In this blog, we read about 10 such sources of sentiment analysis data that can be strategic in shaping your machine learning-driven business strategy. Insights from sentiment analysis data sources help a business identify how customers feel about its products and services. There are ten main sources from which relevant sentiment analysis data can be gathered for analyzing sentiment. News articles and videosArticles and videos from news websites are an apt source of sentiment analysis data for various business needs, especially brand reputation monitoring. In fact, it is for the very purpose of voice of the customer analysis that these calls are recorded.

Now, more than ever, businesses need to make sense of online reviews and analyze what customers are trying to tell them. And, they can simply do this by using AI-powered text analytics and sentiment analysis. Here, we’ll understand the process of sentiment analysis on reviews and how it helps businesses improve their products and services. When brands or businesses have voluminous reviews across multiple sites, extracting and analyzing them for sentiment becomes daunting and time-consuming. A reliable sentiment analysis API is used to find insights and relationships within the textual data.

Reports confirm that almost 92% of customers refrain from using a business with negative online reviews. What is Online Reputation Management? How Social Media Listening Can Help in Boosting ORM? Keep Track of Your CompetitionYou can gain so much more than a hunch about your competitors’ tactics by using social media listening. Read the complete article at: Tips to Boost your ORM with Social Media Listening

After all, companies understand that video content is essential for a brand’s social media account success. In a recent study, it was found that 51% of United States adults started using social media more because of the pandemic. Also, by integrating social media video analytics, your creative juices flow through the presentation of fresh, cutting-edge content and strategies. To better understand how social media video analytics works, let’s have a quick rundown of some of the data extraction software and APIs available. How is Social Video Analysis Done?

In this article, we talk about what brand experience means in new-age marketing techniques and what machine learning (ML) processes are involved in brand experience analytics. As a marketing function, brand experience is based on product experience, customer experience, and brand management, and therefore is often-times used to predict consumer behavior. On an overall business level, customer brand experience constitutes brand involvement, brand personality, the customer’s personal feelings - delight, passion, or familiarity - with a brand, which ultimately translates into sales conversions. We analyze customer brand experience by identifying and analyzing sentiment in customer experience data through machine learning algorithms. An AI-driven customer experience platform analyzes brand experience using many ML subtasks such as natural language processing (NLP), named entity recognition (NER), etc.

But, first, let’s look at the real-world example of text mining. Electronic Health RecordsIn the healthcare industry, text analytics help tremendously in patient management and engagement – right from analyzing patient history to their dosages and responses. Client Nurturing To analyze customer service tickets, call logs, client forums, surveys, social media platforms, emails, tweets, and news feeds, text mining is used. Cyberbullying and Threat Detections Text mining helps in ensuring that community standards are followed in the comments and tweets on your social channels. Besides, keywords can be categorized as red flags and the text analysis engine will choose them, including synonyms or misspelled words that sometimes dodge the engine.

Commonly used CX metricsData sourcesMachine Learning & CX analyticsWhat Is Customer Experience Analytics? The Difference Between CX Analytics and CX InsightsOften, CX analytics and CX insights are used interchangeably. While CX analytics is the mathematical processing of customer feedback data by an AI algorithm, customer experience (CX) insights are the findings that are received and inferred from the results provided by a CX analytics tool through its visualization dashboard. Commonly Used Customer Experience MetricsCompanies commonly use four types of metrics or scorecards for understanding customer experience. Read more at: CX Analytics & Insights

” Well, get ready for the ride because we are about to share the secret – “Sentiment Analysis. However, unlike YouTube, Twitch users can also sell subscriptions or offer perks for high-level membership. Integrating semantic analysis, brings a more targeted and qualified market for your business outreach. You can also extract the basic profile information from Twitch, build customer persona sets, and see how they can match your ideal target market. An entrusted video content analysis is all you need to discover what Twitch users are thinking.

