{"id":579931,"date":"2025-07-02T05:05:54","date_gmt":"2025-07-02T12:05:54","guid":{"rendered":"https:\/\/www.genesys.com\/blog\/post\/understanding-sentiment-analysis-with-social-listening-and-monitoring"},"modified":"2025-07-03T02:07:33","modified_gmt":"2025-07-03T09:07:33","slug":"understanding-sentiment-analysis-with-social-listening-and-monitoring","status":"publish","type":"blog","link":"https:\/\/www.genesys.com\/en-gb\/blog\/post\/understanding-sentiment-analysis-with-social-listening-and-monitoring","title":{"rendered":"Understanding Sentiment Analysis with Social Listening and Monitoring"},"content":{"rendered":"<div class=\"wpb-content-wrapper\"><p>[vc_row][vc_column][vc_column_text css=&#8221;&#8221;]<span style=\"font-weight: 400;\">Social media is a valuable tool in customer experience. According to \u201c<\/span><a href=\"https:\/\/www.genesys.com\/en-gb\/resources\/state-of-cx?ost_tool=blog&#038;ost_campaign=0-0-0-0-0-0-0-blog\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">The State of Customer Experience<\/span><\/a><span style=\"font-weight: 400;\">\u201d report from Genesys, 27% of consumers surveyed have used social media for a service interaction in the past 12 months. And about one-third say they\u2019d mention a service interaction with a brand on social media when they\u2019ve had a great experience. But not all social comments are going to be positive. This mix of customer emotions \u2014 positive, negative or neutral \u2014 toward your brand is called sentiment.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Sentiment analysis is an important metric that analyses the online brand mentions and gives you insights into how your potential customers perceive your brand. However, to apply it correctly, you need to understand what sentiment analysis is, its benefits and how it works.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In this article, we\u2019ll cover those topics, and we\u2019ll look at how some brands are using this metric for success.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">What Is Sentiment Analysis?\u00a0<\/span><\/h3>\n<p><span style=\"font-weight: 400;\"><a href=\"https:\/\/www.genesys.com\/en-gb\/definitions\/sentiment-analysis\" target=\"_blank\" rel=\"noopener\">Sentiment analysis<\/a>, also known as opinion mining, is the process that uses natural language processing (NLP) and machine learning (ML) techniques to analyse the online written pieces and determine the emotional intent behind them.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Simply put, it involves understanding customers\u2019 attitudes toward a particular topic, brand, product or service \u2014 positive, negative, or neutral.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For example:\u00a0<\/span><\/p>\n<ul>\n<li><span style=\"font-weight: 400;\">\u201cI really like your new website design.\u201d \u2013 Positive\u00a0<\/span><\/li>\n<li><span style=\"font-weight: 400;\">\u201cI\u2019m not sure if I like the new design of your website.\u201d \u2013 Neutral\u00a0<\/span><\/li>\n<li><span style=\"font-weight: 400;\">\u201cI don\u2019t like the design of your new website.\u201d \u2013 Negative\u00a0<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Since customers give their feedback more openly than ever before, sentiment analysis is a powerful tool for monitoring and understanding their opinions and social media conversations. This way, brands learn what makes customers happy or angry so that they can tailor their products and services according to customers\u2019 needs.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">There are four types of sentiment analysis:\u00a0<\/span><\/p>\n<ul>\n<li><span style=\"font-weight: 400;\"><strong>Fine-grained:<\/strong> Analysing reviews and ratings and classifying them into positive, negative, and neutral.\u00a0<\/span><\/li>\n<li><span style=\"font-weight: 400;\"><strong>Aspect-based:<\/strong> Identifying the opinions on a specific aspect of a product or service. For example, TripAdvisor uses this approach to identify the sentiment behind the customer feedback and the service itself.\u00a0<\/span><\/li>\n<li><span style=\"font-weight: 400;\"><strong>Intent-based:<\/strong> Identifies the intention of the customer\u2014whether they want to purchase something or just browse. <\/span><\/li>\n<li><strong>Emotion-based:<\/strong> Identifies the emotion of a customer behind the mention. This can include sadness, anger, happiness, satisfaction, frustration, etc.