Workflow
自然语言处理(NLP)
icon
Search documents
售前客服缺乏促单技巧,电商高询单却低转化
Sou Hu Cai Jing· 2025-09-23 05:29
在电商竞争日益激烈的环境下,不少企业面临一个共同困境:店铺咨询量高,但最终成交率却偏低。数据显示,若客户咨询后24小时内未下单,90%的客户 将会流失。这背后往往存在一个关键原因——售前客服缺乏有效的促单技巧。 高询单低转化问题的根源 客户主动咨询时,往往已具备购买意向,是促成交易的宝贵机会。然而,许多客服人员未能把握住此类机会,主要原因包括: 智能客服Agent的解决方案 针对上述问题,智能客服Agent提供了一套完整解决方案,可有效提升售前转化率: 响应迟缓,消耗客户耐心:研究表明,客服响应时间超过30秒,客户流失率增加40%;若超过1分钟,65%的客户会直接关闭对话框。很多客服因同时 处理多个对话导致回复延迟,或使用生硬、模板化的应答,缺乏亲和力。 缺乏主动挖掘需求的能力:客户有时并不清楚自身需求,客服未能通过有效提问进行引导,挖掘潜在需求。往往仅机械回答客户问题,未能深入理解 其真实意图。 产品知识不专业,难以建立信任:面对客户疑问,客服回答不够准确专业,常使用"可能是""不太清楚"等模糊表述,无法使客户放心。 缺乏促单技巧,错失成交时机:即便客户已表露购买意向,客服也未能及时识别购买信号并促成交易, ...
彭博数据洞察 | 化情绪为价值:NLP如何解读新闻标题情绪,捕捉交易信号?
彭博Bloomberg· 2025-09-18 06:05
以数据聚焦重点,重点永不失焦!欢迎阅读 "彭博数据洞察" 月报,基于超过8000个彭博企业 数据集,为您提供有关市场热点问题、最新趋势的深度分析与洞见。您可点击文末 "阅读原文" 链接,联系我们预约有关数据服务的演示。 扫描二维码 立即订阅 彭博数据洞察月报 本期聚焦: 业务分类法重塑基金风险敞口画像;NLP解读新闻情绪;增强版OHLC捕捉价格波 动 衡量基金在贸易格局中的风险敞口 在地缘政治高度紧张的背景下,理解贸易博弈等宏观主题如何影响投资组合至关重要。一种 有效的方法是将行业分类数据与基金持仓数据相结合来量化基金的风险敞口,在传统报告框 架之外,更精准地评估基金在各行业的实际风险敞口情况。 图表1对一只基金的行业构成进行比较,分别采用传统方法和业务分类数据。分类数据能更 细致展现基金实际的行业风险敞口。在示例中,如果不采用业务分类数据(图中紫色部 分),基金看起来对公用事业板块的敞口远高于能源板块;而通过业务分类数据(蓝色)计 算后,行业敞口则呈现更均衡的状态。 图表1:基金行业构成(BICS一级分类)对比:传统方法 vs. 业务分类法 方法: 传统方法是为每家公司分配一个单一行业,并采用自下而上的汇总 ...
