Sinitic AI-enabled system solves Asian iGaming customer support automation’s 4 biggest problems
TAIPEI, August 22, 2018 – Natural language processing (NLP) company Sinitic has announced the launch of a customized customer support automation solution for the iGaming industry in Asia.
Sinitic’s solution is artificial intelligence (AI)-enabled and targets the biggest pain points for customer support automation for iGaming operators.
Because of unique market factors, customer support costs currently make up 50-60% of an Asian iGaming company’s Costs of Goods Sold. These costs are particularly high as companies tend to centralize their operations in the few regulated markets and therefore need to import a large number of staff for multilingual customer support teams.
Sinitic’s AI-enabled chatbots – conversational assistants that assist human customer support staff – give businesses the power to control and reduce these operational costs while expanding profits.
After analyzing several iGaming operators’ data, Sinitic discovered that, on average, 75% of chat is just mundane chit-chat, or FAQs. Only 22 percent is what is termed multiturn, or longer-form complaints. This means that 75% of the total customer support costs are being wasted on easy-to-automate conversations.
The Sinitic product suite streamlines operations by addressing the four biggest challenges for automation projects:
1. Operators spend months labelling their chat history so it can be used for chatbot dialogue. Sinitic’s BotBuilderTM uses a deep-learning algorithm to rapidly convert chat history into chatbot dialogue in a matter of hours.
2. Artificial intelligence can help increase agent productivity. Sinitic’s ChatCentreTM offers advanced features for agent aliases, multi-brand and language management, as well as agent and customer sentiment analysis.
3. The Sinitic CaseManagerTM combines chat and case management, allowing agents to focus on the most urgent customer complaints.
4. Lastly, the Sinitic BotTrainerTM fixes confused chatbots and automatically improves bots’ natural language understanding.
The entire product suite is powered with the SiniticNLPTM engine, which aggregates terminology from hundreds of thousands of chats from the iGaming industry, and supports key Asian languages such as Simplified Chinese, Traditional Chinese, Vietnamese, Thai, Japanese, and Bahasa Indonesia, among others. Crucially, it also uses a proprietary deep-learning algorithm to understand mixed languages including Chinglish, Taglish, and Singlish.
The accuracy is impressive. For a company that receives over 900,000 messages per day, SiniticNLPTM delivered 11% higher Chinese language understanding compared with Microsoft LUIS, using the exact same data without any additional training.
Big spending on software with no automation capability
In Asia, customer support tends to be via messaging platforms such as WeChat or Line, which means iGaming operators need customer support software that integrates support for all chat platforms. However, iGaming companies in the region have typically repurposed limited tools such as Zendesk for their customer support, which not only costs between $3,000 and $6,000 a month but is ill-suited for the iGaming environment. For example, it does not support multiple aliases for agents who may work across several brands, and does not offer chatbots capable of understanding Asian languages.
Another unique feature of the iGaming industry is that operators often do not wish to reveal that they are using chatbots. In the instance that the bot does not understand a question, a human agent will take over the conversation seamlessly, with the same name as the chatbot.
Sinitic has already signed with several Asian-facing operators, who will use the company’s proprietary AI-enabled products for customer support automation.
ABOUT SINITIC
With a pedigree in the banking and system integrator industries, Sinitic is a fast-growing, niche-focused Software-as-a-Service (SaaS) business. The company is headed by Curtis Matlock, a software sales director with experience adapting the SaaS business model to markets in Asia-Pacific, and Albert Zhuang, an award-winning computer science graduate from the National Taiwan University. Zhuang’s master thesis on coreference resolution won first distinction from the Association for Computational Linguistics and Chinese Language Understanding.
To learn more, visit sinitic.ai.
