疫情之下反涨27.3% ？！！居然只用了这一个方法！
Case.1 快速调整商品Case.1 Quick adjustment of products
针对以精细化为基础的企业、组织，如何降低设计生产、物流仓储中的成本，是重中之重。
For enterprises and organizations based on refinement,how to reduce the cost of design,production,logistics and warehousing is the top priority.
某日系头部快时尚品牌就利用场景化知识图谱很好的做到了这一点。A Japanese head fast fashion brand has achieved this by using the scenario Knowledge Graph.
场景化知识图谱将线下一线人员的反馈处理成可视化的信息知识，辅助该企业在商品设计上进行快速调整，从收集到最终发售不到两个月。The scenario Knowledge Graph can process the feedback of offline front-line personnel into visual information knowledge, and assist the enterprise to make rapid adjustment in commodity design. It takes less than two months from collection to final sale.
由于信息源来自于一线，切合了消费者的当下需求，即便在新冠疫情的冲击下，销量依旧大涨（27.3%），远高预期。As the information source comes from the front line, which meets the current needs of consumers, even under the impact of the new crown epidemic, the sales volume still soared (27.3%), far higher than expected.
Case.2 私域流量下的用户留存 Case.2 User retention under private domain traffic
如何提高转化率、如何让用户留存是私域流量变现的重点课题。How to improve the conversion rate and how to keep users are the key issues of private domain traffic realization.
某头部服装品牌就将场景化知识图谱赋能于小程序商城的搜索场景，通过更智能的搜索联想、基于场景的搜索条件，让用户即使在十分口语的描述下也能搜索到符合需求的商品。A certain head clothing brand endows the scenario Knowledge Graph to the searchscenario of the small program mall. Through more intelligent search Association andscenario-based search conditions, users can search for products that meet the demand even under the very colloquial description.
通过场景化知识图谱的落地应用，品牌的商品点击率提升近 3 倍，商品加购率增幅达66%，购买转化率增幅达143% 。
Through the application of scenario Knowledge Graph, the click through rate of brand goods has been increased by nearly three times, the rate of additional purchase of goods has increased by 66%, and the purchase conversion rate has increased by 143%.
两个案例的共同点，在于都运用了场景化的知识图谱。What both cases have in common is that they use scenario-based Knowledge Graph.
什么是知识图谱？What is the Knowledge Graph？
知识图谱（Knowledge Graph），是一种基于图数据结构，以图的方式存储知识，并 将信息加工和推理，用以表达成更接近人类认知形式的语义网络。KG is a kind of semantic network based on graph data structure, which stores knowledge in the form of graph, and processes and infers information to express it into a semantic network closer to the form of human cognition.
知识图谱的特点: 1. 大量且扁平的维度。2. 深度加工。3. 结果可视化。Characteristics of KG: 1. Large and flat dimensions. 2.Deep processing.3.The results were visualized.
人工智能可以分为符号主义、连接主义、行为主义三个流派，而知识图谱即归属于符号主义流派，符号主义与连接主义走向融合，二者共同作用于行为主义，综合多种技术为垂直领域封闭场景提供更智能的解决方案。Artificial intelligence can be divided into three schools: Semiotics, connectivism and behaviorism. The Knowledge Graph belongs to the school of semiotics. Semiotics and connectionism tend to merge. They work together to act on behaviorism and integrate various technologies to provide more intelligent solutions for closed scenario in vertical fields.
什么是场景化的知识图谱？What is a scenario-based Knowledge Graph?
场景化的知识图谱，基于垂直的场景，让范围缩小、让理解更准，同时，能够更快落地。
The scenario-based Knowledge Graph is based on a vertical scenario, which narrows the scope and makes the understanding more accurate, and at the same time, can go online faster.**应用在消费领域，可以缩短消费者的决策周期，进而刺激消费。Applied in the field of consumption, it can shorten the decision-making cycle of consumers and stimulate consumption.
为什么需要场景化的知识图谱？Why do you need a scenario-based Knowledge Graph？
信息的爆炸式增长，导致精准信息获取的难度增加、复杂度提升。The explosive growth of information leads to the difficulty and complexity of accurate information acquisition.
据艾瑞咨询统计推算，2019 年涵盖大数据分析预测、领域知识图谱及 NLP 应用的大数据智能市场规模约为106.6 亿元，预计 2023 年将突破 300 亿元，年复合增长率为30.8%。According to the calculation of Research consulting statistics, the scale of big data intelligent market covering big data analysis and prediction, domain Knowledge Graph and NLP application will be about 10.66 billion yuan in 2019, and it is expected to exceed 30 billion yuan in 2023, with an annual compound growth rate of 30.8%.
巧用技术，赋能品牌
Make good use of new technologies to enhance brand competitiveness
场景化的知识图谱的落地，一方面依赖于技术，另一方面依赖于已有的业务领域的知识积累。这两个门槛导致企业、组织自行搭建知识图谱的难度很高，投入产出比较低。The implementation of scenario Knowledge Graph depends on Technology on the one hand, and on the accumulation of knowledge in existing business areas on the other hand. These two thresholds make it very difficult for enterprises and organizations to build their own Knowledge Graph, and the input-output is relatively low.
若想要通过场景化知识图谱提升企业、组织效益，可寻找较为成熟的现有解决方案，一方面可以快速获得已经积累下的行业知识，更轻量、更快速的落地，另一方面也对效益有更好的保障。If you want to improve the efficiency of enterprises and organizations through the scenario Knowledge Graph, you can find a more mature existing solution. On the one hand, you can quickly obtain the accumulated industry knowledge, and realize it more lightweight and faster. On the other hand, it also has a better guarantee for the benefits.
云享智慧帮你解决Applesay Intelligence helps you solve the problem
云享智慧AppleSay专注于大消费领域的场景化知识图谱建设。深耕各场景需求建立特定场景下的实体关系，能很好解决消费领域的各种场景。The company focuses on the construction of Knowledge Graph in vertical field. Deep ploughing needs to establish entity relationship in specific scenarios. It can well solve various scenarios in the consumer field.
利用先进的场景算法和知识图谱搭建技术，为品牌客户在具体业务场景定制能带来显著业务提升的人工智能解决方案。Using advanced scenario algorithm and Knowledge Graph building technology, we can customize artificial intelligence solutions that can bring significant business improvement for brand customers in specific business scenarios.
放心交给云享，它会给你带来惊喜。Trust Applesay, it will bring you surprise.
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