Social media have been adopted by many businesses. More and more companies are using social media tools such as Facebook and Twitter to provide various services and interact with customers. As a result, a large amount of user-generated content is freely available on social media sites.

To increase competitive advantage and effectively assess the competitive environment of businesses, companies need to monitor and analyze not only the customer generated content on their own social media sites, but also the textual information on their competitors’ social media sites. In an effort to help companies understand how to perform a social media competitive analysis and transform social media data into knowledge for decision makers and e-marketers, He, Zha and Li’s paper [2013] describes an in-depth case study which applies text mining to analyze unstructured text content on Facebook and Twitter sites of the three largest pizza chains: Pizza Hut, Domino’s Pizza and Papa John’s Pizza.

 

Blogs and social networks have recently become a valuable resource for mining sentiments of customers and brand association. A product like sprinkler is well known to perform such activities.

Textual analysis is a text mining analysis approach that determines key factors by analyzing

large amounts of data. It is a qualitative approach that uses the weight and repetition of text in

a given sample to determine the keywords that express the sentiments shown about the subject under

study

Such an analysis would help not only companies but also to the consumers, so that important messages are received by the right set of audiences.

Some Planning before we could start:

  • Identify the source you want to monitor for customer’s feedback
  • Map out keyword with Brands or product
  • Plan the frequency of data collection

Possible Execution activity:

  • Once Network data is collected clustering data in different  groupThese grouping can be done on different attributes like the trustworthiness of the source, the diameter of the network generated from such source.It would interesting to find  any betweenness among such network
  • Identifying most impacting factors like the influencer in each network and what they are talking about.
  • Major topic people are connected or shared news across the network.
  • Identifying the cause by group (by social channel), age, country.

Since certain product do well in some country but not in other, it’s not due to the bad design of the product, it’s due to the difference in the need of the customer in the serving region.

Ref: https://arxiv.org/abs/1510.05301

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