Here, using the benefits of VoC (Voice of Customer) comes in the big picture to retain happy customers and turn potential ones into loyal customers. After all, it’s a powerful business tool that makes it easy to understand what customers need and think of your brand. That’s where VoC benefits help you stay attentive to customer cues that were not visible before. To that end, the best fit solution comes in the disguise of VoC, especially social media listening programs to clutch data as much as possible. For instance, companies looking to improve its pharma services can optimize VoC benefits and analyzing feedback from employees and patients.

But, the good part is that you can address this challenge with AI-powered sentiment analysis. You can better analyze the survey results in a fast, precise, and easy manner. Moreover, human prejudice, non-conducive interview methods, incorrect sample size, and the concern of verbose responses to open-ended questions also pose a roadblock in inefficient survey result analysis. This often illustrates when a rating methodology is used in survey data analysis. In this, an AI algorithm uses the rating method to semantically score survey data and predict answers depending on what answers the survey respondent has chosen.

The Voice of the employee helps in:Find Pain PointsOne of the unskippable advantages of VoE is to identify problems and issues in your business processes that disrupt your efficiency. Here, VoE allows employees to share their concerns over a platform with their managers and leaders for process improvements. Makes Organization Flexible as well as DynamicIn post-COVID times, the voice of employee importance has grown by leaps and bounds more than ever before. Offers a Competitive AdvantageThe Voice of an employee helps you learn a lot about not just yourself but also your competitors. Therefore, most organizations now realize how VoE helps in improving organizational performance.

Moreover, during the Customer Experience Professionals Association Event, it became apparent that many companies are beginning to look at how the Customer Experience (Cx) and Employee Experience (Ex) are related. The employee experience is the journey an employee takes with your organization. How Voice of the Employee Can Improve Employee Experience? Get Valid Data: Feedback, Surveys, Interviews and DiscussionOne of the biggest challenges leaders face when implementing a VoE program is getting honest employee feedback. Read more at: How is VoE Changing the Employee Experience Strategy?

Follow up) feedback loop helps you walk through it with the best possible customer feedback strategy. This process helps you track the pulse of customer opinion, gain customer loyalty by closing feedback loops and use both to inform strategy and stay ahead in trends. So, by requesting customer feedback in the form of surveys ensure your customers feel cared for and get the satisfaction of being heard. Now, categorize your customer feedbackAfter identifying your data, you can proceed to perform cross-tabulation and other sub-grouping analyses on the quantitative data. Surely, this will take some effort to build but will satisfy Android users, likely a significant customer segment.

But, with sentiment analysis today, brands can translate those feelings into actionable business data with ease. That’s why using an Instagram sentiment analysis, can help you gauge aspects like brand awareness, and brand image and predict consumer behavior. Here in this blog, we will shed light on how to implement sentiment analysis on Instagram, and how it can benefit you. The primary formats for getting sentiment analysis on Instagram includes posts, comments, hashtags, videos, and Instagram TV. Further, they are processed for sentiment analysis presenting data on a dashboard.

The Sentiment Analysis ProcessWith the complex patterns of algorithms working simultaneously and in conjunction with each other, the process in which the sentiment analysis API systems actually works is in straightforward steps. A sentiment analysis API helps you monitor these hidden sentiment trends so you are not caught unawares. Monitor sentiment drivers- The fundamental question for any SaaS product manager to ask would be what is the single most important factor that makes your SaaS product attractive to buyers. Go beyond metric-based surveys - Sentiment analysis SaaS empowers you to go beyond net promoter scores, star ratings, and other numerical-based customer satisfaction scores. Read the complete article at: Guide to Sentiment Analysis APIs

It’s easy to analyze video data for crucial information, organize it semantically, and also extract customer insights to quickly solve these concerns. Outlining Video IntelligenceVideo intelligence is the process of analyzing every frame of video content to extract valuable insights from it. Intelligent video analytics allows you to recognize and analyze places, people, and objects along with text and logos in video content. Not to forget, this is also beneficial in jotting down video from a video, especially in educational settings. But, there are many more ways that video intelligence can be applied in a business that is all set to grow.