Analyzing the trend of O2O commerce by bilingual text mining on social media

Highlights

• Study provides insights from crowd intelligence and reveals trend analysis.

• Bilingual text mining approach helps understand O2O development trends.

• O2O ecommerce in China attracts public attention from different languages regions.

• Companies apply approach to develop O2O strategy to enhance competitive advantage.

Abstract

Social media has been changing not only the way people communicate with friends, but also the way providers communicate with consumers. Being as the popular means of information acquisition and facilitating the exploration of various topics and market trends, social media was also playing a key role in the development of online to offline (O2O) commerce. In the current study, bilingual text mining was conducted to analyze the trends of O2O commerce from the perspective of social media. Keywords and their concept linking diagrams were first identified to understand the important O2O trends in different languages. While keywords “China”, “Alibaba”, “Baidu”, “DianPing”, “Meituan”, “Asia”, “ecommerce” and “service” were the most strongly associated English terms with O2O, keywords “Baidu”, “Tencent”, “headlines”, “Sina.com”, mode”, “ecommerce”, “retail” and “platform” were closely related to the Chinese tweets. To further understand the similarities and differences between English tweets and Chinese tweets, we compared the bilingual text mining results according to the categories of company, region, service Apps and operation model. Our study provided important insights from crowd intelligence and revealed trends analysis about the recent O2O development in different language regions.

Introduction

Online to offline (O2O) commerce is a business combination model that includes both online and offline channels; the advantage of the online channel is that it can drive offline sales and coupon redemption (Phang, Tan, Sutanto, Magagna, & Lu, 2014). O2O commerce completely changes price competition in the traditional channel supply chain, and it can be seen as a specific form of multichannel integration, where the focus is on conducting online promotions (Zhang, Chen, & Wu, 2015). With the popularization of information technology, O2O commerce has become localized and integrates internet or mobile technology for linking online shopping and front-line transactions to entice consumers to make purchases in physical establishments (Kang, Gao, Wang, & Wang, 2015). Compared to traditional ecommerce approaches, such as B2C and C2C, the approach of O2O focuses on linking online transactions to local services, such as restaurants, cinemas, gyms, tourism, beauty salons, spa shops and other service-oriented products. Although the abovementioned approaches provide online purchase and payment services, traditional ecommerce delivers goods or services directly to customers, while O2O provides services at brick and mortar businesses with offline logistics (Yan, 2013). Due to the growth of the O2O market, enterprises and retailers need to focus on building a seamless experience between online and offline interactions to acquire and retain customers. Participants in the O2O model include consumers, retailers, third-party service providers, and O2O operators. Meanwhile, online matchmaking, online payments, offline consumption, and message back indispensable are included in the transaction process of O2O. The four aspects for price differences in products or services, revenues from online advertising, business purchases for consumers, and membership fees are considered as the profits of using an O2O business model (Xue & Li, 2016). As a type of business combination, the O2O model focuses on local service, the mobile and internet commerce technology to build a long-term competitive advantage, in terms of both individual customers and business customers. The O2O business model is gradually dominating the online shopping market. According to Japan's Nomura Research Institute survey in 2011, the size of the Japanese O2O market was approximately 24 trillion yen (US $219 billion) and was estimated to reach approximately 51 trillion yen (US $465 billion) in 2017. The size of the Chinese O2O market was approximately RMB 665.94 billion (US $96.56 billion) in 2016 and was estimated to reach approximately RMB 834.32 billion (US $120.96 billion) in 2017.

The popularity of O2O has also resulted in extensive discussions in the academic field. However, seldom do studies explore the O2O trends analysis based on the perspective of social media, especially those that address different languages. In this study, we focus on analyzing the trends of O2O by applying a text mining approach on the Twitter messages posted in English and Chinese. Trend analysis is the widespread practice of collecting information and attempting to spot a pattern, and it could be used to estimate uncertain patterns (John, 2004). It is not only used to predict future outcomes, but also used to observe the past events, to track the variances and frequencies in the past historical data. Trend analysis by using text mining approach has been studied in different disciplines. For example, social media analytics is the practice of gathering data from social media websites and analyzing that data using social media analytics tools to make business decisions. It is considered as information tool to collect, monitor, analyze, summarize, and visualize social media data to extract useful findings (Zeng, Chen, & Li, 2010). Comparing to traditional data analytics, social media tools can improve interactivity between customers and the companies, while the traditional media can only do in one way connection (Bertot, Jaeger, & Glaisyer, 2010). Regarding the O2O business model, social media helps to build close relationships between online interactions and offline marketing. The social media, such as Instagram, LinkedIn, Twitter, Facebook etc. has experienced in outstanding expansion, and became important channels for business and marketing (Herrero, San Martín, & Garcia-De los Salmones, 2017). Social media has become a crucial role, not only in changing people's communication with their friends but also by changing the way providers communicate with customers in O2O commerce (Asur & Huberman, 2010). Through social media's function of referrals and guiding users to real-world stores, retailers can create social events and promote social word-of-mouth to determine who the valuable customers are and promote products or services to social customers (Tsai, Yang, & Wang, 2013). This study focuses on the analysis of Twitter data, because Twitter is one of the most popular microblogging service providers with popular features such as hashtag (Fahd, El Habib, & Omar El, 2017) and tweets from Twitter are a rich source of data that represents trends analysis. The Twitter data was used to make predications and examine the reflective of the climate of public trends during the 2011 Singapore General Election campaign (Skoric, Poor, Achananuparp, Lim, & Jiang, 2012). We applied bilingual text mining approach by means of supervising Twitter data and identified related keywords with building the corresponding concept linking diagram for the first and second layers. Afterwards, terms related to O2O were investigated, and the implication of relevant words were explored. We also compared the differences and similarities of O2O keywords mentioned in English and Chinese tweets to understand the recent development of O2O commerce in different language regions. The study provides promising implications for enterprises aiming to adopt O2O commerce business models by suggesting that they can apply similar text mining approaches for developing similar models based on their available resources. These enterprises can use text mining to obtain news about the industry, information regarding their competitors and comments from the public about products and services. In this way, enterprises can evaluate the market and predict future market trends (Dai, Kakkonen, & Sutinen, 2011). By observing the issues and opinions of people who post in the social media, we can further understand the industry trends and develop immediate responses for a market strategy.