Today, enterprises have the mandate to deliver delightful customer experience throughout the product lifecycle to retain their customers. Fostering positive relationships with customers is possible only if an enterprise can exceed customer expectations. Social media has played a vital role in online marketing in the last one decade.
Role of Social media in marketing
The interactions that happen in social media makes or breaks a brand/ product. Enterprises have become popular overnight and enterprises have fallen flat in a single day; all because of the power of social media. Hence every enterprise should invest in predictive social media analytics to stay competitive in the market. Social media analytics has become a key element in tech support.
What is social media analytics?
Social media analytics is the process of collecting, grouping and consolidating and analysing raw data from social media channels like Facebook, Twitter, LinkedIn, YouTube comments, blogs and online forums to aid forecasting and strategizing.
What does social media analytics do?
With the population in social media channels growing at a rapid pace, interactions in the online space have exploded like never before. In 2018, it is estimated that there will be around 2.67 billion social network users around the globe, up from 1.91 billion in 2014.1 Analytics plays a vital role in building brand image, acquiring customers and retaining them and also in formulating go to market strategies for companies.
How does social media analytics work?
1. In product development
Firstly social media integration based analytics helps enterprises to make instant improvements to their products. Rich social media integration is giving lot of intelligence for product companies to change their consumer patterns or buying patterns and helps them to intrude niche markets. Social media analytics also provide product development insights and support; enabling enterprises to deliver products of customer choice.
2. In delivering proactive support
Social media analytics helps in problem identification. The most common use for social media analytics tools is crisis aversion. They can serve as an early warning system for negative customer feedback about products or customer support. This can avoid conversations like this
Tech Support: "Just call us back if there's a problem. We're open 24 hours."
Customer: "Is that Eastern time?"
Customer support should not be reactive any longer. The issues should be resolved even much before it occurs. An analytical tool that has the capability to scan social media websites, get insights about a product and proactively alert the end users will be responsible for better customer experiences. Such an analytical tool is the need of the hour. This tool will be a bridge that connects traditional support and new age support. With social media analytics, organizations can get to know their customers in ways never before possible.
Is there a challenge with social media analytics that needs to be addressed?
Even though social media analytics enables an enterprise to understand their customers better; social media data is like a teenager throwing tantrums. The reason why I say this is, because social media is still evolving and the data from it is a bit complicated to analyse. Analytical tools can provide valuable information to enterprises; at the same time many findings can be skewed because analytical tools may not pick up the tone, slang and intention of the social media user.
One classic example is when a consumer got irate with poor customer service, he used the phrase "Yea! Right" in a sarcastic tone. The sarcasm in the tone was not picked up by the support agent and the support agent went on to irritate the customer even more by repeating the same question again and again. The enterprise lost that one customer. This image below captures the conversation between the customer and the support agent
If it is difficult for a human support agent to understand the customer's tone, how difficult would it be for social media analytical tool to capture the nuances such as sarcasm when trying to interpret text data?
So what should be done to address this challenge?
The analytic tool should go beyond simple text analytics and should include opinion mining, visual analytics and most importantly sentiment analytics. Text analytics uses algorithms to detect the most popular words and phrases by breaking sentences into component parts and removing meaningless words and analysing meaningful and frequently used words. But sentiment analysis helps users to interpret customer sentiment using sophisticated algorithms to analyse the emotional intensity from the comments
Can you share some examples of how social media analytics has helped you?
