and enhance their products and services.

Here are three of the most meaningful impacts of sentiment analysis in business.

It strengthens customer service

Sentiment analysis helps businesses monitor customer satisfaction trends and pinpoint areas for improvement. Marketers can use these insights to craft strategies that boost customer loyalty and retention.

We know consistently analyzing customer feedback allows businesses to address issues before they escalate, boosting satisfaction and trust. That said, customers are human. Their feedback has nuances, whether it be in the form of regional slang, spelling or even the use of emojis.

Aspect-based sentiment analysis tools enable businesses to detect nuances and identify trends in large volumes of customer feedback, so brands can take appropriate action.

It improves brand reputation

Sentiment data helps brands understand the timeless question: What do people think about us? What used to take countless focus groups and user surveys can now be determined from intuitive reporting dashboards.

Through analyzing the emotional tone of online conversations, companies can precisely identify their brand strengths and areas to improve. Equipped with this insight, they can develop targeted marketing plans, enhance product or service offerings‌ and eventually, strengthen their brand’s image.

Businesses that consistently manage their brand reputation will attract and retain customers, nurture loyalty and fuel business growth.

It provides consumer insight

Sentiment data isn’t just about knowing your current customers; it’s also your ticket to winning more in the future.

Sentiment analysis presents businesses with a unique opportunity to dive into the minds of their target audience, gaining a deeper understanding of their needs and expectations. This knowledge acts as a guiding compass, helping businesses develop products and services that precisely meet consumer demands, leading to increased growth and profitability.

Approaching sentiment data from an industry lens also allows brands to benchmark their offerings against key competitors, making it easier to identify opportunities to swoop in and win their customers.

3 sentiment analysis example to inspire your approach

The following sentiment analysis examples showcase how other brands use sentiment analysis data to refine their approach to product development, customer care and more. Use them to inspire your audience research strategy and playbook.

1. Dig into product-specific trends in consumer perception

If your business offers a variety of different products, services or in-house brands, you can use sentiment analysis data to uncover which offerings are outperforming expectations and why. Here’s a real-world social media sentiment analysis example that proves it.

A food and beverage company used sentiment analysis tools available in Sprout’s Social Listening solution to determine which snack bar flavors consumers preferred most. While the team knew a few products were more popular than others, they didn’t know why.

To achieve this, they used Listening Topic Themes, which are additional groupings applied to a Listening Topic’s messages to compare, filter and analyze data. The team created a Theme for each flavor of their snack bar to compare Listening data across categories. By filtering the surfaced messages by sentiment, they could break down the distribution of positive, negative‌ and neutral messages shared under each Theme.

After reviewing the data aggregated by Sprout’s Listening tool, the team found that chocolate and chocolate-related flavors generated a significantly higher ratio of positive sentiment. The positive sentiment stemmed from consumer preferences toward product consistency. Their fans liked chewy over crunchy.

This information was then passed on to the research and development team, who used it to inform future product releases. 

2. Perform risk assessments ahead of high profile campaigns and initiatives

As internet conversations grow more vast and complex, brand safety has become more crucial than ever. Even the most well-resourced public relations teams can’t proactively identify brand safety issues without smart tools that guide them on where to look.

Big Machine Label Group uses sentiment analysis tools to stay on top of what people are saying online, both good and bad.

“[Sentiment analysis] has been helpful as far as trying to understand how people are reacting to us and our artists. It’s also extremely helpful to have a listening tool where we can flag certain conversations internally and amongst the team, including conversations that could be delicate.”

– Matt Brum, Director of Digital Strategy and Social Media, BMLG

 Conducting a sentiment analysis on campaign themes and topics before major launches is the only surefire way to consider your message from every possible angle. By analyzing messages and conversations that drive negative sentiment, you can identify potential issues early. This can mean the difference between a well-executed campaign and a well-executed crisis management plan.

3. Identify customer experience opportunities

If you’re only paying attention to the customer feedback that comes in the form of glowing reviews or angry DMs, you’re not getting the full story. Customers share feedback every time they interact with your brand. Even a simple heart-eyes emoji left in your comment section can say a lot.

Conducting sentiment analysis on incoming social messages and reviews provides a more holistic view of your customer experience. Brands using Sprout do this by using the sentiment analysis features available in the Smart Inbox and Review Management tools.

Sprout’s sentiment analysis is built using a machine learning technique called a Deep Neural Network (DNN). When ‌you receive a new review or social message, the DNN computes a probability score to determine whether the content is positive, negative or neutral. Messages are automatically given a sentiment label, but users can change those classifications if needed.

Brands use this tool in collaboration with the Tagging feature to better understand the sentiment driving trends in customer feedback. For example, an automotive brand can create a Tag for feedback specific to their dealership experience. From there, they can filter Tagged messages by sentiment classification for deeper insights—like all the feedback about a positive dealership experience.

Feeling inspired by these sentiment analysis examples?

You know what they say about inspiration. It’s like a spark; you have to act on it before it fades.

Schedule a demo of Sprout Social’s social media listening tools for a personalized assessment of your social strategy. Our team will put you on the path to getting more from your audience sentiment data, so you can drive even more impact from social, faster.

FAQs

What is sentiment analysis?

Sentiment analysis, or opinion mining, is an AI technique that determines whether the sentiment in a piece of data is positive, negative‌ or neutral. This method uses algorithms that collaborate with other AI tasks, such as named entity recognition (NER), natural language processing (NLP)‌ and machine learning (ML), to quickly and efficiently assess sentiment in data.

How does sentiment analysis work?

Sentiment analysis uses algorithms that, when combined with other AI tasks such as named entity recognition (NER), natural language processing (NLP)‌ and machine learning (ML), can quickly and accurately determine the sentiment of the data.