Reprinted with permission from Management and Business Review, an academic journal backed by some of the world’s best business schools. This article is part of the AI for Customer Engagement edition.
Alibaba Group, China’s largest e-commerce retailers and one of the world’s biggest companies, has nearly one billion annual active Chinese consumers who daily participate in hundreds of millions of transactions through its Taobao e-commerce platform.
During Alibaba’s busiest shopping period in 2021 − Taobao’s Double 11 shopping festival − its platforms facilitated 540 billion RMB (US $85 billion) in transactions over a two-day period. The sheer numberof customer interactions in Taobao transactions makes it difficult for Alibaba to keep up with proactively engaging with its customers.
Further, Alibaba needs to resolve several million customer service queries each day. The queries can come from end-user consumers or business merchants in Alibaba’s two-sided platform. Consumers may have questions about the Alibaba platforms or be unsatisfied with products they purchased from merchants on the platform. Merchants could have questions about end-user consumers or about the platforms.
Taken together, Alibaba faces resource restrictions that limit its ability to engage with customers solely by human service interactions. Since 2015, Alibaba’s response has been to implement artificial intelligence (AI) chatbots, supplemented by human service interactions, to both proactively and reactively engage with its customers.
Today, Alibaba uses AI chatbots to handle customer engagement for more than two million daily sessions and over 10 million lines of daily conversation on Taobao’s two-sided platform, representing about 75% of Alibaba’s online and 40% of phone hotline consultations.
Not only has the use of AI chatbots raised customer satisfaction by 25%, based on initial results, it has saved the company more than one billion RMB annually (~US $150 million) by employing AI instead of human contact center agents.
Alibaba’s AI chatbots
Alibaba employs five AI chatbots to cater to the heterogenous demands from customers on Taobao’s two-sided platform.
• The Wanxiang-bot assists merchants on the Taobao platform, performing tasks such as resolving questions about the platform’s rules, activities, and service issues.
• The Alibee Shop bot assists merchants with end-user consumer interactions on Taobao, helping with service issues and direct engagements between merchant and consumers.
• The Alime bot helps the end-user consumer. It is employed in online and phone hotline channels and relies on a rich set of interactive user interface components that can provide text dialogues, cards, graphics, videos, and other conversational interactions between robots and consumers. Alime bot also possesses duplex voice dialogue capabilities and a targeted user interface to serve users who prefer to consult by telephone.
• The AI bot complements Alime by proactively engaging with end-user consumers and acting as an intermediary during service disputes with business merchants. The AI bot’s underlying algorithms use transaction and conversational information to evaluate service disputes and make automated judgments. TheAI bot then calls end-users to discuss the dispute decision. If consumers are satisfied withthe AI bot’s decision, the case is closed; if not, the AI bot helps consumers fill out appeal forms to have the case resolved by a human evaluator.
• The Dahuang bot helps train customer service personnel by simulating a larger and more diverse set of consumer and merchant encounters than a non-AI training system. (See Figure 1.)
Implementation challenges and solutions
The first challenge Alibaba faced was organizational hesitancy: Engineers and leaders were wary of AI’s ability to positively engage with and resolve consumer and merchant queries. To overcome this hesitancy, the firm implemented a fast-fail strategy that continually tested the AI against human responses in small real-world experiments.
The pilot proved that the AI chatbots outperformed their human counterparts, delivering superior customer satisfaction scores and improving merchants’ first-contact resolution scores. Those results led the organization to fully buy in to implementing AI customer engagement services.
The second challenge involved two technical issues. Many initial customer queries involved a user-intent classification problem: Queries were stated similarly but had different underlying intents. For example,“I need help with my order†can mean needing help with tracking an order, getting a refund, or many other intents. In addition, many customer queries involved a long tail problem: extremely low likelihood of certain types of queries.
Since niche topics by nature generate less data, queries about them tend to lead to less accurate chatbot answers – which lowers customer satisfaction. To overcome the first technical problem, Alibaba developed an extensible multitask learning paradigm using a Meta Lifelong-Learning framework that learned robust text representations across tasks and employed a Least Recently Used (LRU) replacement policy to manage model deployment and memory resources.
To overcome the latter problem, Alibaba implemented a multi-grained interactive matching network for few-shot text classification that leverages a dynamic routing algorithm in meta-learning to better adapt and generalize unseen classes while also providing more memory-based flexibility.