《工业企业数据质量治理进阶实践指南白皮书》重磅发布
Zhong Guo Fa Zhan Wang· 2025-08-22 08:36
Core Insights - The article emphasizes the importance of data quality governance for industrial enterprises in the context of the digital economy and new industrialization [1] - It highlights the challenges faced by traditional industrial companies in effectively transforming vast amounts of data into actionable insights due to issues like "data silos" and "data inaccuracy" [1] Group 1: Data Governance Concepts - The white paper clarifies key concepts related to data governance, such as master data, static data, source governance, and end governance, providing a solid theoretical foundation for practical guidance [2] - This clarification helps enterprises to plan governance strategies from a holistic perspective rather than a fragmented one [2] Group 2: Data Governance Maturity Model - The white paper introduces a five-stage maturity model for data quality governance in industrial enterprises, derived from extensive research on domestic and international practices [3] - This model outlines a progression from basic standards to intelligent governance, enabling companies to accurately identify their current stage and set clear goals for advancement [3] Group 3: Stages of Data Governance - **Stage 1: Coding Management (Initiation Stage)** - Focuses on establishing unified coding rules to resolve data identification issues, emphasizing the importance of foundational governance [4] - **Stage 2: Master Data Management (Transition Stage)** - Expands governance to standardizing shared data, ensuring consistency and accuracy of core master data across the enterprise [5] - **Stage 3: Static Data Governance (Breakthrough Stage)** - Involves comprehensive governance of all static data, enhancing quality control through business logic validation and algorithmic checks [6] - **Stage 4: Source and End Collaboration Governance (Mature Stage)** - Represents a mature phase where governance covers the entire data lifecycle, ensuring data is reliable and usable in decision-making [7] - **Stage 5: Intelligent All-Domain Governance (Intelligent Stage)** - Aims to govern unstructured data using advanced technologies like AI and NLP, significantly improving governance efficiency [9] Group 4: Value and Outlook - The release of the white paper provides significant industry value by offering a complete action guide for industrial enterprises struggling with data issues, helping them save time and costs [10] - It promotes standardized concepts and frameworks to enhance communication and collaboration across different departments and stakeholders [10] - The white paper serves as a valuable resource for Chief Data Officers, IT leaders, and decision-makers, aiding in the strategic transformation of data governance into a value-creating asset [10]
国投瑞银殷瑞飞—— 破解超额收益困局 三大路径应对“Alpha”衰减
Zheng Quan Shi Bao· 2025-08-17 17:45
Core Insights - The article discusses the robust growth of index investment in a favorable market environment, highlighting the accelerated layout of public funds in index and index-enhanced areas, exemplified by Guotou Ruijin Fund's launch of 7 out of 9 new products as index funds and index-enhanced funds this year [1][9] Group 1: Alpha Decay and Risk Control - The manager emphasizes a clear strategy to address the challenge of Alpha decay due to improved market pricing efficiency, accepting the reality of narrowing Alpha while refusing to compromise on risk control [1][2] - The approach includes traditional methods optimization, broadening investment frameworks with AI strategies, and expanding data dimensions to include non-structured data for better investment decision-making [2][3] Group 2: Research Team and Core Competencies - The team boasts a strong research foundation with members from prestigious institutions, half holding PhDs, covering fields like mathematics, statistics, and data science, which supports high-level quantitative research [4] - The research system balances Alpha and Beta studies, enhancing stock selection and industry allocation capabilities across various domains, including index investment and machine learning [4] Group 3: Business Segmentation and Product Strategy - The manager outlines three business segments: index funds for efficient investment, index-enhanced funds for stable excess returns, and active quantitative funds focusing on deep Alpha extraction [5] - A layered product architecture is being developed, resembling a star map with "stars" as core products, "planets" for growth engines, and "satellites" for capturing structural opportunities [6][7] Group 4: Future Outlook - The manager expresses optimism towards two main directions: low-volatility dividend stocks appealing to risk-averse investors and high-growth assets aligned with China's economic transformation and industry upgrades [8]
电商一键上货软件怎么选?首先掌握其核心运行逻辑,看这篇就够了
Sou Hu Cai Jing· 2025-08-04 11:21
