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Model Evaluation

Brand Sentiment Data

What is brand sentiment data?

Brand sentiment data measures the feelings and opinions that people have towards a specific brand. Besides tracking the number of brand mentions on social networks, online reviews, and customer feedback, it also provides context by the tone of the comments and conversations among target audiences. Brand sentiment can be positive, negative, or neutral, according to the sentiment analysis of opinions and the underlying emotions.

This type of data  can be leveraged in market research to create the ideal target audience for new product launches and marketing campaigns, to increase brand visibility, to improve the customer experience, and to build positive sentiment for the brand. 

Where does the data come from?

The data sources include social mentions and social media posts from sites and apps such as Facebook, Instagram, and Twitter. The information can be obtained through social listening and monitoring tools such as HootSuite. Other sites contributing to opinion mining are various forums, blogs, and review sites.

Brand sentiment analysis tools look at social media analytics and metrics such as mentions, conversations, comments, views, impressions, likes, and shares. Apart from the brand name mentions, also taken into account are hashtags, related keywords, and key phrases.  Some tools may determine the strength of positive and negative mentions in order to provide a sentiment score. 

What types of attributes should I expect?

Brand sentiment analysis provides mentions of the brand name, associated sentiments, customer satisfaction, and the measure of their strength. Most of this information is presented as pie or bar charts.

Some vendors may also offer more detailed information such as keyword measurement across a specific industry or entire markets as cluster graphs. These graphs contain nodes representing keywords and the size of the nodes as the keyword use volume. The location of the node indicates how central the keyword is to the topic.

Historical brand sentiment data is often available in bulk for analyzing trend cycles.

How should I test the quality of the data?

Brand sentiment is driven by social media monitoring and online media mentions. Social media users are posting consistently, therefore this information is constantly evolving. The most critical test for sentiment dataset quality is how often the data is updated. Ideally, the data should be updated in real-time to drive an accurate analysis.

The second test is the accuracy of the sentiment, for which use of machine learning and the most efficient Natural Language Processing (NLP) algorithms is essential.

Ensure that a wide variety of social media platforms and other online sources are included to provide a comprehensive sentiment analysis.

To test the quality of the data:

  • Ensure the range of credible sources providing the data.
  • Evaluate the data for accuracy and real-time updates.
  • Confirm that the vendor uses the most recent and powerful NLP tools for text analysis. 

Who uses brand sentiment data?

Companies use this data to track the performance of their brand, measure campaign success and customer satisfaction, plan new products, respond to queries fast, and manage any crisis. They also use this data to compare with competitors and accordingly improve their products or promotional offers. Another key use of brand sentiment data is identifying opportunities to improve customer experience.

Marketers leverage brand sentiment data to create online content that helps improve the sentiment in the market. It can also result in new campaigns or endorsements to raise brand visibility. 

What are the common challenges when buying brand sentiment data?

Brand sentiment is derived from online sources, and the most common challenge is ensuring its recency. Analysis powered by obsolete data cannot drive the expected outcomes.  Using the best machine learning algorithms to interpret the underlying emotions of customers is critical for arriving at the accurate measurement of sentiments. 

The most common challenges include:

  • Data recency: Brand sentiment data is volatile as any news or event can quickly change sentiments. Ensuring that the data provided is the most recent, ideally real-time, can help maximize the benefits of sentiment analysis. Real-time brand sentiment data will deliver the most accurate insights.
  • Data accuracy: NLP algorithms interpret customer sentiments from comments and reviews. Accuracy of sentiment measurement and keyword frequency drives decisions on products, promotions, and marketing strategies. As the sources of social and online platforms vary in their coverage and user base, ensuring data accuracy for the required purpose is challenging.
  • Data consistency: Brand sentiment data is derived from various social media platforms, and the data provided may not be consistent across all of them. Some sources may deliver only the basic attributes, while others may present cluster graphs. Ensuring data consistency and completeness is a key challenge and needs to be managed carefully.
  • Privacy compliance: Brand sentiment data may include personally identifiable information (PII) and must comply with the required region-specific privacy regulations.

What are similar data types to brand sentiment data?

Brand Sentiment Data is similar to shopper data, product data, eCommerce data, consumer review data, and other brand-related data categories used in sentiment analysis.

You can find a variety of examples of brand-related data in the Explorium Data Gallery.

Sign up for Explorium’s 14-day free trial to access the data available on the platform. 

Access Brand Sentiment Data with Explorium                          

What are the most common use cases of brand sentiment data?

The typical use cases for brand sentiment data are brand awareness and brand repositioning. This data contributes to customer segmentation to identify the ideal segment for targeting. It can also augment other demographic data categories for competitor analysis. You can use brand sentiment data for improving customer experience, managing crises, and developing new products.

  • Brand awareness:  Critical for promotion and marketing strategy, brand awareness indicates the degree to which a customer can recall and recognize your brand. High brand awareness signals the popularity of the brand and its products. Brand awareness is particularly effective for companies in the early stages to improve the top line growth. Brand sentiment data provides the measures of positive, negative, and neutral sentiments associated with the company and its services, signifying its brand awareness.
  • Brand repositioning: The process of changing the perceptions and associations that people have with your brand is called brand repositioning.  It is driven by the need to make radical changes in a company’s identity. This need need may be due to shifts in the market, failed products or services, losing out to competitors, mergers, localization requirements, or the current position losing its relevance. Brand repositioning revitalizes the company and often reaches new demographic segments. Brand sentiment data indicates if the negative or neutral sentiments are unusually high, making a case for brand repositioning.
  • Customer segmentation: Smaller customer segments enable companies to address customer needs with the right experience and optimized resources. Customer segmentation by a wide range of demographic data categories helps companies understand customer interests and the price they are willing to pay for specific products. Brand sentiment data refines the segmentation to help choose the best channels for targeting promotional and advertising efforts.
  • Crisis management: In the case of a sudden and significant negative event, crisis management minimizes the damage to brand's reputation. Modern media and social media react very fast to false information about a company, causing panic. Companies typically have a crisis management strategy in place to ensure that in case of hostile or highly negative sentiments, no rushed decisions are made. Real-time brand sentiment data helps manage any brand reputation crisis quickly.

Which industries commonly use this type of data?

Companies using this data operate in the verticals of retail, CPG, travel, hospitality, leisure, entertainment, financial institutes, insurance providers, banks, and others where brand value is critical.    

How can you judge the quality of your vendors for brand sentiment data?

The vendors use online and social media platforms to collect the brand sentiment data. This data drives the critical decisions on marketing and advertising campaigns, often in real-time. High-quality data supplied by reliable vendors can give you accurate insights, and you can use demos or customer testimonials to judge the vendor quality.

  • Customer testimonials: Reviews and testimonials from customers are a good indicator of the credibility and quality of the vendor. They can also reveal how the vendor interacts and engages with customers.
  • Case studies: They illustrate how the vendor engages in providing custom solutions to specific requirements. Case studies also describe the range of data attributes and data quality.
  • Demo: The easiest and most reliable way of judging vendor quality is by getting a demo. Demos should provide a good idea about data quality and how quickly the vendor can deliver the required attributes. Most vendors also provide sample datasets to help you test the datasets in your project. 
  • Interacting with vendor reps: After you shortlist vendors with the information available online, go to the next step of contacting the vendors. Interacting with vendor reps should go hand in hand with getting a demo - the rep should be able to address your specific queries. Interaction with the rep provides an opportunity to judge the vendor's commitment to your requirements of attributes, quality, and integration.

 

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