One of the most salient trends to emerge in Business Intelligence (BI) is social BI, which focuses on social media analytics and presents results in easily consumable ways to create action. There are many different aspects of social media, analytics, and marketing that social BI both encompasses and improves.
Certain irrevocable changes have taken place in both the data and business landscapes that have altered the way that businesses and their customers interact with one another. The immediacy and ubiquity of social media have helped spur the need for advances in social BI. There are multiple ways of accessing social Business Intelligence and an ever broadening customer base that can take advantage of its insight—and the opportunities it provides.
Perhaps the most readily accessible form of Big Data across vertical industries is that created by social media and which requires various forms of analytics to provide quantifiable and qualitative analyses of sentiment data. The impact of social media on Business Intelligence is considerable. Conservative estimates indicate that in the next four years there will be “8.25% CAGR for the BI market driven by the emergence of social media and social business intelligence.” More liberal prognostications reveal that “the social media analytics market will grow from $620.3 million in 2014 to $2.73 billion in 2019, at a CAGR of 34.5%.”
Regardless of the specific statistics in this field, one can readily confirm the fact that data produced by social media is growing horizontally and vertically. Not only are there increasing numbers of customers and potential customers interacting with one another and businesses through such media, but the very nature of that media is expanding. Social media can take any form of tweets, videos, images, micro-videos (Vines), GIFs, geographic data, and personal metrics. When one factors in the realities of e-commerce, it is quite possible that social media and online platforms represent the preferred method of customer interaction with organizations, and have created a market ripe for data mining and monetization.
Applications of Social Business Intelligence
The actual form and function of social Business Intelligence resemble those of other types of Business Intelligence. The objectives are still to elucidate key performance indicators while analyzing overall performance via a bevy of tools for reporting, dashboards, visualizations, and others. But if the tools are mostly the same, the technologies that make them work have evolved. Consequently, there are marked differences in:
- Time to insight: Whereas typical BI was a time consuming process involving IT, the instantaneous nature of Big Data and self-service tools have drastically reduced time to insight.
- Structure: Conventional Business Intelligence was based on structured data in tabular, relational formats. The vast majority of data analyzed by social BI tools is unstructured.
- Applications: Although there is no shortage of applications for social BI, most tend to focus on various aspects of marketing, product development and placement. These and other aspects of customer interaction are more timely and future oriented than the typical historical reports of older BI systems.
Furthermore, social Business Intelligence is largely preoccupied with the monitoring and capturing of rapidly developed Big Data for sentiment analysis. This analysis influences applications such as brand recognition and image maintenance, marketing and advertising campaign customization, and determines critical influences for products, services, and public opinion.
One of the most viable applications of social Business Intelligence associated with its penchant for segmenting customers, and the various facets of products and services that influence them is its ability to presage and create trends among one’s customer base. The incorporation of predictive analytics, prescriptive analytics, and Machine Learning algorithms can align with analysis to determine future behavior and offer a fair amount of insight about it. Social Business Intelligence is substantially enhanced by these analytics options; it can be used to reduce the rate of churning and to boost the rate of converting prospects to customers. Its propensity to do so is largely based on the numerous ways which it can stratify and identify trends related to customer behavior and product popularity. When such data is combined with business drivers, it is possible to predict customer behavior via a series of recommendations.
The implementation options for Social BI tend to mirror those for most Big Data analytics. There are numerous advantages to deploying these tools in the Cloud including lower infrastructure costs and remuneration models in which customers only pay for the time they use services. Numerous vendors offer social Business Intelligence capabilities as a means of delivering Big Data analytics targeted towards sentiment analysis for social media platforms. Frequently, these services are rendered as SaaS or PaaS offerings.
However, another accessible means of leveraging social BI is to utilize third-party vendors who perform a number of services in-house. These include aggregating, analyzing, forecasting, and curating data to deliver the sort of insights that this form of analytics can produce. Frequently, all customers need to do is simply issue their data to such companies in order to reap the benefits of this Cloud-based application. These Cloud options are particularly attractive for small and medium sized enterprises (SMEs) who would not otherwise have the resources to exploit Big Data analytics for social media.
Due to the reduced overhead costs, attractive pricing models, and the lack of technical or even analytical skills required of certain Cloud options, one can argue that they are instrumental in democratizing Big Data analytics for social media. Even the least technologically savvy organization can use these services and help level the playing field with larger organizations with much more capital to devote to cutting-edge Data Management technologies. Furthermore, since sentiment analysis of social media data is one of the points of commonality between organizations regardless of their size or resources, it is an area in which smaller organizations can readily rival their larger counterparts. A recent article indicates the natural relationship between social Business Intelligence and SMEs: “…this type of technology can be a windfall for small businesses especially, since small-to-medium-size businesses typically don’t have the budget to market outside of social media.” By leveraging the aforementioned Cloud resources, however, these organizations can ensure that they are still able to utilize Big Data analytics for sentiment analysis as well as any large, nationwide enterprise.
From Customer Interaction to Customer Engagement
The growing focus on social Business Intelligence reflects the burgeoning importance of social media in the way that businesses and customers interact with one another. Prior to the advent of social media, organizations made a considerable effort to interact with customers via marketing measures that did not always produce their desired effects. Nonetheless, the ready availability of social media and the instant accessibility it provides consumers has shifted the organizational focus to one of customer engagement. In most cases, the goal is to sustain and benefit from the nearly constant interactivity that social media facilitates.
Social Business Intelligence plays a substantial role in giving organizations the tools and technology they need to both keep abreast of their various online means of customer engagement and to actually predict or guide it, to a certain extent. By combining this form of Business Intelligence with capabilities for mobile and Cloud technologies, organizations can better control their public perception and their understanding of their customer base.