There are many scenarios for the implementation of AI technology, among which there are many application scenarios for customer service and its related tracks. In this article, the author shares several popular applications of AI in the field of customer service at this stage, covering pre-sales, in-sales, and after-sales.
With the rapid development of artificial intelligence technology, customer service has become one of the important scenarios for AI implementation. However, in addition to customer service, at present, it has also developed a lot of interesting application scenarios in other related tracks of customer service.
What are the specific inclusions?What are the new trends in the application of AI in these tracks?
1. "AI+ Trading"
Although the application of AI in trading is not as early as that of customer service, its market value and development potential are growing rapidly.
For example, in the financial industry, AI is being widely used in financial data analysis, market trend forecasting, automated trading and other scenarios, in addition to giving traders effective stock trading suggestions in real time, it can also execute transactions when conditions are met.
In terms of assisting decision-making, AI uses AI large models and algorithms to analyze and predict market trends of massive data such as market indicators, economic reports, and news reports, and the processing speed of AI is very fast, and the accuracy of executing transactions is even higher than that of humans.
In the power and energy industry, Sipu Technology and others have launched AI power traders based on customer needs. This virtual trader also needs to be assessed and certified to work, and his ability to integrate industry research, market variable monitoring, price research and judgment, and trading decision-making is equal to one.
Relying on the industry knowledge base, the knowledge reserve of AI power traders is far beyond ordinary people, and they are aware of historical trading data, market trends, day-ahead/spot power, the latest policies and regulations, and even coal power prices and weather changes, which can help real traders do a better job in risk management to a greater extent and comprehensively improve the efficiency and economic benefits of station trading.
In the securities investment industry, AI has also become a robo-advisor to assist novice users and flexible investors in making trading decisions. In addition to absorbing the corresponding knowledge reserves, this AI trading advisor can also intelligently match users' financial goals and risk appetites through algorithms, and automatically give people corresponding portfolio references.
There are many similar cases. Although it is still difficult for AI to completely replace people in trading, AI is playing an increasingly important role in transaction risk management, trading strategy output, and automatic execution of transactions.
2. From human warfare to AI sales
In the era of pulling sales and increasing performance, there are many people and it seems to be powerful.
However, when the "crowd tactics" are difficult to bring corresponding performance growth to the company, it will exacerbate the phenomenon of redundancy and redundancy. With the "awakening" of AI sales capabilities, many companies have begun to actively or passively accelerate the pace of optimization of sales personnel.
The financial and insurance services industry is one of them.
According to incomplete statistics, in recent years, the scale of practitioners in the profession of insurance agents has declined by more than 50% compared with the peak period of various insurance companies, and the proportion of agents in some insurance companies has even declined by more than 80%.
In addition to market conditions, business optimization and other considerations, in fact, because the early insurance business was manpower-intensive and data-intensive, the dependence on manual operation was quite high.
With the strong penetration of AI in intelligence collection, competitive product analysis, demand communication, Q&A, insurance planning, underwriting and claims in the era of large models, many manual business processes are becoming automated, gradually promoting the adjustment of job structure and scale.
For example, some insurance service companies focus on the performance of LLMs in terms of language organization and output capabilities, and begin to use AI insurance sales to complete tasks such as pre-sales consultation, product introduction, competitor comparison, and insurance planning.
In this process, the original insurance salesperson can save a lot of trouble of collecting and sorting out and repeating answers, and can also effectively screen business opportunities and improve the quality of one-to-many customer services.
For example, in the past, insurance agents needed to spend a lot of time filling in health information for policyholders, collecting and sorting out underwriting materials, and communicating underwriting conclusions and rates.
At present, with the emergence of tools such as Sipu AI underwriter, there is no need for too many intermediate links, and after submitting underwriting materials, it can automatically identify, realize structured management of data, make underwriting decisions, output underwriting conclusions, and give corresponding rate rating references.
The significant reduction in manpower and time required to complete insurance and underwriting has led to new changes in the industry and accelerated the transformation of some practitioners.
On the other hand, the waiting period of the customer side has been greatly shortened, and the decision-making risk has been further reduced, which has also promoted the elite insurance agents and the overall performance of the company to achieve new growth.
3. Industry reshaping under AI marketing
According to an industry report published by Gartner, generative AI will be used to create 30% of outbound marketing content by 2025.
We don't yet know where AI marketing will develop in the future. However, in terms of customer marketing services alone, AI+ marketing has emerged many imaginative use scenarios.
For example, the identification of customer needs. Through guided multi-round dialogue, AI is rapidly moving closer to humans in terms of demand understanding, intent recognition, scene switching, and business navigation, which is convenient for targeted product promotion and personalized marketing services in the later stage.
Another example is the use of AIGC to automatically generate diversified marketing content, which is not only marketing speech and marketing content, but also AI has covered multiple modalities such as text, images, audio and video.
Some AI marketing service providers have also introduced the RLHF feedback system for marketing users on the basis of the original product to continuously optimize the quality of product generation and customer service experience through efficient data feedback.
In the field of e-commerce, some people also use AI to analyze and model historical marketing, which is used to optimize marketing strategies, achieve accurate customer acquisition, and create vertical large models.
In addition, the value of AI in marketing creativity and advertising process optimization is gradually being seen.
Fourth, the attacking AI customer service, the evaluation is divided into two levels
Customer service is an indispensable part of customer service. Moreover, compared with AI trading, sales and marketing, customer service is one of the earliest and most promising AI application fields.
According to the "2023 China Intelligent Customer Service Market Report", the domestic intelligent customer service market will exceed 6.6 billion in 2022, and it may approach 10 billion in the next two years.
From the perspective of the development curve, the two changes of intelligent customer service (turning to NLP after 2016 and using LLM to reconstruct customer service business in 2023) are closely related to the boom of artificial intelligence. At present, AI has achieved breakthrough application results in the fields of customer service, outbound calls, quality inspection, and think tanks.
According to media reports, some online customer service robots in China have been able to solve 80% of common problems independently. In the AI applications of leading communication companies, the proportion of intelligent customer service and intelligent services has even exceeded 85%.
Driven by strong artificial intelligence, the language understanding, dialogue, and business processing capabilities of intelligent customer service continue to evolve. With the emergence of AI digital employees, intelligent customer service can also appear in front of people in a more three-dimensional and intuitive form.
However, at present, the new and old intelligent customer service products are still largely in the delivery period. In practical applications, some intelligent customer service products are frequently complained about because they can't understand "human words", their functions are frivolous, their speech skills are dull, and the probability of transferring to manual work is high. Coupled with the sharp decline in manual customer service and poor suitability for the elderly, people's evaluation of it is somewhat polarized.
The number of customer service employees in banking financial institutions, data source: China Banking Association
Write at the end
The above are the popular applications of AI in the field of customer service at this stage, covering pre-sale, in-sale, and after-sale.
In fact, in the area of business processing, AI also shows great synergy, because it involves functional permissions, business integration, settlement, approval, etc., and is currently mainly focused on business navigation, information query, primary business handling, etc., and the degree of development is not high.
At present, the development of AI has shown people its great potential and some limitations, and people are full of expectations and hesitation.
However, with the in-depth development and standardized use of AI, the future business scenarios and the market value that can be brought by thousands of empowerments are still promoting its in-depth application in customer service and multiple fields.
Note: This article is original, the first release of sipu-tech, and everyone in the third-party launch is a product manager.
This article was originally published by @iseeworld on Everyone is a Product Manager. Reproduction without permission is prohibited.
The title image is from Unsplash and is licensed under CC0
The views in this article only represent the author's own, everyone is a product manager, and the platform only provides information storage space services.