Core Insights - The rise of "one-click listing" is driven by the need for efficiency in the e-commerce sector, as traditional manual listing methods become bottlenecks for business expansion [2] - The global AI market in e-commerce is projected to reach $7.25 billion by 2024, highlighting the urgency for merchants to enhance operational efficiency [2] - The transformation from manual input to AI-driven processes represents a significant cognitive revolution in the digital commerce landscape [12] Group 1: Efficiency and Automation - "One-click listing" is not merely a convenience but a necessity for survival in a highly competitive market where speed and accuracy are critical [2] - AI technologies such as Natural Language Processing (NLP) and Computer Vision are essential for automating product information extraction and management [4] - The integration of generative AI allows for the creation of compelling product titles and descriptions, enhancing marketing efforts and reducing content creation costs for small businesses [6] Group 2: AI Agents and Workflow Management - The ultimate form of "one-click listing" involves an AI agent that autonomously manages various tasks, acting as a virtual operations expert [8] - Advanced AI agents can interact directly with user interfaces, bypassing traditional API limitations and enabling seamless automation across different platforms [9] - This shift towards autonomous commerce signifies a new era where AI systems collaborate independently, enhancing operational efficiency [9] Group 3: Impact on E-commerce Operations - The value of "one-click listing" extends beyond product listing, influencing the entire e-commerce operational chain, including inventory management and personalized marketing [11] - AI-enhanced data can improve inventory forecasting accuracy, potentially reducing stock levels by 20% to 30% without compromising service quality [11] - Personalized experiences driven by precise user and product tagging can significantly increase consumer purchasing preferences [11] Group 4: Challenges and Future Directions - The path to full automation is challenged by the quality of input data, adhering to the "Garbage in, garbage out" principle [12] - Ethical concerns such as data privacy and algorithmic bias remain critical issues in AI applications [12] - The future of e-commerce is moving towards an "agent-first" IT architecture, where systems are designed for machine collaboration rather than human interaction [12]
线下活动邀请|探索外汇、固收及贵金属领域量化交易新机遇
Refinitiv路孚特· 2025-07-24 05:12
Core Insights - The article emphasizes the capabilities of Tick History, a cloud-based historical real-time pricing data service that provides access to over 45PB of standardized data from more than 500 trading venues and third-party quote providers [3][4]. Group 1: Tick History Overview - Tick History encompasses over 1 billion tools and has historical data spanning 25 years, amounting to over 87 trillion transactions, enabling users to explore vast market opportunities [2]. - The service offers a consistent data experience across all exchanges, with options to view data in standardized or raw formats [3]. Group 2: Core Solutions - Tick History - Data Packet Capture (PCAP) is a cloud-based repository exceeding 20PB of high-quality global market data, allowing direct access to data center-level information [4]. - The Tick History query feature, supported by Google® BigQuery, enables users to access and analyze massive datasets within minutes [5]. Group 3: Analytical Tools - Tick History Workbench provides standard tools and a Springboard to focus on analyzing market microstructure, trading strategies, or execution quality [6]. - MarketPsych offers a suite of AI-based natural language processing (NLP) solutions, delivering data feeds and predictive insights from real-time, multilingual news, social media, and financial documents [8]. Group 4: Key Services - The service digitizes data from major countries, commodities, currencies, cryptocurrencies, stock sectors, and both public and private companies into machine-readable values and signals [9]. - An emotional framework is established to measure sentiments from extensive news and social media content, including optimism, anger, urgency, and financial language [10]. Group 5: Applications - The solutions are designed to create and enhance trading strategies and predict volatility [11].
AI生成行业趋势报告指南_一躺科技
Sou Hu Cai Jing· 2025-07-21 12:14
各位科技小达人、数据爱好者们,你们知道吗?今天咱来聊聊超火的AI生成行业趋势报告这事儿。这就好比给大家打开一个神秘的科技宝箱,看 看里面都藏着啥宝贝。 真的是,AI生成行业趋势报告这事儿门道太多了。本指南的数据截至2025年5月,行业动态还得结合SimilarWeb最新报告持续更新。大家要不要也 试试用这些技术和工具,一起在AI的浪潮里乘风破浪? 操作流程和优化策略也很重要。数据准备阶段,优先采用API接口和结构化数据库,还要把重复率大于15%的数据剔除,用KNN算法填补缺失 值。模板配置与逻辑设置时,支持用户自定义行业指标权重,还嵌入了时间序列模型和聚类算法。生成与审核机制也很严格,单份万字报告输出 时间小于3分钟,还支持多格式导出,人工还要校验关键数据源的可靠性,修正模型误判。 先说这技术原理和核心模块。自然语言处理(NLP)就像一个超级翻译官,能把文本数据的意思解析出来,还能自动识别行业术语,就像在金融 领域能提取财报关键指标,在医疗领域能给病历数据做标准化处理。机器学习和深度学习呢,就像一个超级预言家,通过历史数据训练预测模 型,能识别行业的周期性波动和新兴趋势。比如说零售行业的销售预测模型,准确率高 ...
