Social media in the past started merely as communication platforms. It served a straightforward function: interaction among friends, families, and even total strangers via the Internet. But today, social media is not this simple anymore. As having at least one or more social media account has become a necessity for many people, doors have also been opened to businesses.
No matter the size of an organization, whether you’re an individual starting up an online shop or a large company bent on expanding your market; social media serves as a powerful tool in establishing an online presence. But that’s only if you know how to leverage social media for your business properly. And with social media being made up of a vast number of platforms, where do you even start?
To save you the headache of thinking where to begin and which path to take, we’ll help you with the first and most crucial step: social media data mining. In this article, we’ll discuss data mining in social media, social media mining techniques, and even provide examples of the best data mining software platforms that you can use in your business strategies.
As the term itself describes, social media data mining is very much like the mining industry. It is the process of extracting precious elements that are not apparent even more so not easily attainable. It’s not gold or diamond this time, but something just as valuable. In this case, it’s all about social media data, and mining the web for such elements can prove vital to the success of your business, especially in view of the latest social media trends.
With studies proving social media effects on the purchase decisions of most consumers, such platforms have become effective tools in marketing and business strategies. According to a recent study by Social Media Examiner, about 93% of businesses reported having increased exposure, while 72% had more sales with the help of social media marketing.
Source: Social Media Examiner (2019)Designed by
This report proves the importance of gathering consumer data from social media, otherwise known as social media data mining. Its main goal is to collect data from different social media sources and find new information that may be hidden. With different social media data mining techniques, you’ll have a better understanding of how people react to a certain topic and gain new insights about consumer behavior.
That being said, let’s get into a more detailed discussion of social media data mining techniques. At the same time, we’ll give you an idea of how you can achieve the maximum benefits from a variety of social media data collection methods.
Data mining can help us understand large sets of data. There are two approaches when it comes to data mining–supervised and unsupervised approaches. These approaches provide algorithms that you can use in identifying the hidden patterns in your data. The supervised approach depends on the inferred knowledge from previous data. Meanwhile, an unsupervised approach automatically characterizes data by classifying them into similar elements.
One example of an unsupervised approach in data mining is clustering. In clustering algorithms, the given data is characterized without any prior instruction as to what kinds of patterns the algorithms will generate. In other words, it partitions similar elements in a data set into one homogenous group. The main function of this algorithm is to segregate groups with similar elements and turn them into clusters.
For the supervised approach, however, we have, for example, the classification technique. In this technique, the algorithm learns from training data. It then automatically categorizes the newly found data into the distinct classes generated by the first set of the data that was previously gathered.
The data collected from social media platforms can be useful in identifying social media trends and gaining new and valuable insights for your business. This data can also be used to make marketing and engagement strategies. Social media data, by showing you how users are engaged by your content, can aid you in making decisions such as determining which group of users you need to communicate the most with or which among them should receive marketing emails.
Types of social media data vary widely. Facebook has a number of likes, followers, or shares. On Twitter, it’s the number of impressions or retweets, and on Instagram, you can find the hashtag usage and engagement rates in the raw data. The necessary business intelligence analyst skills sure can help, but for starters, these are the kinds of data that you’ll need to collect and keep in mind so you can begin optimizing your social content, monitoring your brand image, and identifying influential customers.
Source: Buffer (2019)Designed by
Social media data mining software solutions are available in the market, and they make it easier to identify common patterns and the correlation of various data points in large volumes. The main functionality of data mining software platforms is to provide important metrics and formulas that can help you make measurements and comparisons. Data mining, like most business intelligence tools, can also help in identifying the relationships of different business indicators with one another.
The following are examples of data mining software solutions:
Sisense is the leading business intelligence software that can quickly turn your data into valuable insights. The software provides interactive BI dashboards, allowing you to filter, drill down, and further explore your data. With the tools provided by the software, you can easily prepare and analyze big or disparate data sets. Sisense is also an AI-driven business analytics software that comes with an intuitive UI purposely built for a deeper analysis of your data.
With an easy-to-use drag-and-drop web user interface, you can create fully-interactive dashboards and incorporate multiple data sources into a single data model. The platform also has features that allow you to add new data sources to already-governed models so you can quickly test out new ideas.
RapidMiner is a lightning-fast analytics system with more than 1,500 native algorithms, data prep, and data science functions. It provides support to any third-party ML libraries and integration with custom Python and R code. In addition, it is an end-to-end collaboration platform where you can ingest and transform your data from multiple sources.
The software provides a simplified solution accelerator with pre-canned use-case templates. RapidMiner also allows automated selection and validation of the best ML models, as well as deployment and optimization models in production processes. The software can help you drive revenue, cut back on financial costs, and avoid risks. By organizing team-based data science projects, you can also share predictive analytics processes and valuable and repeatable data pipelines to your team.
