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Model Evaluation

Social Media Data

What is social media data?

Social media data provides commonly available information on social media channels published by social media users. These data points are in the form of blogs, posts, likes, followers, clicks, shares (reposts and retweets), comments, or engagement rates. 

Typically, marketers use this data to obtain audience insights, sentiment analysis, and for target audience segmentation. Social media analysis can also provide some indication of the level of brand awareness and customer satisfaction, which can help measure the effectiveness of marketing campaigns and social media strategy. 

Where does the data come from?

Most of this data comes from the social media content individuals publish on social channels and apps such as Facebook, Twitter, Instagram, LinkedIn, Snapchat, or TikTok. The published information includes posts, likes, comments, shares, clicks, as well as POI and event check-ins. Blog and forum sites may also contribute to this data.

Social media metrics can be collected from social media analytics tools (i.e. Twitter Analytics), social media management tools such as Hootsuite, social listening tools, and sophisticated data collection tools. Vendors who use the more sophisticated tools can provide more detailed information such as the time spent viewing a post or promoted content, total time spent on a specific app, location, the device used, and the choice of primary language.

What types of attributes should I expect?

The common attributes include:

  • URLs of social media apps or sites visited
  • Number of social networks and social mentions
  • Number of tweets, followers, the accounts followed, and analysis of tweets
  • Number of  LinkedIn connections and followers
  • Time spent on different social media apps and sites
  • Demographic information such as age, gender, location, educational profile, and income bracket, which can help in customer segmentation
  • Interests and sentiments, which can indicate brand awareness and brand sentiment
  • Business presence and engagement on social media (to help assess a brand)

How should I test the quality of the data?

Social media data should have two critical requirements: 

1)  The data must be clean 

2) The Natural Language Processing (NLP) algorithms must be effective.

Social media sites are swamped with bots and web scraping tools, making data clean a big challenge. Manual processing or review is practically impossible due the volume of data generated on social media. Availability of clean data is the first concern while working with social media presence data.

The next issue is about the accuracy of data, which largely depends on the NLP algorithms used. Social media users more often tend to express extreme sentiments, either overly positive or excessively negative. They also tend to write long stories on Facebook or multiple consecutive tweets on Twitter. Reviewing the text manually for real sentiments, mockery, sarcasm, or other indications is not feasible, highlighting the need for the most recent, most effective NLP algorithms.  

How often the data is updated will determine if it can rapidly provide actionable insights. On social media, users tend to scroll quickly, and have short attention spans. If this data gets captured, cleaned, and interpreted in real-time or near-real-time, the analysis can deliver the immediate best action to leverage the user sentiments for decision making. 

It is also essential to ensure that social media data is privacy-compliant if the attributes contain personally identifiable information (PII) or any other sensitive information.

To test the quality of the social media data:

  • Evaluate the data for frequent or real-time updates.
  • Test the data accuracy, completeness, and consistency.
  • Validate that the vendor uses the most current, sophisticated, and effective NLP tools to interpret the information published on social media.
  • Verify the privacy compliance of data.

Who uses social media data?

Companies use this data for business decisions around a number of marketing use cases, including tracking campaign performance. Social media marketers use this data to help build their social marketing strategies, and assess the performance of content marketing on social channels. 

Social media companies use this data to improve user experience and launch new features. They also capitalize on this data for pricing promotions on their platforms.

Other groups that use this data include academics, researchers, government bodies, and intergovernmental organizations. They analyze sentiments and trends to track specific activities. 

What are the common challenges when buying this type of data?

The most common challenge when buying social media data is assuring its accuracy. This data plays a part in many important marketing decisions, including advertising and promotion strategies, and it must correctly present the information derived using the NLP tools. This type of data is a measure of brand awareness and marketing campaign effectiveness, it must get frequently updated to reflect the most recent posts and mentions.  

Other challenges when buying this data include assuring its consistency, completeness, and compliance with privacy regulations.

