Social media is the most reliable generator of user data. Any channel can generate terabytes of data each day depending on how many users frequent the particular channel. Each point of action on social media generates data. Even a simple login from a user account produces data that marketers can collect and use in different surveys. We will get into the details of such data later, but in a space where each point of contact generates bulks of data, managing, collecting and analyzing data for market analysis becomes a real challenge.

What are your KPIs?

Social media KPIs or key performance indicators can tell you how your followers are reacting to your posts. Logins, likes, shares, clicks, comments, profile visits, traffic, leads, and purchases are your KPIs. They are the only data you need to monitor your brand performance on social media. Platforms like Twitter and Facebook have come up with easy indicators and monitoring tools for KPIs, which have made the job of social media analysts a lot easier.

Using these latest tools, metrics, and reports to rationalize your ROI will tell you how rewarding your social media campaign has been. However, to get a comprehensive view of your investment, social media engagement and ROI, you need to create a complete database that contains all the details mentioned above of your social media campaign and user-generated data. Even a remote database can serve the purpose, as long as you have a qualified senior DBA to take care of the maintenance processes. By identifying your KPIs, you will be able to categorize relevant data in the database. It will also help you to optimize the use of social media in your brand campaign.

The quintessential process of social data analysis

Universal social data analysis is a thorough process. It starts when you define the purpose of the analytics process. It includes the removal of noise and fine-tuning the data from your target audience. It is a big data step that involves analysis of large chunks of data from scattered sources, collated by chosen common factors.

One way to find relevant data is by using social listening tools. Keywords and phrases containing your “search query” will help you find user-end data pertinent to your cause. HootSuite and Social Mention are two of the most popular social media listening tools, which you can use to find relevant user-generated data.

Always remember to stay away from what we refer to as “kitchen sink analysis.” The name is quite obvious! A kitchen sink is a messy place where you have everything, but you cannot find anything. Too much data will make the process very confusing for all data analysts, database administrators and data interpreters. Too little data will not be sufficient to paint the bigger picture. Therefore, you need to find the perfect listening tool and “search query” combination for generating good user generated data.

Create targets based on your platforms

Your KPIs can help in the isolation of unique metrics from different social media platforms. Facebook, Twitter, Instagram, Google+ and LinkedIn have options of linking analytics tools to generate data. In fact, Facebook, Twitter, and Google have native analytics tools which can efficiently analyze data for the brands on these social media platforms. Facebook Insights, Twitter Analytics, and Google Analytics play a significant role in the daily analysis of social media-generated data.

These native tools are considerably evolved, and they offer many features including, but not limited to –

  • Twitter Analytics – this is capable of showing the engagement rate, link clicks, mentions, retweets, number of followers and more over a 28 day period.
  • Google Analytics – this can provide metrics over many social media platforms that include sales, visits, duration, and leads.
  • Facebook Insights – you can directly see and analyze KPIs including impressions, engagements, post reach, likes, and reactions.

There are other types of tools for experts as well. These are based on Python, Ruby and R platforms. They provide unstructured data analysis, text analysis and selective data storage from similar social media platforms. Some traditional enterprise versions of analytics software come as packages for social data analysis, while others are opensource, so you can download and run them independently.

A new era of social media analysis

The social analysis does not involve quantifiable actions like shares, comments, likes, and mentions. There are subtle nuances in mentions, tags, and comments that human users can pick up. Earlier machines could only understand the use of keywords or phrases mechanically. It was more of an all-or-none process. Right now, computers are evolving, and they are becoming aware of the human “emotions” and the “intents” behind each action. It has given rise to social media intelligence, which is a collection of solutions, tools, and algorithms for monitoring social media activities.

Social media intelligence is a heterogeneous term thanks to the various kinds of data obtained from social media interactions. Advanced forms of social media analysis also involve sentiment analysis and natural language processing. It gives marketers the power to analyze the “tonality” of posts, comments, and mentions. The intent behind a share or the sentiment behind a comment plays key roles in the future of a product and a campaign. Text analysis is a significant part of such sentiment analysis algorithms.

Interestingly, any marketer or entrepreneur can get the result of the analysis as a quantified score of the public sentiments towards their company on any social media platform. The advancement of technology has made it possible for anyone to understand popular opinions and “feelings” about their business on social channels like Facebook, Twitter, Instagram, LinkedIn, and Google+.

These native tools do their bit to help analyze the data available to them. However, the real drill lies in the isolation of the relevant data, their curation, and management.

Role of database in social media analysis

Social media analytics is supposed to help marketers find their target customer, fine-tune their marketing strategies and analyze the ROI of their campaigns. Now, without a proper database and a DBMS, it is impossible to analyze data worth months or even years. With the advancement of machine learning and big data management, it is becoming a little easier for marketers to leverage data from various channels and collate them to compose the bigger picture.

Remote DBA and new DBMS provide ways for enterprises to collect data in a scalable manner. Any good database you pick should be scalable. So you can expand the functions, fields, and properties of your tables along with the expansion of your business. It is one reason why AI is becoming indispensable at most data analytics firms who deal with social media analysis and big data analysis. A considerable number of opensource platforms such as Python and Tensor Flowis currently serving as the nexuses of social media learning algorithm development for different markets and different social media channels.

Social media analysis is a broad topic. It encompasses database management, social media intelligence, sentiment analysis, text analysis and big data, to just begin with. It is a complex process, which any marketer can simplify a bit by choosing the right social media listening tools, analytics tools, big data management options and a good database for storing the relevant data.