Social data analysis is the analysis of customer interactions on social media platforms, and involves collecting and analyzing social metrics, like average reach, total engagements, total impressions, trending topics, and more.
However, businesses receive a huge amount of unstructured data, in other words, data that’s hard to quantify because it comes in strings of text. In some social media analytics tools, you can easily filter by topic and sentiment to get a bird's eye view of how customers perceive particular products or services, but they don't scratch beneath the surface and explain ‘why’.
For example, maybe you want to know why there are more negative opinions about customer support than usual.
In this article, we’ll focus on analyzing data in social media conversations, and why it’s important for gaining even deeper insights about your customers.
The interconnectivity of the modern age means there are huge amounts of data created every day, most of it right at your fingertips. But it’s not always easy to analyze data, especially when it’s unstructured.
Imagine your company releases a new product and you see a sudden uptick in tweets about your company.
While there are some great social media analytics tools on the market, which dig into your metrics and help you identify patterns, trends, and opportunities, you also need to know which specific tweets are positive or negative, which topics customers mention and why.
Feedback from social media platforms sometimes provides the truest opinions.
Unsolicited opinions on social media are often where pure admiration or distaste for a product or company is expressed, when customers feel compelled to voice their opinion for others to see, making it a valuable source of feedback.
Text analysis is an advanced machine learning technique that helps you understand, process, and sort social media conversations for polarity (negative to positive sentiment), topic, urgency, intent, and beyond. It allows companies to:
There could be dozens of conversations happening about your brand on social media right now. Keeping an eye on Twitter using sentiment analysis tools, for example, will help you understand your customers, your competitors, and industry trends.
For social media monitoring, machine learning techniques like sentiment classification can be particularly useful. There are approximately 6,000 tweets sent every second. Imagine having access to all that data and the ability to analyze it in real time.
AI and machine learning programs allow you to scour Twitter for mentions specific to your company and find out exactly how users feel about your brand in minutes. Something that would otherwise take dozens of employees and countless hours to achieve.
Data analysis tools, like MonkeyLearn, can help you get the most out of your social data. By sorting your data by topic, sentiment, and more, you can truly understand what your customers need and start making data-driven decisions.
Social data reveals so much more than just quantitative data. By analyzing the text within this data, you can drive customer segmentation, carry out competitor research, discover marketing opportunities, quickly identify issues related to specific products or services, or unhappy customers that require urgent attention, and so much more.
All with the aim of improving business processes and customer experience.
Other data analysis tools include:
Social data analysis can be an extremely helpful tool to evaluate how the public feels about any business. Automated analysis of social media can save hundreds of employee hours and provide more consistent and accurate results.
There are a number of avenues you can take to gather your data. A proactive approach can keep your company ahead of the trends. Constant customer opinion monitoring will help you stay in direct contact.
Visit MonkeyLearn to discover advanced social data analysis software.
April 6th, 2020