Microsoft SharePoint is one of the smartest analytical options for corporate and non-commercial users. It works perfectly well in all Microsoft Office products in one seamless integration, offering an array of custom development capabilities. Microsoft Sharepoint allows you to create powerful data models, as well as perform self-service predictive analytics
The software comes with information management and security tools, giving you access to different development scenarios. It can also help you visualize and explore your data. With its mobile compatibility, you can access reports from your mobile devices from anywhere you are.
IBM Cognos is a self-service analytics tool and a smart business intelligence software that helps you determine and turn your data into insights. With IBM Cognos, you can visualize, analyze, and share the insights you get from your business data. The software also comes with strong governance rules to better protect your systems and data.
Additionally, IBM Cognos has a number of beautiful and interactive dashboards, as well as compelling reports all packed in a single platform. The software is cloud-based, so everyone in your team won’t have a hard time accessing the data that they need.
Dundas BI is a data analytics platform that provides dashboards, multi-page reports, and visual data analytics for the personalization of your reports and the creation of actionable insights from your data. The software can perform ad-hoc queries and generate reliable reports from the gathered data. Dundas also comes with intuitive drag-and-drop functionality to help you review your data and insert your own data files.
With an exploration designer tool, Dundas BI lets you share design options with your team members. You can also have better control of your dashboards, reports, as well as the scorecard views of your data.
Social media data have three challenging qualities: it is large, noisy, and dynamic. Thus, applying social media data mining techniques can make your BI processes much easier. As social media grows ever more popular in being part of business and marketing strategies, data mining techniques can help you improve your search results in search engines. It even allows you to find new insights from your data. Moreover, it can guide you in personalizing services for your customers.
Here are some examples of data mining techniques:
Association is one of the most basic techniques in data mining. In this data mining technique, you need to use machine learning models. This comes handy in analyzing data, finding the patterns, and identifying co-occurrences in one set of databases. It is dependently linked to several variables in a data set. Furthermore, the association technique consists of if-then patterns and, thus, has two parts. First is the antecedent “if,” where an item is found in the data. The second part is the consequent “then,” which is an item found in combination with the antecedent.
Classification is used in various fields, although it is quite a complicated data mining technique. It requires you to collect various attributes in a data set and combine them together into discernible categories. You can then gain insights and draw conclusions from the generated classifications of the data attributes. The decision tree induction, Bayesian networks, and k-nearest neighbor classifier are some major kinds of classification methods.
Tracking patterns is a data mining technique that specifically identifies the rules and patterns in the data based on their relational attributes. This technique involves the identification of anomalies present in your data at regular intervals. It can also point out the ebb and flow of a certain variable in a data set. For instance, when determining which seasons a certain product is most widely sought by your customers, this technique comes very handily.
The prediction technique in social data mining is used in projecting the kinds of data you’ll most likely see in the future. In this technique, you will be covering the predictor and predicted variables from your data. This also involves recognizing trends and patterns and generating predictions from them. Reviewing consumer behavior, for example, can help you expect the future demands of your customers.
Most prediction techniques have mathematical models as their basis. These include simple statistical models like regression, non-linear statistics like power series, as well as neural networks, RBFs, and more.
Source: State of Social (2018)Designed by
Through social media data mining and analytics, you can harness the power that social media data brings to your business. Data mining and data analytics, however, are two different subsets of business intelligence. They are two very close elements, but they have a number of differences too.
Data mining is more of a systematic process of discovering and identifying information. It also allows you to see the hidden patterns in a large data set. Meanwhile, data analysis is like a combined and deeper form of data mining. It’s a superset that involves the processes of extracting, sorting, transforming, organizing, and visualizing data to uncover the otherwise hidden and meaningful information from the data.
While data mining is the process of extracting data from a specific set of patterns from large data sets, data analysis is the process of not only gathering but also organizing the raw data to turn them into actionable insights. When you combine them, however, you’ll get a powerful tool that can predict consumer behavior. This way, you can identify consumer trends and patterns, leading to better sales.
Association, classification, tracking patterns, and predictions–these are the important social data mining techniques that you should know by now. That is if you want conversions from your social media engagements. Being proactive on your social media accounts in engaging and interacting with your customers is a great practice. However, there’s so much more that you can do to stay ahead of your competitors. Sure, social data mining is yet another task on top of all your marketing plans, but you’ll soon find that the extra effort is well worth it.
Social media platforms present endless possibilities and opportunities, so it’s important to know how to leverage the data you can get from them. These social media data mining tools do not exist for no reason at all. Such tools and techniques can provide you the insights that you need for data-driven business decisions. You can also check out the best data analytics software platforms that can help you understand your customers more and, thus, increase your revenues.
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