<\/li>\n<\/ul>\n<p>[\/vc_column_text][vc_row_inner][vc_column_inner][vc_column_text css=&#8221;&#8221;]<\/p>\n<h3><span style=\"font-weight: 400;\">How Brands Use Sentiment Analysis<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Let\u2019s look at some of the popular applications of sentiment analysis.\u00a0<\/span><span style=\"font-weight: 400;\">\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>Brand monitoring:<\/strong> Customers talk about your brands, products and services on the internet and share their recommendations. Blog posts, social media platforms, product reviews, news articles and discussion forums are excellent sources of business information. These are filled with customers\u2019 opinions and comments that can help assess your brand health if analysed.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">With social listening tools and performance dashboards, such as with Genesys Cloud Social, you can also check the ratio of positive and negative mentions within conversations. You can do this at the brand level, sub-brand level or even more granular as needed. This can also help you identify what aspects customers find helpful as well as where negative sentiments tend to collect.<\/span><span style=\"font-weight: 400;\">\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>Preventing reputation issues:<\/strong> Reputation disasters can happen to any brand. And it\u2019s nearly impossible to predict when your business might fall prey to a reputation crisis.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">But when the brand gets the crisis response wrong, it can go viral for all the wrong reasons. Analysing online sentiments can be your best bet to avoid these crises.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Sentiment analysis<\/span><span style=\"font-weight: 400;\"> is your secret weapon when it comes to managing brand reputation crises. It helps you identify the spikes in negative mentions so you can spot a reputation crisis early and act quickly, leaving lesser chances of spreading it online.<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>Measuring campaign effectiveness:<\/strong> Similar to brand monitoring, you can track the mentions of your marketing campaigns and identify how users perceive your message by measuring the overall sentiment of their comments, shares, likes and posts.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This is especially useful if you\u2019re launching a new product or service. For example, if you\u2019ve launched a new product and noticed a sudden spike in your customers\u2019 sentiments, it might be because of something related to your new product or how you marketed it. You can also adjust your settings so that you can get even more granular. If your company is wondering how a specific feature for a new product is being received, you can track sentiment for that specific product and one of its features based on keywords.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">So, track your marketing campaigns as they launch to find out your customers\u2019 sentiments. This way, you can learn what\u2019s resonating and what\u2019s not with their needs and preferences and tailor your future campaigns accordingly.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>Performing competitor analysis:<\/strong> Don\u2019t only analyse your own sentiments. To take your brand\u2019s online presence to the next level, keep an eye on your competitors, too.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Analysing your competitors will help you know what aspects of their products receive several positive or negative reactions. Plus, you\u2019ll be able to see what\u2019s working for them and what\u2019s not, so you can repeat their successes and avoid their failures.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Competitor sentiment analysis can also serve as a benchmark when you analyse your customers\u2019 sentiments behind your brand. For example, if 40% of your mentions are positive, 35% are negative and the rest are neutral, competitor analysis can help determine whether it\u2019s a good thing or something that needs improvement.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>Improving customer experience:<\/strong> Finding out what your customers feel about your brand, products and services is essential for your business. This is where sentiment analysis comes in.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">It helps you monitor and understand customers\u2019 opinions and feedback about your products and campaigns the moment they\u2019re launched. Further, it helps you identify positive mentions conveying what they liked about your product and negative mentions showing bad reviews and problems customers are facing.