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.

• The existing research techniques such as survey and interview are no longer engaged the change of people’s 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.

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.

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.

Literature Review

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 customer’s need bring about idea generation and be a guideline for making decision to NPD process. (Ogawa and Piller, 2006)

• Customer’s real need is very important for pre-development step and vital to the NPD achievement.

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.

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.

Information Retrieval and Discovering Keywords

Unit of Analysis Construction Methodology Authors

Message Automating eWOM process Entropy algorithm Pavlov et al., 2004

Message Improving opinion web site design Corpus linguistics, textual analysis

Pollach, 2006

Message Extracting customers’ opinions Developing data mining Hu & Liu, 2006

Network Analyzing micro blogs’ structure Sentiment analysis and opinion mining

Bernard & Mini, 2009

Network Tracking eWOM on global network Developing algorithm Cebrian et al., 2009

Market Classifying reviews Vector machine algorithm

Zheng and Ye, 2009

Message Sentiment summarization Developing algorithm Nishikawa et al, 2010

Market Analyzing online forum Web mining Wong et al., 2010

Message Measuring text weight TFIDF Saito & Yukawa, 2010

Message Tracking literature on web TFIDF Bollacker et al, 2007

Message Scoring words in text documents TFIDF Lee & Chun, 2007

Methodology

Methodology

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.

Methodology

eWOM

Text pre-processing

Dividing texts to threads

Threads

Yes

NoYes

No

M-TFIDF

Keywords

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.

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 sk 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+Sk = ( ) t ť

Г-

Ѓ

Result & Discussion

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 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:

Result and Discussion

ติ่��มซำ�� (Dim Sum): 244 threads

Ranking

Words TFIDF Words M-TFIDF

1 ครั�บ (polite term)

1497.526

ติ่��มซำ�� (dim sum)

878.279

2 รั �น (shop) 1359.32 เข่�ง (basket)

16.234

3 ไม� (no) 1169.707

ครั�บ (yes) 14.267

4 ที่�� (at) 1138.169

รั �น (shop)

14.165

5 ไป (go) 1107.488

อรั�อย (deliciou

s)

10.157

6 ค�ะ (polite term)

1087.352

อ�ห�รั (food)

9.955

7 ก็� (also) 1000.177

โรังแรัม (hotel)

9.574

8 ม� (come) 968.472 จี�บ (grip) 9.505

9 ว่�� (talk) 907.531 ที่�น (eat) 9.110

10 ค!ณ (you) 893.696 ที่อด (fry) 8.998

Result and Discussion

กับแกัล้�ม (Hors d’ oeuvres): 63 threads

Ranking

Words TFIDF

Words M-TFIDF

1 ครั�บ (polite term)

325.339

ก็�บแก็ล้ ม (hors d’ oeuvres)

179.686

2 ค�ะ (polite term)

318.388

ที่อด (fly) 0.751

3 ไม� (no) 281.682

จี�น (dish) 0.705

4 ก็� (also) 277.402

ก็�น (eat) 0.652

5 ไป (go) 267.196

ไก็� (chicken) 0.641

6 รั �น (shop) 263.541

รั �น (shop) 0..625

7 ม� (come) 257.747

ปล้� (fish) 0.580

8 จีะ (will) 228.624

บ �น (home) 0.573

9 เล้ย (really)

225.771

เอ� (take) 0.551

10 ก็�น (eat) 223.562

พรั�ก็ (chili) 0.548

Result and Discussion

ยำ��ล้�กัชิ้��น (Meatball spicy salad): 76 threads

Ranking

Words TFIDF Words M-TFIDF

1 ไม� (no) 794.404

ล้&ก็ชิ้�(น (meatball)

878.279

2 รั �น (shop)

729.940

ย�� (salad) 16.234

3 ก็� (also) 709.322

หม& (pork) 14.267

4 ครั�บ (polite term)

673.010

ผั�ด (fry) 14.165

5 ค�ะ (polite term)

659.234

ก็*ว่ยเติ่�+ยว่ (noodle)

10.157

6 ที่�� (at) 652.643

ที่อด (fly) 9.955

7 จีะ (will) 604.585

ใส่� (put) 9.574

8 ไป (go) 601.191

รั �น (shop) 9.505

9 ม� (come) 572.330

ปล้� (fish) 9.110

10 ว่�� (talk) 550.346

ก็! ง (shrimp) 8.998

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 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.

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.

Thank You