Data intelligence industry innovation service media
——Focus on digital intelligence and change business
Since its listing on the main board of the Hong Kong Stock Exchange on June 30, 2022, Tianrun Rongtong has continuously increased its investment in AI research and development, and established a product development goal of reconstructing customer contact systems and processes with "AI native" thinking. In the past year, Tianrun Rongtong launched a number of innovative AI products. These innovative products have promoted the rapid growth of the company in terms of the number of customers and revenue scale.
Since the beginning of this year, the rapid development of large language models represented by ChatGPT has injected strong impetus into the implementation of artificial intelligence in various application scenarios. At the same time, the big language model also activates the imagination of customers and encourages customers to actively participate in the innovation of AI applications. This important innovative force will accelerate the "moment of qualitative change" of customer contact intelligence.
Wu Qiang, founder and CEO of Tianrun Rongtong
From self-service to intelligence: the evolution of AI in customer contact
With the rapid development of AI technology, the traditional "self-service" customer contact is undergoing a transformation. In the past, customers had to deal with templated self-service systems to interact with businesses. This makes it difficult to meet customer needs, and there is always a gap between enterprises and customers. However, innovative technologies such as large language models in AI technology have brought revolutionary changes to customer contact, which can identify customer intentions, emotions and needs, and give personalized and targeted responses.
In Tianrun Rongtong’s view, the development process of customer contact from self-service to intelligence can be divided into four stages:
Era 1.0: Basic Self-Service
The business jump is realized through button configuration, and the required business can be selected according to the prompt tone. If the configuration is not covered, it can only be transferred to manual service.
2.0 Era: Based on Rich Keyword Matching Technology
At this stage, it can be understood that the business jump is performed when the system preset keywords are triggered by voice or text, which supports more services than the button mode, but the maintenance cost is high, and when the keywords change, the matching cannot be successful. .
Era 3.0: Semantic Understanding Based on Deep Learning
Through deep learning technology, such as convolutional neural network, cyclic neural network, etc., to analyze a large amount of data, AI customer service can accurately understand the user's intention, so that customers can feel that it has a sense of reality close to human intelligence. In addition, at this stage, technical means such as multimodal learning can also be applied to realize natural language understanding, dialogue management and other capabilities, and provide users with multi-dimensional experience services.
4.0 Era: Vertical Industry Application of Large Language Models
The parameter scale is larger (hundred billions or trillions), and it can use industry data and knowledge to provide more accurate and efficient solutions to better meet the needs and expectations of users in a certain field or scenario. Understand the special needs of users and provide more perfect AI solutions.
From "quantitative change" to "qualitative change"
The continuous upgrading of AI technology is a process of "quantitative change". The early models were small in scale and low in complexity, and could only handle some simple, special, and target-specific tasks, which had great limitations. With the continuous improvement of computing power, the scale of AI models is getting bigger and bigger, and the structure is getting more and more complex, AI technology has the ability of sustainable learning, in-depth analysis, and generative. It is this kind of "quantitative change" in technology that directly promotes The "qualitative change" at the application level has opened the "moment of qualitative change" in the field of customer contact , which we define as the following characteristics:
1. Out of the box
Early stage: The application of an AI model needs to go through processes such as data preparation, model evaluation, and model tuning, which usually take a long time, have high comprehensive costs, and slow value presentation.
The moment of qualitative change: Based on massive parameters and content reserves, the cold start capability of the enterprise's intelligent business is improved, and it can be put into business scenarios after deployment, with low comprehensive cost, fast launch, and rapid presentation of business value.
2. Multi-mode integration
Early stage: Independent AI models need to be deployed in different business scenarios, such as multilingual translation, text-to-speech conversion, quality inspection, robots, etc. The integration cost is high, maintenance complexity is high, and the island effect leads to information fragmentation, which cannot be maximized. business value.
The moment of qualitative change: A set of large language model technology can realize multiple task processing, low deployment cost, comprehensive decision-making of data sharing, and can maximize business value.
3. Emotional interaction
Early stage: Rules and strategies need to be formulated manually, and the needs of different customers cannot be individually handled. The cold and blunt robot service capabilities cannot fully release the value output of human agents.
Moment of qualitative change: Through the real-time analysis of the conversation process, it can effectively monitor the customer's emotional state and communication intention, etc., and superimpose emotions such as comfort, guidance, and approval on the response words, maintain emotional resonance with customers, and improve semantic and emotional understanding. , improve the accuracy of consultation responses, customer satisfaction, and reduce the rate of customer complaints.
4. Self-evolution
Early stage: By manually extracting, creating, sorting and screening various documents, pictures, videos and other information, the comprehensive maintenance cost is high, the update speed is slow, and the knowledge applied to the front line is seriously lagging behind, which cannot effectively support services, clues/ Business opportunity conversion process.
