Harvest Customer Feedback

Foursquare, Twitter, WeChat, QQ, and a whole host of other apps are available for the mobile platform and it is the content that is of the utmost importance, not the platform from where it came.

Mobile analytics—the use of data collected as a visitor accesses a website from a mobile device—can effectively track unique visitors, as well as reveal a mobile user’s network, device and location.

With site analysis added to a mobile analytics service, marketers can capture mobile metrics such as link tracking for campaign analysis and page tracking for site analysis. Data collected as part of mobile analytics typically includes information such as page views, length of visit as well as such mobile-specific information as mobile device, mobile network operator or carrier, country where the mobile user is calling from, language of the caller, and a unique user ID, which is required because http cookies and JavaScript do not work reliably on mobile browsers.

As He et al. argue in their article Social Media Competitive Analysis and Text Mining: A Case Study in the Pizza Industry[i], “The wide adoption of social media tools has generated a wealth of textual data, which contain hidden knowledge for businesses to leverage for a competitive advantage.” By digging through the vast amounts of unstructured social media data, businesses can discover important brand information.200 “Decision makers can also use the findings to develop new products or services and make informed strategic and operational decisions.”200

Text mining is “an emerging technology that attempts to extract meaningful information from unstructured textual data.”200 It is a form of data mining that attempts to find patterns, models, and/or trends in either structured or unstructured data such as text files, HTML files, social media files as well as a whole host of other proprietary files. Solutions such as SAP’s Infinite Insights, SAS’s Enterprise Miner, SPSS Modeler, and R can be used in the text mining process and they “use sophisticated computer paradigms including decision tree construction, rule induction, clustering, logic programming, and statistical algorithms to find insights and patterns from unstructured textual data.”200

The He et al. study “examined the social media sites of the three largest pizza chains and applied text mining to analyze unstructured text content on their Facebook and Twitter sites.”200 The He et al. study attempted to answer the following questions200:

  • What patterns could be found from their Facebook sites respectively?
  • What patterns could be found from their Twitter sites respectively?
  • What were the main differences in terms of their Facebook and Twitter patterns?

The results of the study revealed that the three largest pizza chains—Pizza Hut, Domino’s and Papa John’s—are all active in social media and have committed substantial resources for their social media efforts.200 The data showed that each pizza chain was committed to providing a delightful experience to its customers. For example, if customer questions could not be answered immediately, the pizza chain’s representatives quickly apologized and directed customers to a toll-free telephone number or customer service for further assistance.200

Of the three chains, the one with the smallest market share—Domino’s Pizza—demonstrated a higher level of commitment and consumer engagement than the other two through the number of social media posts and user comments.200 Perhaps the fact that they were so burned in social media previously also had a little to do with it? In particular, Domino’s Pizza responded to user comments more quickly, which, the authors believe, reflects their strong efforts in monitoring and handling their social media activities.200

In addition, the study found that user engagement levels on Facebook were much higher than on Twitter.200 Not only are there more Facebook fans than Twitter followers, but “the three pizza chains offered more promotional and user engagement activities on Facebook than on Twitter.200 It was the differences in the platforms that was the key; “Facebook allows people to stay connected and supports more active user participation; Twitter is mainly used for submitting concise updates and noteworthy information.”200  

The study demonstrated the importance that social media had become in the field of customer engagement for each of these three pizza chains. “Specific staff members have been assigned to engage customers and monitor the content that customers created in their social media applications.”200 Each chain has used social media as “an additional customer services and communication tool to gain insight into consumers’ needs, wants, concerns and behaviors in order to serve them better.”200 These pizza chains are using social media to listen and engage with their customers, handling customer suggestions and complaints.200 This is something every casino operator should be doing.

Social media is also being used for competitor analysis. “Social media competitive analysis allows a business to gain possible business advantage by analyzing the publicly available social media data of a business and its competitors. A business can compare its social media data to the social media data of their competitors to gain perspective on their performance.”200

He et al. conclude the paper by offering up the following recommendations when it comes to companies establishing a social media monitoring and competitive analysis strategy200:

  • Constantly monitor your own social media presence and your competitors’ social media presence.
  • Establish competitive benchmarking.
  • Mine the content of social media conversations.
  • Analyze the impact of social media findings and events on your business.

All-in-all social media has become a great place for companies to gain a real competitive advantage. “Correlation between social media findings (consumer sentiments and opinions) and events (e.g., price changes, rival’s promotional activities) and structured data like sales data need to be examined to understand how competition affects business and provide [sic] information for decision making,” state Dey et al.[ii]


[i] He, Wu, Zha, Shenghua, Li, Ling. Social media competitive analysis and text mining. A case study in the pizza industry. International Journal of Information Management 33 (2013) 464-472. http://saharbread.sahargroup.ir/Uploads/28460.pdf

[ii] Dey L., H. S. (2011). Acquiring Competitive Intelligence from Social Media. Proceedings of the 2011 Joint Workshop on Multilingual OCR and Analytics for Noisy Unstructured Text Data.

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