In earlier articles we defined analytics as a mathematical and creative process that finds information useful to the business inside data. It applies in any setting; today we focus on social media.
Researching social networks through analytics makes it possible to better understand the relationships between the entities — people — interacting on the network. As a result of that understanding we can set precise strategies to:
1) Generate and grow audiences loyal to our company
2) Understand how our audiences feel so we can improve our products and services
3) Compare ourselves against our competitors
4) Understand the market our products and services are aimed at
5) Prepare for possible inflection points in our target market
6) Act on our audiences with offers that differentiate us from our competitors in order to increase sales, and
7) Generate new leads for our sales teams.
There is a wide variety of tools on the market offering this kind of analysis, and their algorithms are oriented towards: (1) selecting and collecting data, (2) processing data, (3) transforming and mining data, and (4) identifying hidden patterns, or insights.
In short, generating information useful to the business is done by extracting knowledge hidden in large volumes of data in order to make efficient decision-making easier. That is what we call insights.
Insights can be drawn from all the interactions generated on social networks that are tied to a specific topic: a tweet, comments, likes, shares, posts, news items and more.
Sources for digital analysis include social networks (Facebook, Twitter, Instagram, YouTube and so on), blogs, forums and RSS feeds.
There are three fundamental steps in the social network analysis process:
Identifying the data: data is the raw material for producing information, so it has to carry a message with a clear purpose — otherwise it will convey useless information. Data has to be structured and measurable.
Analysing the data: this is the process in which data becomes information, that is, knowledge and business value. Filtering the data as input, alongside various presentation techniques, will turn it into information for the analysts.
Interpreting the information: visualising it in charts that speak for themselves is the best option for sound decision-making, since it will be the starting point for analysis. Those charts have to show the patterns, trends and relationships found in the data analysis, and they have to (1) follow a logic that runs from the general to the specific and (2) tell a coherent story that makes the analysis easier.
Social media analytics software
Its purpose is to make it easier to carry out our social media strategy and to secure timely, efficient decision-making. With it you can coordinate the management, planning and execution of strategies as needed.
When we assess software for our social media analytics initiatives we have to take into account the profile of the person who will use it: taking decisions about marketing campaigns is not the same as analysing cubes of information extracted from the networks.
There is both free and paid software which, depending on what it offers, can range from small amounts to thousands of dollars a month.
Among the main features we can identify:
Support for community manager processes.
Dashboards for monitoring indicators and metrics for social media marketing campaigns.
Social listening through artificial intelligence.
Add-ons for creating interactive forms and collecting leads, with graphic design functions.