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">If you catch these negative comments early, chances are you can act on relevant suggestions quickly for this specific customer and improve the experience for others too. <\/span>[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][vc_row_inner][vc_column_inner][vc_column_text css=&#8221;&#8221;]<\/p>\n<h3><span style=\"font-weight: 400;\">How Does Sentiment Analysis Work?<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Now that you\u2019re aware of what sentiment analysis is and how it is used by brands, let\u2019s get into the mechanics of how it works.<\/span><span style=\"font-weight: 400;\">\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Sentiment analysis<\/span><span style=\"font-weight: 400;\"> is a form of data science. Or, more specifically, it uses natural processing language (NLP) to identify and classify the pieces of text as positive, negative, or neutral. There are three main approaches to sentiment analysis: supervised machine learning, rule-based system or a combination of both. Let\u2019s look at bit more closely at each.<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>Supervised machine learning (SML):<\/strong> As the name suggests, this model doesn\u2019t rely on manually created rules but on ML techniques. In this case, you have to build labeled datasets (similar to the data you\u2019d like to analyse) to help the machine learn how to classify text based on different emotional tones.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Since the human evaluators already determine the sentiments of the given dataset, the system uses sentiment analysis from the training set to label the new test set. After providing enough relevant data, feed the machine new, unlabeled data to see if it marks it correctly. If not, keep adding more data to increase the analysis\u2019s accuracy further.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">A SML model consists of the following classification algorithms:\u00a0<\/span><\/p>\n<ul>\n<li><span style=\"font-weight: 400;\">Naive Bayes<\/span><\/li>\n<li><span style=\"font-weight: 400;\">Linear regression<\/span><\/li>\n<li><span style=\"font-weight: 400;\">Support vector machines\u00a0<\/span><\/li>\n<li><span style=\"font-weight: 400;\">Deep learning\u00a0<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">The advantage of this approach is that it can create models trained for many different contexts and purposes. For example, the word \u201capple\u201d refers to fruit but also a leading technology company. A machine learning algorithm can be trained to identify what \u201capple\u201d means when the word is used in the sentence. It can then apply this experience to analyse similar cases.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>Rule-based techniques:<\/strong> Unlike supervised machine learning, the rule-based system uses a set of manually crafted rules to identify the text sentiments. These rules may include various NLP techniques, such as stemming, tokenisation, parsing, part of speech tagging and lexicons (i.e., dictionaries of pre-labeled words and expressions).\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Check out the following example:\u00a0<\/span><\/p>\n<p><strong>Word sentiment<\/strong><\/p>\n<ul>\n<li><span style=\"font-weight: 400;\">Great 0.8<\/span><\/li>\n<li><span style=\"font-weight: 400;\">Good 0.5<\/span><\/li>\n<li><span style=\"font-weight: 400;\">Bad -0.5<\/span><\/li>\n<li><span style=\"font-weight: 400;\">Terrible -1<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Here\u2019s how the rule-based system works:\u00a0<\/span><\/p>\n<ul>\n<li><span style=\"font-weight: 400;\">Determine the polarity of a text by using sentiment analysis tools to break it down into individual components, i.e., words and phrases. Then, assign each component a score using lexicons.\u00a0<\/span><\/li>\n<li><span style=\"font-weight: 400;\">Generally, positive words such as good, great, helpful, and useful are assigned a positive score (above 0), whereas words and phrases like terrible, bad, and disappointing would get scored negatively (below 0). Plus, neutral words and phrases are assigned a score of zero.\u00a0<\/span><\/li>\n<li><span style=\"font-weight: 400;\">Next, add up the score for every word and divide the result by the number of words in the text. For example: The ambiance was good, but the food was bad. In this case, the score would be: <\/span><span style=\"font-weight: 400;\"><br \/>\n<\/span><strong>(0.5 + (-0.5)) \/ 2 = -0 \/ 2 = 0<\/strong><span style=\"font-weight: 400;\"><br \/>\n<\/span><span style=\"font-weight: 400;\">Therefore, this text will be labeled as neutral by the system.