Moment of qualitative change: Integrating all kinds of knowledge documents/pictures/videos and other knowledge base information can automatically extract, analyze, aggregate, reason, retrieve, generate, etc., with extremely low maintenance costs and automatically completed by machines, knowledge base Always keeping up-to-date can provide strong support to the front line, improve customer satisfaction and business goal achievement rate.
Based on the in-depth insight into the large language model, Tianrun Rongtong upgraded itself, completed the comprehensive integration of AI products and large language model technology, and opened the "qualitative change" moment of Tianrun Rongtong.
With AI native as the engine, what kind of new experience will the big language model bring to the enterprise?
In the past few months, from ChatGPT to Wenxin Yiyan, Tongyi Qianwen, Xinghuo Cognitive Model, 360 Smart Brain, etc., major manufacturers have launched their own general-purpose large language model products. Tianrun Rongtong More attention is paid to how to quickly realize the real implementation of large language models in customer contact scenarios and bring more value to enterprises.
Wu Qiang said that Tianrun Rongtong will be driven by AI technology to build a new customer contact experience of "human-machine integration", helping customers achieve the goals of efficient assistance, close collaboration, and deep insight.
Tianrun Rongtong Human-Machine Fusion Capability Matrix
Through the integration of self-developed AI products and large language model technology, the field of customer contact will bring three major value enhancements to the enterprise:
1. Efficient assistance to create a new customer experience of human-machine integration
In the past, when enterprises provided customer service, they used a large number of manual receptionists, and the emergence of robots can effectively release manual labor from low-value work. Let humans focus more on solving complex problems.
Although the ability of robots has made remarkable progress in many aspects, "human assistance" and "assisted decision-making" are also crucial in business scenarios. In the past, the training cycle of professional customer service personnel was long, the cost was high, and the low success rate was also a long-term problem faced by enterprises. With the help of agent assistants, the professionalism of newcomers can be greatly improved. For example, speech navigation according to business rules, customer consultation content automatically matched with the best speech recommendation, automatic filling of work orders, etc., so that the work efficiency and professionalism of manual customer service can be significantly changed, so as to enhance the professional brand image of the enterprise and customer experience.
In the process of customer service, enterprises will accumulate a large amount of valuable conversational information. However, this information is often not used effectively. Through in-depth conversation analysis technology, it is possible to effectively extract and analyze key information generated by customers during the dialogue process, helping enterprises improve service quality and product promotion strategies.
2. Close collaboration to build a knowledge base for the rapid development of enterprises
The three core business scenarios of marketing, sales, and service cannot do without a large number of knowledge bases as an important guarantee for business development. Previously, the knowledge owned by enterprises was often scattered among different departments and different people, making it difficult to form a knowledge base that effectively supports business development. Traditional knowledge management requires a lot of time and labor costs, and it is difficult to quickly respond to customer needs. However, through the vertical application of large language models, enterprises can easily realize efficient knowledge management, including one-click FAQ writing, document knowledge extraction, document-based learning and answering, and automatic learning of knowledge in the contact process . Agents maintain the accuracy and timeliness of knowledge during the customer contact process, and transform knowledge achievements into performance value.
Traditional multi-department business collaboration scenarios often have the following shortcomings: cumbersome processes and low efficiency. Customer service personnel need to manually create and process work orders, resulting in slow processing speed and long feedback cycle, which seriously affects customer service efficiency. Through the context understanding ability of AI conversation analysis, combined with the automatic clustering of work orders and content analysis technology, it can timely capture customer needs, automatically generate work orders, automatically assign processing personnel, automatically update progress, notify customers in real time, etc., thereby optimizing the traditional multi- department Business collaboration scenarios, improve the efficiency of work order processing and full-closed-loop service efficiency, and improve the quality and effect of customer service.
3. Deep insight, turning the ever-changing market into a predictable opportunity
At present, the common difficulties encountered by many enterprises in the course of operation are largely due to the difficulty in getting through the data generated during the contact process between enterprises and customers, the large volume is difficult to analyze, the output of multi-department and multi-role business analysis reports takes a long time, Business value is difficult to mine and other problems, which have caused a huge waste of data resources. Relying on the integration of AI products and big language model technology, the data can be further aggregated, cleaned, mined and visualized, such as displaying customer voice, incoming line intention, high-frequency problems, golden speech skills, service level, service efficiency, customer satisfaction degree, customer sentiment, etc. The effective awakening of a large amount of data can provide an optimization basis for the business strategy of the company and help the company improve its market competitiveness.
It's the beginning, it's the future
For service providers in various fields, AI technology represented by large language models is not only a competition with technology, but also a race against time. This not only tests the service provider's understanding of new scenarios and new needs, but also tests who can create more unexpected surprises for customers through technology application, data accumulation and rapid iteration of service capabilities.
We have reason to believe that the big language model is just the beginning, and the integration of AI and industry will have a better future. As the head enterprise of intelligent customer service, Tianrun Rongtong will wait and see what trends it will have in the future.
