Social platforms are an integral part of the society we live in. As the dependency on social media surges, the number of interactions is rising. This generates a phenomenal amount of data every single minute. By leveraging it, data scientists can draw various insights on consumer behavior. This is how data mining in social interactions operates.
It enables us to track the thoughts and conversations of millions of people regarding a brand, an event, or a service. By correlating the client issue with his social interaction, data mining provides a new dimension to the client servicing. The data drawn can be in any format - a blog post, tweet, photo, or a video, they enable us to analyze the individual and collective sentiment toward the brand.
Data mining social interactions has many advantages in the current business landscape:
1. Predictive Analysis
Data mining gives much-needed impetus to draw predictions relating to consumer behavior. This prepares the business processes to handle the future consumer move. It will reduce, if not drop the chances of companies making non-productive decisions. By analyzing the social interaction, an e-commerce expert can predict that a customer of a certain demographic buys a specific apparel. This insight helps them to manage inventory by phasing out the possibility of over or under procurement.
2. Lower Costs and Improve Revenue
Data mining lowers the chances of immature business decisions by the constant flux of social data. This also helps in near real-time issue solving and curb the odds of the brand image getting tarnished. The investment once required to seek consumer complaints and grievances will also come down. It reduces the costs of the loyalty programs and results in increased financial stability of the company.
3. Enforcing Governmental Regulations
Many urban development departments depend upon data mining techniques. To estimate the number of people coming under the enforced regulations, they make use of social data obtained through mining. This helps them choose the channels onto which the citizens are more active on. When they have enough information, they can decide on the content format through which they can reach the users.
4. Creating Awareness
Social data can bring awareness to the public in the case of misconduct. If a fake handle gives the inappropriate information, it difficult to track since the miscreant will not interact directly with the brand. Data mining can grasp the first public mention of the issue in a span of seconds. This is where the inherent capabilities of a data mining prove to be effective.
As user data is getting into the social domain with rapid pace, it also poses many disadvantages:
When you mine the social interaction, you try to get more information about the user than is visible at superficial layers. When you access a user's social interactions about the brand, it can also include his private interactions. Hence this can affect the privacy of the individual. This will be the case where you sought the confidential information. It gives a leisure for a data mining center to misuse it.
Many data mining companies get the user data generated by one click sign-in through their personal accounts. Even though the user is alerted about the data vulnerability, he overlooks it in the haste to getting the work done. This can lead to serious security threats although it is not a common phenomenon. Also, when a third party app gathers data, the user ignores the fact that even after the session expiry it will have access to his account until it is revoked.
3. Initial Cost
In one of the above advantages, I advocated that data mining will bring down costs for the company. But what comes as an initial installation cost will be very high to afford for many SMBs. At implementation stage, the tools will involve high costs. If data mining doesn't succeed in giving the expected results, it incurs a relatively huge loss for a company.
4. Incorrect Information
Incorrect information can also be the main drawback of data mining systems. When a user interacts on the social platform, they don't always assure us that they're being pristine in their thoughts.
Automated data mining systems can misinterpret the user's satire or mockery as a positive sentiment. Hence, the sentiment analysis might not be accurate or even exactly opposite of what the user really meant.