\u00a0<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">You can also count the number of positive and negative words mentioned in the given text. If the number of positive words is more than the number of negative words, it indicates a positive sentiment and vice versa. Also, if both the numbers are equal, it means a neutral sentiment.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Overall, the rule-based technique is much easier to deploy than machine learning. However, it can take a lot of time and effort to maintain. You have to keep adding new rules, which might also affect the previous results.<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>Hybrid approach:<\/strong> Hybrid systems combine the elements of machine learning and rule-based approach into one system to improve the accuracy of sentiment analysis. Data scientists often use these techniques alongside each other.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">A rule-based model will try to classify the sentiment of the text. If the sentiment is not easily identified (when no or very few words from the sentence are available in the lexicon), a machine learning system will be used to gain better insights.<\/span>[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][vc_row_inner][vc_column_inner][vc_column_text css=&#8221;&#8221;]<\/p>\n<h3><span style=\"font-weight: 400;\">Is Sentiment Analysis Really Accurate?\u00a0<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">As sentiments are often subjective, it\u2019s hard to expect a sentiment analysis tool to be 100% accurate. So, let\u2019s explore the major challenges that affect the accuracy of a sentiment analysis model.<\/span><span style=\"font-weight: 400;\">\u00a0<\/span><\/p>\n<h5><span style=\"font-weight: 400;\">Irony and sarcasm<\/span><\/h5>\n<p><span style=\"font-weight: 400;\">In many cases, people often use negative words to express positive sentiments. This can be difficult for the machines to detect without knowing the context of the situation.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Consider these two situations:\u00a0<\/span><\/p>\n<ul>\n<li><span style=\"font-weight: 400;\">The order is only 30 minutes late again. What a great start to the day!\u00a0<\/span><\/li>\n<li><span style=\"font-weight: 400;\">The order is only 5 minutes late again today. What a great start to the day! <\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Obviously, an order that\u2019s 30 minutes late won\u2019t make someone\u2019s day better. But this can be tricky for machines to understand.\u00a0<\/span><span style=\"font-weight: 400;\">\u00a0<\/span><\/p>\n<h5><span style=\"font-weight: 400;\">Negations<\/span><\/h5>\n<p><span style=\"font-weight: 400;\">Negations are a way of reversing the meaning of a word, phrase or sentence. For example, \u201cI wouldn\u2019t say the hotel stay was particularly good.\u201d For accurate sentiment analysis, it\u2019s crucial to identify the negation and detect which word or phrase is affected by it.\u00a0<\/span><\/p>\n<h5><span style=\"font-weight: 400;\">Multipolarity\u00a0<\/span><\/h5>\n<p><span style=\"font-weight: 400;\">Often, a sentence will express different emotions at once \u2014 it might speak positively to one topic or object but negatively to another. For example, \u201cThe pizza place on 2nd Avenue is much better than the pizza place on 4th Street.\u00a0 This sentence says positively about the pizza shop on 2nd Avenue and negatively about the one on 4th Street.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">A sentiment analysis system will consider the sentiment to be positive, considering the \u201cmuch better\u201d phrase. However, if you\u2019re analysing the mentions for the pizza shop on 4th Street, you might disagree. Therefore, you\u2019ll need more sophisticated programming to score this sentence accurately.<\/span>[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][vc_row_inner][vc_column_inner][vc_column_text css=&#8221;&#8221;]<\/p>\n<h3><span style=\"font-weight: 400;\">Classification of Sentiments<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">\u00a0<\/span><span style=\"font-weight: 400;\">Sentiments can be classified into three main categories:\u00a0<\/span><span style=\"font-weight: 400;\">\u00a0<\/span><\/p>\n<p><strong>1. Negative sentiments brands need to watch out for<\/strong><\/p>\n<p><span style=\"font-weight: 400;\">Negative sentiments include brand mentions that have negative words\/sentences attached to them. Many people believe that the presence of negative sentiments means that the campaign, product, or service is a failure.