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Social Media Analytics Software Definition

Social media analytics software definition is just one of the many important keywords people are searching for on a daily basis. In fact, Google itself is talking about this keyword on their blog titled The Rise of Social Media Analytics Software.

What exactly IS social media analytics software? Believe it or not, there are many definitions for the term. For example, a lot of people (including myself) thought social media analytics was simply using some type of plugin to track the number of page visits and their duration, as well as email capture statistics…but I was wrong. In this article, I’m going to use my SEOTipRealtor skills and share with you a bunch of different definitions I’ve collected from other SEOs as well as describe what I think are the most important features that should be included in your definition.

What Does Social Media Analytics Tools Mean?

Social media analytics tools are pieces of web application analysis software that are used to monitor, assess and consequently improve social media performance. They are simply a subset of web analytics tools that are designed to gather and make sense of web performance data produced by social media sites and platforms, and consists of the usual graphical dashboard and data visualization techniques that give the user a clear understanding on the performance of their social media presence.

Techopedia Explains Social Media Analytics Tools

Social media analytics tools are essential in running a successful social media campaign. It allows social media experts to track and determine the performance of various portions of the social marketing campaign such as sales, customer service and sentiment analysis.

In terms of sales, these tools show how well a social media marketing campaign is going by showing all positive turnovers or purchases that come directly from social media sources such as Facebook and Twitter. These sites are useful for disseminating purchase or signup links and correlate directly to traffic which can be picked up by a specific social media analytics tool. For brand recognition and sentiment analysis, some tools are able to mine the data from social networking sites in order to find or discover the sentiment of people towards a brand or business through methods such as natural language processing and pattern recognition.

Common methods of analysis used:

  • Data mining
  • Transformation
  • Natural language processing
  • Data pre-processing
  • Data visualization
  • Hidden pattern evaluation
  • Traffic analysis

Examples of social media analytics tools or platforms:

  • Google Analytics
  • Twitter Analytics
  • Facebook Insight
  • Hootsuite

Why is social media analytics important?

IBM points out that with the prevalence of social media: “News of a great product can spread like wildfire. And news about a bad product — or a bad experience with a customer service rep — can spread just as quickly. Consumers are now holding organizations to account for their brand promises and sharing their experiences with friends, co-workers and the public at large.”

Social media analytics helps companies address these experiences and use them to:

  • Spot trends related to offerings and brands
  • Understand conversations — what is being said and how it is being received
  • Derive customer sentiment towards products and services
  • Gauge response to social media and other communications
  • Identify high-value features for a product or service
  • Uncover what competitors are saying and its effectiveness
  • Map how third-party partners and channels may affect performance

These insights can be used to not only make tactical adjustments, like addressing an angry tweet, they can help drive strategic decisions. In fact, IBM finds social media analytics is now “being brought into the core discussions about how businesses develop their strategies.”

These strategies affect a range of business activity:

  • Product development – Analyzing an aggregate of Facebook posts, tweets and Amazon product reviews can deliver a clearer picture of customer pain points, shifting needs and desired features. Trends can be identified and tracked to shape the management of existing product lines as well as guide new product development.
  • Customer experience – An IBM study discovered “organizations are evolving from product-led to experience-led businesses.” Behavioral analysis can be applied across social channels to capitalize on micro-moments to delight customers and increase loyalty and lifetime value.
    Branding – Social media may be the world’s largest focus group. Natural language processing and sentiment analysis can continually monitor positive or negative expectations to maintain brand health, refine positioning and develop new brand attributes.
  • Competitive Analysis – Understanding what competitors are doing and how customers are responding is always critical. For example, a competitor may indicate that they are foregoing a niche market, creating an opportunity. Or a spike in positive mentions for a new product can alert organizations to market disruptors.
  • Operational efficiency – Deep analysis of social media can help organizations improve how they gauge demand. Retailers and others can use that information to manage inventory and suppliers, reduce costs and optimize resources.

What are the use cases of social media analytics?

Social media analysis platforms can track and analyze a range of data and interactions used in a variety of social media marketing use cases.

Measure the ROI of social media marketing efforts

The main goal for any social media post, like, retweet or share is ROI.

To determine social media ROI, marketers must first determine an initial benchmark and then have a way to measure key performance indicators (KPIs) against that benchmark over time. When efforts aren’t working well, analysis of those metrics will reveal tweaks marketers can make to improve the performance of the campaign and overall ROI.

In fact, a recent study from Hootsuite, a vendor offering a social media management platform, found that 85% of organizations that began measuring social media data within their analytics tools were able to accurately show ROI for those efforts.

To begin tracking social media campaign performance, a tracking pixel or Google Analytics UTM parameter can be added to any links used in social media posts or ads. That will show any conversions that came from social media marketing and can help with planning retargeting campaigns for visitors who didn’t convert.

Improve strategic decision-making

Social media analytics can improve a marketing team’s ability to understand what social media strategies are working and which ones aren’t as effective.

However, the analytical results can also provide insight that can be useful for making business decisions about other important aspects of the business not necessarily directly related to the marketing campaigns.

For example, with social listening tools, audience and competition can be analyzed by extracting useful insight from social media data being posted on various social media networks like LinkedIn and Facebook. It can also provide demographic information about the audience that will enable enhanced marketing efforts targeting that sector and more effectively create brand awareness.

By using real-time data, emerging trends may be detected that can give a business a jump on the competition by posting social media content sooner.

Track the efficiency of marketing teams

Most organizations strive to streamline workflows and enable team members to be more productive. A lesser known, but still important, feature of social media analytics is its ability to improve efficiency with your marketing team.

In addition to the KPIs for your social media content, you can also measure aspects like response time and customer sentiment.

Showing the chief marketing officer areas where workflows can be automated and resources can be redirected to strategic activities that directly impact revenue are key to obtaining marketing budget and approvals for future campaigns.

Key capabilities of effective social media analytics

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.

  • 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.
  • Segmentation is a fundamental need in social media analytics. It categorizes social media participants by geography, age, gender, marital status, parental status and other demographics. It can help identify influencers in those categories. Messages, initiatives and responses can be better tuned and targeted by understanding who is interacting on key topics.
  • 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.
  • Share of voice analyzes prevalence and intensity in conversations regarding brand, products, services, reputation and more. It helps determine key issues and important topics. It also helps classify discussions as positive, negative, neutral or ambivalent.
  • Clustering analysis can uncover hidden conversations and unexpected insights. It makes associations between keywords or phrases that appear together frequently and derives new topics, issues and opportunities. The people that make baking soda, for example, discovered new uses and opportunities using clustering analysis.
  • Dashboards and visualization charts, graphs, tables and other presentation tools summarize and share social media analytics findings — a critical capability for communicating and acting on what has been learned. They also enable users to grasp meaning and insights more quickly and look deeper into specific findings without advanced technical skills.

Conclusion

The need for Social Media Analytics Software has been steadily on the rise. Business professionals worldwide are looking for an easier way to monitor their social media presence, stay ahead of the competition, and improve their marketing strategies.

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