Social media analytics is a six step iterative process of mining the desired business insights from social media data (Khan, 2015). At the center of the analytics is the organizational goals and objectives that we want to achieve with social media analytics.

Step 1: Data Source Identification

Data source identification stage is concerned with searching and identifying the right source of information for analytical purposes. Although, most of the data for analytics will come from business-owned social media platforms, such as an official Twitter account, Facebook fan pages, blogs, and YouTube channel. Some data for analytics, however, will also be harvested from nonofficial social media platforms, such as Google search engine trends data or Twitter search stream data.

Step 2: Data Extraction

Once a reliable and mineable source of data are identified, next comes extraction of the data. Most of the large-scale social media data extraction is done through an API (application programming interface). Mostly, the social media analytics tools use API-based data mining. APIs, in simple words, are sets of routines/protocols that social media service companies (e.g., Twitter and Facebook) have developed that allow users to access small portions of data hosted in their databases.

Step 3: Cleaning

Next comes removing the unwanted data from the automatically extracted data. Some data may need cleaning, while other data can go into analysis directly. In the case of the text analytics cleaning, coding, clustering, and filtering may be needed to get rid of irrelevant textual data using natural language processing (NPL).

Step 4: Analyzing the Data

At this stage, the clean data is analyzed for business insights. Depending on the layer of social media analytics under consideration and the tools and algorithm employed, the steps and approach to take will greatly vary. For example, nodes in a social media network can be clustered and visualized in a variety of ways depending on the algorithm employed. The overall objective at this stage is to extract meaningful insights without the data losing its integrity.

Step 5: Visualization

In addition to numerical results, most of the social media data will also result in visual outcomes. Effective visualization is particularly helpful with complex and large data sets because it can reveal hidden patterns, relationships, and trends. It is the effective visualization of the results that will demonstrate the value of social media data to top management.

Step 6: Consumption

While companies are quickly mastering sophisticated analytical methods, skills, and techniques needed to convert big data into information, there seems to a gap between an organization’s capacity to produce analytical results and its ability to effectively consuming it. Effective Consumption of analytics results relies on human judgments to interpret valuable knowledge from the visual data. Meaningful interpretation is of particular importance when we are dealing with descriptive analytics that leaves room for different interpretations.

References

1. Khan G. F., 2015, Seven layers of social media analytics: Mining business insights from social media text, actions, networks, hyperlinks, apps, search engine, and location data, CreateSpace Independent Publishing Platform.

Author info

Dr. Khan, Gohar is a Senior Lecturer at the University of Waikato, New Zealand. He is the Founding Director of the Center for Social Technologies, which investigates strategic, organizational, behavioral, legal, and economic aspects of social technologies. His work on social media and information technology has appeared in several refereed journals, conference proceedings, and books.

Social media data is the new gold and analytics is its digging tool. Social Media Analytics (SMA) is the art and science of extracting valuable hidden business insights from social media media data (Khan, 2015) . SMA turns the vast amounts of semi-structured and unstructured social media data into actionable business insights for informed business decision making.

Types of Social Media Analytics

Depending on the business objectives, social media analytics can take four different forms, namely, descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics.

1.Descriptive Analytics (Is Reactive in Nature)

Descriptive SMA tackles the questions of “what happened and/or what is happening?” Descriptive analytics gather and describe social media data in the form of reports, visualizations, and clustering to understand a well-defined business problem or opportunity. Social media user comments analysis, for instance, falls into the descriptive analytics category. Comment analysis can be used to understand users’ sentiments or identify emerging trends by clustering themes and topics. Currently, descriptive analytics accounts for the majority of social media analytics landscape.

2.Diagnostic Analytics (Is also Reactive in Nature)

Enablers of diagnostics analytics include inferential statistics, behavioural analytics, correlations & retrospective analysis and outcome being cause and effect analysis of a business

issues.

3.Predictive Analytics (Is Proactive in Nature)

Predictive analytics involves analyzing large amounts of accumulated social media data to predict a future event. Thus, it deals with the question of “what will happen and/or why will it happen?” For example, an intention expressed over social media (such as buy, sell, recommend, quit, desire, or wish) can be mined to predict a future event (such as a purchase). Alternatively, businesses can predict sales figures based on historical visits (or in-links) to a corporate website.

4. Prescriptive Analytics (Is also Proactive in Nature)

While predictive analytics help to predict the future, prescriptive analytics suggest the best action to take when handling a scenario (Lustig, Dietrich, et al. 2010). For example, if you have groups of social media users that display certain patterns of buying behavior, how can you optimize your offering to each group? Like predictive analytics, prescriptive analytics has not yet found its way into social media data. The main enablers of prescriptive analytics include optimization and simulation modeling, multi-criteria decision modeling, expert systems, and group support systems.

References

1. Khan G. F., 2015, Seven layers of social media analytics: Mining business insights from social media text, actions, networks, hyperlinks, apps, search engine, and location data, CreateSpace Independent Publishing Platform.

2. Tuncay Bayrak, A Review of Business Analytics: A Business Enabler or Another Passing Fad, Procedia - Social and Behavioral Sciences, Volume 195, 2015, Pages 230-239, ISSN 1877-0428, http://dx.doi.org/10.1016/j.sbspro.2015.06.354.

Author info

Dr. Khan, Gohar is a Senior Lecturer at the University of Waikato, New Zealand. He is the Founding Director of the Center for Social Technologies, which investigates strategic, organizational, behavioral, legal, and economic aspects of social technologies. His work on social media and information technology has appeared in several refereed journals, conference proceedings, and books.