<\/span><span style=\"font-weight: 400;\">\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">However, that\u2019s not always the case. Such sentiments can also indicate opportunities for business. For example, they could mean that something is off and you need to find a solution to fix it.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Examples include:<\/span><\/p>\n<ul>\n<li><span style=\"font-weight: 400;\"><strong>Sadness:<\/strong> This happens when a customer is not satisfied with the purchase or if you haven\u2019t replied to their request. For example, \u201cYour response time is really slow. I\u2019ve been waiting over two weeks.\u201d<\/span><\/li>\n<li><span style=\"font-weight: 400;\"><strong>Anger:<\/strong> When a customer talks about how bad your product or service is. For example, \u201cYour support team is useless.\u201d\u00a0<\/span><\/li>\n<li><span style=\"font-weight: 400;\"><strong>Comparison:<\/strong> When a customer compares your product or service with that of the competitor\u2019s.\u00a0<\/span><\/li>\n<\/ul>\n<p>[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][vc_row_inner][vc_column_inner][vc_column_text css=&#8221;&#8221;]<strong>2. Positive sentiments brands need to monitor\u00a0<\/strong><\/p>\n<p><span style=\"font-weight: 400;\">Positive sentiments include brand mentions that have positive words\/sentences attached to them. Customers have positive feelings about your brand and want to recommend your products or services to their loved ones. The more positive brand mentions, the better your brand is perceived in the customers\u2019 eyes.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Examples include:\u00a0<\/span><\/p>\n<ul>\n<li><span style=\"font-weight: 400;\"><strong>Happy:<\/strong> When a customer loves your product or service and shares their experience with their friends, saying how amazing it was. For example, \u201cGreat service at an affordable price. We\u2019ll definitely try this again.\u201d<\/span><\/li>\n<li><span style=\"font-weight: 400;\"><strong>Extremely satisfied:<\/strong> When a customer recommends your product or service to their friends and family. Such customers are more likely to become your brand advocates. For example, \u201cI really like how easy this product is to use and how it successfully helps manage my team \u2014 clearly, one of the best team management tools I\u2019ve used.\u201d<\/span><\/li>\n<\/ul>\n<p>[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][vc_row_inner][vc_column_inner][vc_column_text css=&#8221;&#8221;]<strong>3. Neutral sentiments brands can work on to turn these people into positives\u00a0<\/strong><\/p>\n<p><span style=\"font-weight: 400;\">Neutral sentiment is neither positive nor negative. This means that such brand mentions don\u2019t contain any words\/phrases that can be classified as positive or negative. Instead, they just state some facts.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Such sentiments offer an excellent opportunity for brands to reach out to customers and ask them for detailed feedback and how their experience was.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Examples include:\u00a0<\/span><\/p>\n<ul>\n<li><span style=\"font-weight: 400;\"><strong>No comments:<\/strong> When a customer seems satisfied with your product or service but doesn\u2019t express their feelings clearly. In simpler words, it happens when the customer writes neither good nor bad about your brand. For example, \u201cGood job, but I expect a lot more in the future.\u201d Or \u201cThe movie was incredibly unpredictable.\u201d<\/span><\/li>\n<\/ul>\n<p>[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][vc_row_inner][vc_column_inner][vc_column_text css=&#8221;&#8221;]<\/p>\n<h3><span style=\"font-weight: 400;\">Why You Need Sentiment Analysis<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">In times when customers come first, and they give their feedback more openly than ever before, creating products and campaigns that appeal to your audience and make you stand out among others is crucial. This is where sentiment analysis comes into play.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Sentiment analysis is a valuable metric for businesses as it provides real-time feedback from your customers. Plus, it enables you to uncover your audience\u2019s emotions about your brand, which can make your product, service or campaigns more effective.