  • Data accuracy: Information about what active users share on social media platforms must be accurate to power the analysis used for decision making. Opinions and sentiments expressed on social media channels need to be understood and interpreted correctly using the most sophisticated tools.  The capabilities and efficiency of the NLP algorithms drive data accuracy in this case. As the traffic across social networks is high, getting accurate data in a timely manner is a significant challenge.
  • Data timeliness: Social media platforms generate a large volume of activity, and tracking it in real time is challenging. The data collected from social channels must be cleaned and made available in near-real-time to derive any value. Opinions, interests, trends, and attention change quickly on social media, and only the most recent data can deliver trusted insights.
  • Data completeness and consistency: Over time, the user profiles of different social media platforms have evolved. Social channel usage varies greatly in different demographics. Interests and behaviors may also vary for users across different platforms. Data completeness and consistency across sources are critical in correctly segmenting customers.
  • Privacy compliance: Social media presence data can include personally identifiable information (PII) or any other sensitive information. Ensuring that the data is compliant with all the required privacy regulations is essential.

What are similar data types to social media data?

Social media data is related to brand sentiment data, demographic data, consumer lifestyle data, and other consumer data categories used to obtain consumer insights and for audience targeting.

You can find a variety of examples of brand and audience data in the Explorium Data Gallery.

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

What are the most common use cases of social media data?

Social media data is leveraged in many marketing use cases such as brand awareness, audience segmentation, social media performance tracking, promotion planning, and content distribution strategy.        

  • Brand awareness: It indicates the degree to which a customer can recall and recognize a brand. High brand awareness is a good sign of the acceptance of the brand and its products. Companies use brand awareness to build marketing strategies and content distribution strategies. Social media data provides information about the positive and negative points about the brand that people are discussing on social media platforms. 
  • Audience segmentation: Marketers segment the audience into smaller distinct groups to address their specific needs and wants. Audience segmentation is convenient in providing personalized experiences and customized offers. Social media data enriches the audience profiles and helps design targeted messaging, communications, and promotional offers. 
  • Social media performance tracking: Companies try to leverage their social media presence in addition to their website presence. Social media performance tracking provides the measure of how their promotions are performing on social media sites. Performance tracking includes improvement in brand awareness, boosts in new leads, and growth in website traffic. Companies use social media presence data to maximize the effectiveness of their promotional investments on social media platforms.  
  • Promotion planning: It is the process of optimizing promotional tools and resources to meet business objectives. Companies use several categories of data, including social media data, to analyze the effectiveness of promotions. Some companies use AI tools for this analysis and improve their plans to achieve marketing goals. 
  • Content distribution strategy: When companies plan blogs, newsletters, videos, webinars, advertisements, or any other content, they need to reach their target audiences. A content distribution strategy works with the target audience, distribution channels, and posting times to make the best use of content. An effective content distribution strategy maximizes ROI to leverage paid, owned, or free channels.  

Which industries commonly use this type of data?

Industries that commonly use this data for marketing and advertising include eCommerce, retail, CPG, travel, hospitality, leisure, entertainment, financial service providers, insurance providers, and banking.    

How can you judge the quality of your vendors for social media data?

The sources vendors use for social media data are social media sites that are constantly updated. Your vendor must be able to provide accurate and updated data. Some methods for judging the quality of vendors are checking what the customers say about them, how they present demos, and if the reps are knowledgeable and available for discussion.

  • Customer reviews and testimonials: Customer reviews help to understand how the vendor engages and provides appropriate service. Reviews and testimonials can also present some information about the projects using the vendor data.
  • Case studies: Some vendors provide detailed case studies on the website or when requested. Case studies present valuable information about the types of projects, data quality, data attributes, level of vendor engagement, and availability of custom datasets. 
  • Demo: A demo is a reliable method of ascertaining if the vendor dataset is suitable for your project. A demo should go over specific use cases, models, integrations, and other aspects, which indicate if the vendor services can match your requirements.
  • Interacting with vendor reps: After shortlisting vendors by evaluating customer reviews, case studies, and demos, you can shortlist some vendors. At the next stage, discussing your requirements with vendor reps can help you quickly make the final selection. Vendor reps should be able to respond to your queries and discuss custom solutions.

 

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