Facebook has become one of the most important means for B2C (business-to-consumer) communications. When a Facebook user likes, posts comments, or shares content with their Facebook credentials, an update will appear on this person’s wall, helping companies rapidly spread information. Companies must pay close to attention to Facebook users’ reactions to the messages they send on Facebook because Facebook users’ endorsement of a message can be very important in indicating the effectiveness of a company’s social media strategy.
In one of my recent studies (co-authored with Dr. Bei Yu), we adopted the text mining techniques to identify the type(s) of Facebook messages that are endorsed (and thus propagated) by Facebook users. We analyzed 982 Facebook messages initiated by 10 restaurant chains and two independent operators and found the following results:
- The “more popular” messages, which receive more “Likes” and comments, contain keywords about the restaurants (e.g., menu descriptions).
- The “less popular” messages seem to involve with sales and marketing.
- Dividing the messages into four media types (i.e., status, link, video, and photo), photo and status receive more “Likes” and comments.
- In terms of the content of the messages, we coded the messages into two message types, namely sales/marketing and conversational messages, which do not directly sell or promote the restaurants. As compared to sales and marketing messages, conversational messages receive more “Likes” and comments even though they only account for 1/3 of the messages in the study.
- There is also a cross-effect of media type and message type on the number of comments a message received.
Based on the research findings, we outlined several practical tactics in this paper to help companies improve their use of Facebook. They include:
- Use the eye-catching keywords when writing a social media message.
- Focus more on sharing status and photo rather than links or videos.
- Provide a brief description when sharing links or videos so that Facebook users are informed about the content without clicking the links or watching the videos.
- Engage with Facebook users with conversational messages rather than just selling or promoting a product, service, or the company.
- Learn from the best examples (i.e. Starbucks and Chick-fil-A in this study) and see how they engage with their Facebook users.
To read more about this study and the findings’ managerial implications, please access the full-text article online at SAGE Publication.
If you are a social media manager or an expert in the field, do you think our research provide any useful insight? What suggestions will you make to help other practitioners better engage on social media sites? For future studies, what important (research) questions do you want me or other researchers to answer?
Relevant discussion:
References:
Kwok, Linchi, and Yu, Bei (In press). Spreading the social media messages on Facebook: An analysis of restaurant business-to-consumer communications. Cornell Hospitality Quarterly, special issue on Information-Based Strategies in the Hospitality Industry. (DOI: 10.1177/1938965512458360)
The picture was downloaded from Ignitesocialmedia.com.
Relevant publications:
Yu, Bei, Chen, Miao, and Kwok, Linchi (2011). Toward predicting popularity of social marketing messages. In J. Salerno, S.J. Yang, D. Nau, & S.K. Chai (Ed.), Social Computing, Behavioral-Cultural Modeling and Prediction: Lecture Notes in Computer Science (pp. 317-324). Heidelberg, Germany: Springer.
Yu, Bei, and Kwok, Linchi (2011, July). Classifying business marketing messages on Facebook. Empirical full paper presented in the Internet Advertising (IA 2011) Workshop at the 34th Annual International ACM SIGIR (Association for Computing Machinery; Special Interest Group on Information Retrieval) Conference, Beijing, China.
The picture was downloaded from Ignitesocialmedia.com.
Relevant publications:
Yu, Bei, Chen, Miao, and Kwok, Linchi (2011). Toward predicting popularity of social marketing messages. In J. Salerno, S.J. Yang, D. Nau, & S.K. Chai (Ed.), Social Computing, Behavioral-Cultural Modeling and Prediction: Lecture Notes in Computer Science (pp. 317-324). Heidelberg, Germany: Springer.
Yu, Bei, and Kwok, Linchi (2011, July). Classifying business marketing messages on Facebook. Empirical full paper presented in the Internet Advertising (IA 2011) Workshop at the 34th Annual International ACM SIGIR (Association for Computing Machinery; Special Interest Group on Information Retrieval) Conference, Beijing, China.