SOCIAL MEDIA ANALYTICS

                           Social media sites have been with us for more than two decades. The first social media channel was created in 1997 and was called Six Degrees. It allowed users to send messages and post bulletin board pieces. A lot has changed since then – it’s hard to imagine everyday activities without Twitter, Instagram, or Facebook. Social media offers a huge pool of consumers ripe for brand communication. Social is a wonderful place for consumers and brands to connect, as long as they remember one thing: social media may provide your brand’s first and last impression, so both need to be good ones. Many businesses adopt a brand-centric focus when starting out with on their data analytics journey, and that can be dangerous.Social media had also become one of the backbones of marketing. It’s hard to imagine any marketing campaign, even an offline one, without sparking some kind of social media involvement. Social media analytics isn’t about brands. It’s about people sharing their lives with others they know based on common interests. And they resent interruptions, particularly when someone is trying to sell them something! This is where social media analytics comes into play. We’ll break down just what this crucial business tool is; what it isn’t; why you need to use it; and, how!

 

Social Media Monitoring & Reputation Management with Mentionlytics

What Is Social Media Analytics?

                   Social media analysis is the process of collecting the most valuable data from your social media channels and drawing actionable conclusions. Social media analytics (SMA) refers to the approach of collecting data from social media sites and blogs and evaluating that data to make business decisions. This process goes beyond the usual monitoring or a basic analysis of retweets or ‘likes’ to develop an in-depth idea of the social consumer.” When it comes to social media analysis, you can base your data on two sources of information.Social media analysis will help you develop the most successful social media campaign. A campaign that will reach the right audience, at the right time, and on the right channel.

                       Brands often make the mistake of running a social media analysis on a topic once and then call it good. Online is always in a state of flux, so there is an ongoing relationship with the data in social media analytics to account for fluctuations inherent in the medium.The ability to cut through the online noise in pursuit of actionable market, competitive and consumer intelligence, coupled with consistent monitoring to track conversational fluctuations over time is the mark of effective social media analytics.

 What Is Social Media Analytics & Why Is It Important?

Key capabilities of effective social media analytics 

                When it comes to social media analysis, there is a ton of information to gather and analyse. The first step for effective social media analytics is developing a goal. Goals can range from increasing revenue to pinpointing service issues From there, topics or keywords can be selected and parameters such as date range can be set. Sources also need to be specified — responses to YouTube videos, Facebook conversations, Twitter arguments, Amazon product reviews, comments from news sites. It is important to select sources pertinent to a given product, service or brand. 

                       Typically, a data set will be established to support the goals, topics, parameters and sources. Data is retrieved, analyzed and reported through visualizations that make it easier to understand and manipulate.These steps are typical of a general social media analytics approach that can be made more effective by capabilities found in social media analytics platforms.

                         Behavior analysis is used to understand the concerns of social media participants by assigning behavioral types such as user, recommender, prospective user and detractor. Understanding these roles helps develop targeted messages and responses to meet, change or deflect their perceptions.  

                         Sentiment analysis measures the tone and intent of social media comments. It typically involves natural language processing technologies to help understand entities and relationships to reveal positive, negative, neutral or ambivalent attributes. 

                          Natural language processing and machine learning technologies identify entities and relationships in unstructured data — information not pre-formatted to work with data analytics. Virtually all social media content is unstructured. These technologies are critical to deriving meaningful insights.

 social media analysis Archives - Analytics Vidhya

 

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