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How does analytics work for social commerce in China?
Sisi Wan - 06.24.2021

Global e-commerce companies are experiencing overall growth nowadays, and the performance of Chinese companies has been particularly eye-catching.  In fact, China is the fastest growing and largest e-commerce market in the world today. The popularity of social media and social connection needs under Covid-19 situation even boost social commerce development in China. In the post-pandemic era, China’s economy has been on a rapid recovery with the rise of social commerce. The social commerce industry is expected to grow at a compound rate of more than 50% and the market size will reach nearly USD 0.3 trillion.

In China, Alibaba's largest business is its core giant e-commerce operations that offer a digital marketplace where consumers and merchants can connect and buy and sell from each other. Alibaba empowers the business of all sizes with technology and innovation and help them succeed at their own terms. Furthermore, the subset of e-commerce, social commerce is the future trend due to its seamless nature. Tencent’s ambition in converging social and e-commerce over the last two years, gradually adjusting features and revolutionizing Wechat’s ecosystem. Peer recommendation is a powerful driver of purchasing decisions due to social trust foundation. Functionality of information sharing activities in social commerce can help to drive the conversion rate of transactions by leveraging the power of social buying. Brands may establish seamless e-commerce experiences immediately within popular social media channels. Besides, it allows brands to offer customer service and set up loyalty program to improve customer engagement. E-commerce and social commerce players in China, with Alibaba and Tencent as two representative giants, with the features of engagement, mobility, precise content, best-matching recommendation and referral; they together build the complex and fast growing EC industry while reconstructing the whole China e-commerce ecosystem as well as consumers’ shopping behaviors and lifestyles

 

 

In this era of information explosion and the digital revolution, interaction analytics tools and recommendation engine are very essential for business success. Teleperformance provides TP Recommender and TP Interact to drive better business insights from all the interactions. Our client steps their business in both e-commerce and social commerce platform. The client faces big problems with high return rate of 50-60% for their online business with an impact of over USD 0.31 billion. Hence, a refund predictive machine learning model was developed by Teleperformance to reduce return and refund by understanding customer’s purchasing and return patterns.​ Root causes for refund were identified and actionable plans were designed to reduce return aiming at potential refund revenue decrease by 13%.

In the next phase, a recommendation engine with the next best offers using machine learning algorithm will be deployed. Recommendation system implementation at agents’ workspace empowers the agents to customize the rebuttals if customers want to return a product with similar alternates. This AI based recommendation engine will help to infer the most relevant product alternatives, thereby increasing relevance of communications, delivering personalized customer experience, improving engagement, and boosting the revenue. Furthermore, interaction analytics can be done to reveal the most relevant and usable insights for operational, sales and marketing improvement. We look to bring those business case to other leading e-commerce company in China and continue diversify and bring value to our customer in China market as TP is building great capabilities to differentiate from our competition.

 

 

The application of these AI based tools in e-commerce and social commerce is critical for business success. Teleperformance provides a machine learning-based predictive analytics suite, TP Recommender that uses historical data, such as customer demographics, buying and paying patterns to predict churn to recommend alternative products, improve upsell, enhance collections, and propose the next-best action for agents. Besides, interaction analytics offered by Teleperformance, TP Interact helps to provide accurate actionable insights to contact center teams with the expertise of our in-house team of interaction consultants. Both TP Recommender and TP Interact work together to recommend next best actions for agents at workspace and improve customer experience and sales revenue during interaction, which in turn helps to strengthen brand bonding and customer loyalty.

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