潮玩公司TOYCITY表示下阶段拼的是更智能和拟人化
Core Insights - TOYCITY has launched the world's first emotion-aware AI companion toy, Xiaoba AI, aimed at addressing emotional needs in modern society, particularly for working women and children in dual-income families [1][7] - The company is based in Shipa Town, Dongguan, known as a hub for toy production, with over 4,000 toy manufacturers and a significant share of China's toy export market [2][1] - The AI emotional companionship sector is rapidly growing, with various applications emerging globally, including Character.AI and Replika, driven by advancements in natural language processing and machine learning [3][4] Company Overview - TOYCITY is recognized as a leading company in the innovation and incubation of original brands within the toy industry, with an annual production value close to 12 billion yuan [2][1] - The company has invested heavily in AI development, employing around 30 engineers and collaborating with partners like Lexin and Volcano Engine for technical support [5][6] Product Features - Xiaoba AI incorporates features such as emotion recognition through voice interaction, intelligent assistance, and data security with encrypted personal memories [6][7] - The product aims to blend technology with emotional warmth, focusing on emotional companionship, intelligent interaction, and collectible appeal [7] Market Context - The AI emotional companionship market is considered one of the hottest sectors in the AI application wave, with various companies exploring this niche [3][4] - Despite some skepticism regarding the necessity of AI emotional companions, the market continues to grow, fueled by high-profile endorsements and technological advancements [4][3]
谷歌发布Gemini嵌入模型,拓展基础层NLP能力
Investment Rating - The report does not explicitly provide an investment rating for the industry or specific companies involved. Core Insights - Google's release of the Gemini embedding model marks a significant advancement in NLP capabilities, achieving a score of 68.37 on the MTEB, surpassing OpenAI's 58.93, establishing it as the leading embedding model [1][12] - The ultra-low pricing strategy of $0.15 per million tokens is expected to democratize access to embedding capabilities, significantly lowering barriers for small and medium businesses, educators, and freelancers [2][14] - The Gemini model enhances Google's AI infrastructure, transitioning from content generation to a comprehensive semantic understanding platform, reinforcing its competitive edge in the AI workflow [3][15] Summary by Sections Event - On July 15, 2025, Google launched the Gemini embedding model, achieving a record score of 68.37 on the MTEB, and set a competitive price of $0.15 per million tokens [1][12] Commentary - The Gemini model excels across nine major task categories, showcasing its versatility and strong performance in various applications such as semantic retrieval and classification [2][13] - The aggressive pricing strategy is anticipated to disrupt the market, compelling competitors to reassess their pricing structures [5][18] Strategic Implications - The introduction of the Gemini embedding model signifies a strategic shift for Google, enhancing its capabilities in AI systems that require task matching and context retention [3][16] - The embedding layer is projected to become a new value center in AI workflows, indicating a transition from compute-centric to semantic-centric infrastructure [5][18]
马斯克推出二次元“AI女友”,但AI陪伴赛道已充满泡沫
Hua Er Jie Jian Wen· 2025-07-17 02:10
Core Insights - Elon Musk's AI company xAI has launched a new feature called "companions" for its AI chatbot Grok, aimed at providing immersive and emotionally engaging interactions [2] - The initial characters for this feature include a gothic-style girl named Ani and a cartoon panda named Bad Rudy, both of which have 3D animated representations [2] - The "companions" service is currently available only to users of the SuperGrok subscription service, which costs $30 per month [2] Industry Overview - The AI emotional companionship sector is one of the hottest areas in the current wave of AI applications, providing personalized emotional support and social interaction [4] - The global AI companion market is projected to reach $28.19 billion in 2024, with a compound annual growth rate (CAGR) of 30.8% expected from 2025 to 2030, potentially reaching $140.75 billion by 2030 [5] - Despite initial rapid growth, the sector is showing signs of cooling, with user growth and engagement metrics declining for some key players like Character.AI [5][6] Market Dynamics - Character.AI experienced a surge in users, reaching 22 million monthly active users by August 2024, but has since seen a drop in engagement, with monthly visits falling from over 200 million to 180 million [5] - Other applications, such as Byte's Cat Box and MiniMax Starry Sky, have also reported significant declines in monthly downloads and daily active users [6] - The industry faces challenges in addressing ethical concerns and identifying genuine user needs, with some critics labeling AI companionship as a "pseudo-demand" [6]