4 types of social media analytics explained
Depending on the business objectives, social media analytics can take four different forms, namely, descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics.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)
Diagnostic SMA analytics looks into the questions of “why something happened?” For example, while descriptive analytics can provide an overview of your social media marketing campaign’s performances (posts, mentions, followers, fans, page views, reviews, pins, etc); diagnostic analytics can distill this data into a single view to see what worked in your past campaigns and what didn’t. Enablers of diagnostics analytics include inferential statistics, behavioural analytics, correlations & retrospective analysis and outcome being cause and effect analysis of a business issues.
4. 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.
5. 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.
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.
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