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Case Study: Discovering Novel Food Development Brief from Online Communication.

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1 Case Study: Discovering Novel Food Development Brief from Online Communication

2 Abstract The success of new product development depends on how the new products effectively meet the unmet needs of customer. The existing research techniques such as survey and interview are no longer engaged the change of peoples lifestyle. Electronic communication now plays an important role in people daily lives and influences their lifestyles. Electronic word-of-mouth (eWOM) can be a valuable platform to gain customers information including their opinions, experiences, satisfactions.

3 Abstract The main objective of this study is to develop a novel approach to discover valuable keywords from eWOM. The empirical finding reveals natural language processing together with information retrieval as most suitable techniques for discovering keywords from eWOM. The main advantage of this tool is its real-time discovering keywords to generate ideas for new product development. This alternative tool can facilitate new product development process and provide valuable real-time input for companies to stay competitive in this social network era.

4 Case Study This research selected ready to eat food business in Thailand as case study for two key issues: The market opportunities: - 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. eWOM category ranking: - Keller Fay Group reported that 80% of eWOM conversations are about food and dining.

5 Literature Review

6 New Product Development The most important phase of new product development (NPD) is pre-development phase: which consists of idea generation, idea screening, and idea selection; to research and practice in order to make decision for next step of NPD. NPD success depends on the capability of pre- development activities and effective NPD. (Cooper and Kleinschmidt, 1990) Data concerning customers need bring about idea generation and be a guideline for making decision to NPD process. (Ogawa and Piller, 2006) Customers real need is very important for pre- development step and vital to the NPD achievement.

7 eWOM and NPD eWOM has major impact on product evaluations, customers attitudes, and decision to buy. The survey on the purchase decisions of online customers showed the most reliable information is from online customer themselves (85%). Successful new product need three important elements to meet customer needs: desirability, purpose, and positive user experience. Understanding customer needs is a major step towards idea generation in NPD. eWOM is highly valuable sources of customer data. eWOM may increase positive results in terms of success and accuracy of new product. It is necessary to explore the value of data from eWOM to maximize its merit in NPD.

8 Information Retrieval and Discovering Keywords Information Retrieval sourcing and analyzing online data requires specific searching and gathering methods because there are several types of information. Discovering Keywords 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.

9 Information Retrieval and Discovering Keywords Unit of AnalysisConstructionMethodologyAuthors MessageAutomating eWOM processEntropy algorithmPavlov et al., 2004 MessageImproving opinion web site designCorpus linguistics, textual analysis Pollach, 2006 MessageExtracting customers opinionsDeveloping data miningHu & Liu, 2006 NetworkAnalyzing micro blogs structureSentiment analysis and opinion mining Bernard & Mini, 2009 NetworkTracking eWOM on global networkDeveloping algorithmCebrian et al., 2009 MarketClassifying reviewsVector machine algorithm Zheng and Ye, 2009 MessageSentiment summarizationDeveloping algorithmNishikawa et al, 2010 MarketAnalyzing online forumWeb miningWong et al., 2010 MessageMeasuring text weightTFIDFSaito & Yukawa, 2010 MessageTracking literature on webTFIDFBollacker et al, 2007 MessageScoring words in text documentsTFIDFLee & Chun, 2007

10 Methodology

11 This study has been conducted by online data gathering from, 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 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

12 Methodology eWOM Text pre-processing Dividing texts to threads Threads Yes No Yes No M-TFIDF Keywords

13 The Modified for Threads-TFIDF This research proposed the modification of TFIDF to fit our requirement that the definition of keywords is as follow: 1.Frequent keywords in high-relevant threads are defined as high-level importance, frequent keywords in low-relevant threads are defined as low-level importance. 2.Frequent keywords in both high-relevant and low- relevant threads are defined as low-level importance. 3.Frequent keywords in high-relevant threads are more important than keywords in single high-relevant thread.

14 The Modified for Threads-TFIDF 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 s k which 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. TFIDF ( ) t t ť + SkSk = t ť Г - Ѓ

15 Result & Discussion

16 Result and discussion The case study of CPF, the product development department specified three input keywords from the new product development plan: Dim Sum, Hors doeuvres and Meatball spicy salad. The range for data collection was one year of eWOM posting in 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:

17 Result and Discussion (Dim Sum): 244 threads RankingWordsTFIDFWordsM-TFIDF 1 (polite term) 1497.526 (dim sum) 878.279 2 (shop) 1359.32 (basket) 16.234 3 (no) 1169.707 (yes) 14.267 4 (at) 1138.169 (shop) 14.165 5 (go) 1107.488 (delicious) 10.157 6 (polite term) 1087.352 (food) 9.955 7 (also) 1000.177 (hotel) 9.574 8 (come) 968.472 (grip) 9.505 9 (talk) 907.531 (eat) 9.110 10 (you) 893.696 (fry) 8.998

18 Result and Discussion (Hors d oeuvres): 63 threads RankingWordsTFIDFWordsM-TFIDF 1 (polite term) 325.339 (hors d oeuvres) 179.686 2 (polite term) 318.388 (fly) 0.751 3 (no) 281.682 (dish) 0.705 4 (also) 277.402 (eat) 0.652 5 (go) 267.196 (chicken) 0.641 6 (shop) 263.541 (shop) 0..625 7 (come) 257.747 (fish) 0.580 8 (will) 228.624 (home) 0.573 9 (really) 225.771 (take) 0.551 10 (eat) 223.562 (chili) 0.548

19 Result and Discussion (Meatball spicy salad): 76 threads RankingWordsTFIDFWordsM-TFIDF 1 (no) 794.404 (meatball) 878.279 2 (shop) 729.940 (salad) 16.234 3 (also) 709.322 (pork) 14.267 4 (polite term) 673.010 (fry) 14.165 5 (polite term) 659.234 (noodle) 10.157 6 (at) 652.643 (fly) 9.955 7 (will) 604.585 (put) 9.574 8 (go) 601.191 (shop) 9.505 9 (come) 572.330 (fish) 9.110 10 (talk) 550.346 (shrimp) 8.998

20 Result and discussion 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 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. from threads posted for the duration of one year.

21 Conclusions 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.

22 Thank You

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