Journal of Intelligence and Information Systems (지능정보연구)

Korea Intelligent Information System Society (한국지능정보시스템학회)
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Measuring the Economic Impact of Item Descriptions on Sales Performance

온라인 상품 판매 성과에 영향을 미치는 상품 소개글 효과 측정 기법

  • Received : 2012.10.15
  • Accepted : 2012.11.07
  • Published : 2012.12.31
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Abstract

Personalized smart devices such as smartphones and smart pads are widely used. Unlike traditional feature phones, theses smart devices allow users to choose a variety of functions, which support not only daily experiences but also business operations. Actually, there exist a huge number of applications accessible by smart device users in online and mobile application markets. Users can choose apps that fit their own tastes and needs, which is impossible for conventional phone users. With the increase in app demand, the tastes and needs of app users are becoming more diverse. To meet these requirements, numerous apps with diverse functions are being released on the market, which leads to fierce competition. Unlike offline markets, online markets have a limitation in that purchasing decisions should be made without experiencing the items. Therefore, online customers rely more on item-related information that can be seen on the item page in which online markets commonly provide details about each item. Customers can feel confident about the quality of an item through the online information and decide whether to purchase it. The same is true of online app markets. To win the sales competition against other apps that perform similar functions, app developers need to focus on writing app descriptions to attract the attention of customers. If we can measure the effect of app descriptions on sales without regard to the app's price and quality, app descriptions that facilitate the sale of apps can be identified. This study intends to provide such a quantitative result for app developers who want to promote the sales of their apps. For this purpose, we collected app details including the descriptions written in Korean from one of the largest app markets in Korea, and then extracted keywords from the descriptions. Next, the impact of the keywords on sales performance was measured through our econometric model. Through this analysis, we were able to analyze the impact of each keyword itself, apart from that of the design or quality. The keywords, comprised of the attribute and evaluation of each app, are extracted by a morpheme analyzer. Our model with the keywords as its input variables was established to analyze their impact on sales performance. A regression analysis was conducted for each category in which apps are included. This analysis was required because we found the keywords, which are emphasized in app descriptions, different category-by-category. The analysis conducted not only for free apps but also for paid apps showed which keywords have more impact on sales performance for each type of app. In the analysis of paid apps in the education category, keywords such as 'search+easy' and 'words+abundant' showed higher effectiveness. In the same category, free apps whose keywords emphasize the quality of apps showed higher sales performance. One interesting fact is that keywords describing not only the app but also the need for the app have asignificant impact. Language learning apps, regardless of whether they are sold free or paid, showed higher sales performance by including the keywords 'foreign language study+important'. This result shows that motivation for the purchase affected sales. While item reviews are widely researched in online markets, item descriptions are not very actively studied. In the case of the mobile app markets, newly introduced apps may not have many item reviews because of the low quantity sold. In such cases, item descriptions can be regarded more important when customers make a decision about purchasing items. This study is the first trial to quantitatively analyze the relationship between an item description and its impact on sales performance. The results show that our research framework successfully provides a list of the most effective sales key terms with the estimates of their effectiveness. Although this study is performed for a specified type of item (i.e., mobile apps), our model can be applied to almost all of the items traded in online markets.

온라인 상에서 판매되는 상품은 매우 다양하지만, 소비자에게 판매 가격을 제시하거나 소개글을 통해서 상품에 대한 구체적인 설명을 제공한다는 점은 모든 상품에 있어서 가장 기본이 되는 공통적인 특징이다. 만약, 상품의 실제 품질이나 가격과는 독립적으로 상품 소개글이 판매에 미치는 영향력을 파악할 수 있다면 어떠한 소개글이 상품의 판매를 촉진하는 측면에서 더 좋은 글인지 분별할 수 있게 된다. 이런 관점에서 본 연구는 상품 소개글과 판매 성과의 관계를 파악하기 위한 목적으로 수행되었으며, 구체적으로는 온라인 시장에서 한글로 작성된 상품 소개글에 쓰인 각각의 표현 별로 소비자가 구매를 결정하는 데에 어떤 영향을 미치는지를 분석하고자 하였다. 한글 형태소 분석기를 사용하여 국내 앱 시장에서 수집된 앱 소개글 및 판매이력 데이터로부터 상품을 설명하는 주요 속성과 그 속성에 대한 평가를 추출하였으며, 추출된 키워드를 입력 변수로 구성한 계량경제학 모형을 구축하였고, 구체적으로 특정 표현들이 판매 성과에 미치는 영향을 구축된 모형을 사용하여 계량적으로 측정하였다. 앱의 카테고리 별로 표현의 종류가 상이하게 나타남이 관찰됨에 따라, 분석은 각 카테고리 별로 수행되었다. 유료 앱과 무료 앱에 대해서 데이터 분석을 수행한 결과, 판매 성과에 영향을 미치는 '속성과 평가' 키워드를 그 영향력의 크기 별로 파악할 수 있었으며, 특히 무료 앱의 경우는 무료로 이용할 수 있음에도 불구하고 품질이 좋다는 것을 강조했을 때 판매량을 더 높일 수 있다는 것이 확인되었다. 본 연구는 모바일 앱에 대해 수행되었으나, 온라인에서 거래되는 다양한 상품에 대해서도 소개글이 판매 성과에 미치는 영향을 분석할 수 있는 모형으로 활용될 수 있다. 마지막 장에서는 기업의 마케팅 매니저가 본 연구에서 제시하는 연구 방법론과 분석 결과를 활용할 수 있는 방안을 제시하였다.