AI vs. human results
Overall, Alibaba’s AI-based customer satisfaction scores exceeded or matched human interactions in most product categories. Crucially, it provided customers much quicker responses and enabled Alibaba to engage with customers at all hours of the day.
A/B testing results reveal that, in its first few weeks, Alibaba’s proactive AI-based intermediary dispute service resulted in a 25% increase in customer satisfaction over its previous non-AI-based dispute resolution procedures. Meanwhile, Alibaba’s training bot is assisting more than 1,500 customer service personnel daily. In addition, it is reducing customer service personnel training time by more than 20%.
Finally, Alibaba has realized cost savings of over a billion RMB annually (~US $150 million) by employing AI over humans for service engagements.
Lessons learned and next steps
Alibaba employs its five AI-based chatbots in more than 80 Alibaba-related apps, including Taobao, Xianyu, Fliggy, Hema, and Lazada. It took Alibaba developers one year to create the initial AI bot. However, this process has become much more efficient, and it now takes Alibaba’s developers only one month to develop a new AI bot for its platforms, although they have to continually improve each bot.
Alibaba has also learned that customers have accepted the chatbots and appreciate their role in resolving many, though not all, types of customer service queries. It has learned that it’s critical to constantly test the structure to see what types of help customers are willing to accept from AI chatbots.
A key lesson Alibaba learned was that, despite its chatbots’ success, AI cannot and should not completely replace human customer service agents. But each should be deployed in the scenarios that best suit their abilities. For simple FAQ questions, AI can directly reply to users. However, for complex complaints and disputes, AI can attend to labor-intensive tasks, such as collecting information on appeals and vouchers, and possibly make initial decisions, but humans will still need to review the information, as well as related non-standardized materials, to make final judgments.
Going forward, Alibaba will continually invest significant resources in making a seamless human-machine collaboration. For example, Alibaba’s AI bots continuously monitor whether customers encounter obstacles and whether the AI-based service can understand and resolve their queries.
Whenever appropriate, theAI bot automatically transfers customers to human service agents and then prompts and provides those agents with essential information to avoid asking the customers to repeatedly describe the problem. At the same time, the AI bot enhances customer interactions with human agents by providing data-informed solutions and particular phrasing recommendations, as well as issuing immediate warnings to human agents if they behave improperly.
Finally, Alibaba believes that, although technologically proficient AI natural language processing models and training data are important, establishing an organizational mindset which offers customer-centric AI-based solutions to efficiently solve customer problems is more important than just implementing AI.
So, Alibaba is continuing to refine its AI bots through hundreds of releases, iterations, and improvements over time, with the ultimate goal of solving customers’ problems in the most efficient and satisfying method possible, whether through AI or human service.
Authors
Yitong Wang is head of the Data Science and Business Insights Department in the Customer Experience Business Group at the Alibaba Group. Yitong earned his undergraduate and master’s degrees from Tsinghua University and his Ph.D. from the University of California, Irvine. His research has been published in the Journal of Consumer Psychology, Strategic Management Journal, and Journal of Experimental Psychology: General, among others.
Depin Chen is the director of applied algorithms in the Customer Experience Business Group at the Alibaba Group. Depin is an expert in developing recommender systems, conversational AI, anomaly detection, and more. He earned his Bachelor’s degree and Ph.D. at the University of Science and Technology of China, in 2005 and 2010, respectively. He has several publications in leading data and computer science conference proceedings such as ICDM, SIGIR, and PAKDD.
Ofer Mintz is an associate professor of marketing at the University of Technology Sydney, visiting associate professor of marketing at Tel Aviv University, and author of “The Post-Pandemic Business Playbook: Customer-Centric Solutions to Help Your Firm Grow.” His research focuses on marketing metrics, analytics, strategies, and marketing’s role in start-up firms. His research has been published in the Journal of Marketing, Marketing Science, and World Economic Forum, among other managerial and academic outlets.
Kehan Chen is manager of the Chatbot Algorithm Team in the Customer Experience Business Group leading the AliMe algorithm team at Alibaba. Kehan’s research interests include natural language understanding, human-computer interaction, and dialogue systems. Kehan earned his master’s degree from Zhejiang University, Hangzhou, China and has authored or co-authored several papers in leading data and computer science conference proceedings, including at Interspeech and KDD.