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">No matter what stage your business is at, sentiment analysis should be an essential strategy to take your brand\u2019s online presence to another level. Read this guide to learn more about about <a href=\"https:\/\/www.genesys.com\/en-gb\/resources\/genesys-cloud-social-product-overview?ost_tool=blog&#038;ost_campaign=0-0-0-0-0-0-0-blog\" target=\"_blank\" rel=\"noopener\">how to integrate social listening into your customer experience strategy.<\/a><\/span>[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][vc_row_inner][vc_column_inner][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row]<\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>[vc_row][vc_column][vc_column_text css=&#8221;&#8221;]Social media is a valuable tool in customer experience. According to \u201cThe State of Customer Experience\u201d report from Genesys, 27% of consumers surveyed have used social media for a service interaction in the past 12 months. And about one-third say they\u2019d mention a service interaction with a brand on social media when they\u2019ve had [&hellip;]<\/p>\n","protected":false},"author":1010,"featured_media":571178,"template":"","tax_priority":[54],"tax_blogtype":[17751],"tax_blogcategory":[13125,13232],"tax_contenttheme":[14850],"tax_bundle":[],"tax_contenttheme2":[16186],"tax_capability_sitewide":[16260],"tax_products_programs":[17531],"tax_buying_job":[16658],"tax_buyer_persona":[16881,16900],"tax_sector":[],"tax_segment":[17096,17121,17123],"class_list":["post-579931","blog","type-blog","status-publish","has-post-thumbnail","hentry","tax_priority-54","tax_blogtype-genesys-en-gb","tax_blogcategory-cloud-en-gb","tax_blogcategory-digital-en-gb","tax_contenttheme-improve-customer-experience-en-gb","tax_contenttheme2-level-up-your-technology-en-gb","tax_capability_sitewide-digital-en-gb","tax_products_programs-genesys-cloud-cx-en-gb","tax_buying_job-job-2-solution-exploration-en-gb","tax_buyer_persona-business-en-gb","tax_buyer_persona-technical-en-gb","tax_segment-enterprise-en-gb","tax_segment-midsized-en-gb","tax_segment-smb-en-gb","tax_content_type-blog-en-gb"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.genesys.com\/en-gb\/wp-json\/wp\/v2\/blog\/579931","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.genesys.com\/en-gb\/wp-json\/wp\/v2\/blog"}],"about":[{"href":"https:\/\/www.genesys.com\/en-gb\/wp-json\/wp\/v2\/types\/blog"}],"author":[{"embeddable":true,"href":"https:\/\/www.genesys.com\/en-gb\/wp-json\/wp\/v2\/users\/1010"}],"version-history":[{"count":4,"href":"https:\/\/www.genesys.com\/en-gb\/wp-json\/wp\/v2\/blog\/579931\/revisions"}],"predecessor-version":[{"id":579936,"href":"https:\/\/www.genesys.com\/en-gb\/wp-json\/wp\/v2\/blog\/579931\/revisions\/579936"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.genesys.com\/en-gb\/wp-json\/wp\/v2\/media\/571178"}],"wp:attachment":[{"href":"https:\/\/www.genesys.com\/en-gb\/wp-json\/wp\/v2\/media?parent=579931"}],"wp:term":[{"taxonomy":"tax_priority","embeddable":true,"href":"https:\/\/www.genesys.com\/en-gb\/wp-json\/wp\/v2\/tax_priority?post=579931"},{"taxonomy":"tax_blogtype","embeddable":true,"href":"https:\/\/www.genesys.com\/en-gb\/wp-json\/wp\/v2\/tax_blogtype?post=579931"},{"taxonomy":"tax_blogcategory","embeddable":true,"href":"https:\/\/www.genesys.com\/en-gb\/wp-json\/wp\/v2\/tax_blogcategory?post=579931"},{"taxonomy":"tax_contenttheme","embeddable":true,"href":"https:\/\/www.genesys.com\/en-gb\/wp-json\/wp\/v2\/tax_contenttheme?post=579931"},{"taxonomy":"tax_bundle","embeddable":true,"href":"https:\/\/www.genesys.com\/en-gb\/wp-json\/wp\/v2\/tax_bundle?post=579931"},{"taxonomy":"tax_contenttheme2","embeddable":true,"href":"https:\/\/www.genesys.com\/en-gb\/wp-json\/wp\/v2\/tax_contenttheme2?post=579931"},{"taxonomy":"tax_capability_sitewide","embeddable":true,"href":"https:\/\/www.genesys.com\/en-gb\/wp-json\/wp\/v2\/tax_capability_sitewide?post=579931"},{"taxonomy":"tax_products_programs","embeddable":true,"href":"https:\/\/www.genesys.com\/en-gb\/wp-json\/wp\/v2\/tax_products_programs?post=579931"},{"taxonomy":"tax_buying_job","embeddable":true,"href":"https:\/\/www.genesys.com\/en-gb\/wp-json\/wp\/v2\/tax_buying_job?post=579931"},{"taxonomy":"tax_buyer_persona","embeddable":true,"href":"https:\/\/www.genesys.com\/en-gb\/wp-json\/wp\/v2\/tax_buyer_persona?post=579931"},{"taxonomy":"tax_sector","embeddable":true,"href":"https:\/\/www.genesys.com\/en-gb\/wp-json\/wp\/v2\/tax_sector?post=579931"},{"taxonomy":"tax_segment","embeddable":true,"href":"https:\/\/www.genesys.com\/en-gb\/wp-json\/wp\/v2\/tax_segment?post=579931"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}