Keywords

References

  1. Park, S., K. Hwang, and D. Lee, "A PageRank based Data Indexing Method for Designing Natural Language Interface to CRM Databases", CRM Research, Vol.2, No.2(2009), 53-70.
  2. Lee, D., J. Yeon, I. Hwang, and S. Lee, "KKMA : A Tool for Utilizing Sejong Corpus based on Relational Database", Journal of KIISE : Computing Practices and Letters, Vol.16, No.11(2010), 1046-1050.
  3. Shim, K., "MADE : Morphological Analyzer Development Environment", Journal of Korean Society for Internet Information, Vol.8, No.4(2007), 159-171.
  4. Shim, K. and J. Yang, "High Speed Korean Morphological Analysis based on Adjacency Condition Check", Journal of KIISE : Software and Applications, Vol.31, No.1(2004), 89-99.
  5. Archak, N., A. Ghose, and P. Ipeirotis, "Deriving the Pricing Power of Product Features by Mining Consumer Reviews", Management Science, Vol.57, No.8(2011), 1485-1509. https://doi.org/10.1287/mnsc.1110.1370
  6. Archak, N., A. Ghose, and P. Ipeirotis, "Show me the money! Deriving the Pricing Power of Product Features by Mining Consumer Reviews", Proceedings of the Thirteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Jose, USA, 2007.
  7. Chevalier, J. A. and D. Mayzlin, "The effect of word of mouth onsales : Online book reviews", Journal of Marketing Research. Vol.43, No.3 (2006), 345-354. https://doi.org/10.1509/jmkr.43.3.345
  8. Cho, I. and N. G. Kim, "Recommending Core and Connecting Keywords of Research Area Using Social Network and Data Mining Techniques", Journal of Intelligence and Information Systems, Vol.17, No.1(2011), 127-138.
  9. Choi, J. and H. J. Lee, "The Effects of Customer Product Review on Social Presence in Personalized Recommender Systems", Journal of Intelligence and Information Systems, Vol.17, No.3(2011), 115-130.
  10. Das, S. R. and M. Chen, "Yahoo! for Amazon: Sentiment extractionfrom small talk on the Web", Management Science, Vol.53, No.9(2007), 1375-1388. https://doi.org/10.1287/mnsc.1070.0704
  11. Dave, K., S. Lawrence, and D. M. Pennock, "Mining the peanutgallery : Opinion extraction and semantic classification of product reviews", Proceedings of the 12th International World Wide Web Conference(WWW), (2003), 519- 528.
  12. Forman, C., A. Ghose, and B. Wiesenfeld, "Examining the Relationship Between Reviews and Sales : The Role of Reviewer Identity Disclosure in Electronic Markets", Information Systems Research, Vol.19, No.3(2008), 291- 313. https://doi.org/10.1287/isre.1080.0193
  13. Gartner, Gartner Says Free Apps Will Account for Nearly 90 Percent of Total Mobile App Store Downloads in 2012, Available at http://www.gartner.com/it/page.jsp?id=2153215(Downloaded 1 October, 2012).
  14. Ghose, A. and P. G. Ipeirotis, "Estimating the helpfulness and economic impact of product reviews : Mining text and review ercharacteristics", IEEE Trans. Knowledge Data Engineering, Vol.23, No.10(2011), 1498-1512. https://doi.org/10.1109/TKDE.2010.188
  15. Ghose, A., P. Ipeirotis, and B. Li, "Designing Ranking Systems for Hotels on Travel Search Engines by Mining User-Generated and Crowd-Sourced Content", Marketing Science, Vol.31, No.3 (2012), 493-520 https://doi.org/10.1287/mksc.1110.0700
  16. Hu, M. and B. Liu, "Mining and summarizing customer reviews", Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), (2004), 168-177.
  17. Hwang, K., S. Park, and I. Han, "Multi-Screen Strategy for Selling Mobile Content to Customers", Proceeding of International Conference on Information Systems(ICIS), Orlando, USA, 2012.
  18. Lee, J., I. Son, and D. Lee, "Does Online Social Network Contribute to WOM Effect on Product Sales?", Journal of Intelligence and Information Systems, Vol.18, No.2(2012), 85-105.
  19. Schein, A. I., A. Popescul, L. H. Ungar, and D. M. Pennock, "Methods and Metrics for Cold Start Recommendations", Proceedings of the 25th Annual International ACM SIGIR Conference Research and Development in Information Retrieval, (2002), 253-260.
  20. Wooldridge, J. M., Econometric Analysis of Cross Section and Panel Data. The MIT Press, Cambridge, MA, 2002.