Case Study: Discovering Novel Food Development Brief from Online Communication. Abstract. The success of new product development depends on how the new products effectively meet the unmet needs of customer.
- Growth rate continuously increased in past ten years.
- The case study company: Charoen Pokphand Food (CPF); the leader of food producer in Thailand, will increase their sales 50% in 2012.
- Keller Fay Group reported that 80% of eWOM conversations are about food and dining.
sourcing and analyzing online data requires specific searching and gathering methods because there are several types of information.
eWOM messages are continuous in each group and often have similar sub-themes. To define keywords requires: 1) dividing the gathered messages to thread, 2) measuring the weight of a term found in messages, and 3) identifying keywords. 1) and 3) depends on research objective. The standard method for 2) is Term Frequency Inverse Document Frequency (TFIDF) which measures the importance of each term based on its frequency.
This study has been conducted by online data gathering from www.pantip.com, one of the top ten websites in Thailand. Discussions about current events on its topics boards are often cited by the Thai media. Featured forums or “cafés” consist of 25 topic. The topic for this research is the “Food Café”www.pantip.com/cafe/food which provides an information exchange platform related to food. Data gained from the website has been utilized to define the keywords relating to the topic of eWOM.
Dividing texts to threads
This research proposed the modification of TFIDF to fit our requirement that the definition of keywords is as follow:
According to discovering keyword definition, this research adjusts weight for calculating the M-TFIDF. The words are ranked from all topics containing a keyword k by ranking score skwhich is calculated by the equation as follow:
where t is the number of relevant threads that can be extracted from all related topics, and
ť is the number of non-relevant threads that can be extracted from all related topics,
Г is the number of all relevant threads,
Ѓ is the number of all non-relevant threads,
and TFIDF is the standard one.
The case study of CPF, the product development department specified three input keywords from the new product development plan: Dim Sum, Hors d’oeuvres and Meatball spicy salad. The range for data collection was one year of eWOM posting in www.pantip.com/cafe/food. The developing instrument retrieved all the posted texts for one year, selected the topics and combined the text into threads. Total of 17,738 topics were conducted to test the instrument. Posted eWOM in each topic was combined, summarized, and divided into threads. The settle variables were calculated and utilized to divide all topics into threads, and then 25,332 threads relating to 3 input keywords were divided. Threads were analyzed and the outcome yielded a set of keywords as shown in the following table:
According to the testing of the developed instrument, users searched for sample terms. Those terms were inputted and relevant information related to the terms or in this case “keywords” were retrieved from the website www.pantip.com/cafe/food from threads posted for the duration of one year. The outcome received from the keywords searched corresponded to the keywords inserted by users. M-TFIDF rearranged the importance of terms and allowed users to retrieve information kept in relevant threads. In some cases, the outcomes were unclear and did not lead to new product development decision. The problem found during instrument testing was the interpretation of keywords. We will improve the preliminary operation by allowing users to retrieve exact meaning of keywords by reading the original texts.
This study presented the utilization of the popular eWOM as the open source to gather and detect the customers’ needs in order to assist in the development of new product. We developed an alternative instrument to search the information relating to the customers’ behavior through eWOM; in this case, the Thai eWOM. The developed instrument benefits users in the gathering of up-to-date and accurate customers’ behavior, acknowledgement, opinions and their satisfaction regarding to products and services. Significantly, the users can use the acquired keywords as the basis or development brief for the new product development process and to increase the business competitiveness of the industry.