随着互联网技术的不断发展和普及，电商行业在过去几十年中取得了巨大的发展和变革。从供小于求的“以商品为主”阶段，到享受时代红利的“以流量为主”阶段，再到重视消费者体验的“精细化运营”阶段，电商市场正在进入以消费者为中心精细化运营时代，这要求电商企业从存量市场中挖掘潜力，从增量市场中寻找机会。

　　With the continuous development and popularization of Internet technology, e-commerce industry has made tremendous development and change in the past decades. From the stage of "commodity oriented" where supply is less than demand, to the stage of "traffic oriented" where the benefits of the times are enjoyed, and then to the stage of "refined operation" that values consumer experience, the e-commerce market is entering the era of consumer centered refined operation. This requires e-commerce enterprises to tap into the potential of existing markets and seek opportunities from incremental markets.

　　电商行业的数据驱动目标是利用数据来指导和支持业务决策，以实现提升营销效果、优化运营效率、提升用户体验、发现商机和创新等目标。

　　The data-driven goal of the e-commerce industry is to use data to guide and support business decisions, in order to achieve goals such as improving marketing effectiveness, optimizing operational efficiency, enhancing user experience, discovering business opportunities, and innovation.

　　但随着电商行业的数字化发展，电商行业的数据驱动中有三个特别明显的问题。

　　But with the digital development of the e-commerce industry, there are three particularly obvious problems in the data-driven approach of the e-commerce industry.

　　01

　　01

　　电商行业数据驱动的现存问题

　　The Existing Problems of Data Driven E-commerce Industry

　　1. 电商数据获取难

　　1. Difficulty in obtaining e-commerce data

　　电商平台众多，每个平台的数据获取接口各不相同，这导致了企业在获取数据时面临困难。缺乏统一的数据接口和集成方案，使得企业需要花费大量的时间和精力去从各个平台手动导出数据，这不仅效率低下，还容易出现数据遗漏和不准确的情况。

　　There are numerous e-commerce platforms with different data acquisition interfaces, which makes it difficult for enterprises to obtain data. The lack of a unified data interface and integration solution requires enterprises to spend a lot of time and effort manually exporting data from various platforms, which is not only inefficient but also prone to data omissions and inaccuracies.

　　2. 数据加工整合难

　　2. Difficulty in data processing and integration

　　电商数据分散在各个平台、系统和部门中，没有统一的储存地方和标准化处理方式。这导致了数据加工整合的困难，需要耗费大量的时间和资源来进行数据清洗、转换和整合，以形成可用于分析和决策的统一数据集。

　　E-commerce data is scattered across various platforms, systems, and departments, without a unified storage location or standardized processing method. This leads to difficulties in data processing and integration, requiring a significant amount of time and resources for data cleaning, transformation, and integration to form a unified dataset that can be used for analysis and decision-making.

　　3. 数据业务分析难

　　3. Difficulty in data business analysis

　　电商数据分析需要与实际业务场景相结合，以赋能企业在决策和运营中发挥数据的价值。然而，许多企业在这方面还存在不足，缺乏有效的数据分析场景和工具，无法将数据转化为实际的业务洞察和行动计划。

　　E-commerce data analysis needs to be combined with actual business scenarios to empower enterprises to leverage the value of data in decision-making and operations. However, many enterprises still have shortcomings in this regard, lacking effective data analysis scenarios and tools to transform data into practical business insights and action plans.

　　面对电商行业中的各种困境和挑战，寻找切实可行的解决方案成为了企业前进的关键。只有通过合理的策略和有效的措施，才能解决问题，实现数据驱动电商精细化运营的目标，推动业务的持续增长和发展。

　　Faced with various difficulties and challenges in the e-commerce industry, finding practical and feasible solutions has become the key for enterprises to move forward. Only through reasonable strategies and effective measures can problems be solved, the goal of data-driven e-commerce refined operation be achieved, and the sustained growth and development of the business be promoted.

　　02

　　02

　　解决方案框架

　　Solution Framework

　　对于电商企业的数据需求，我们从数据到应用的框架出发，拆解得到如下三个层面：

　　For the data needs of e-commerce enterprises, we break down the framework from data to application into the following three levels:

　　数据底层

　　Data underlying layer

　　在数据底层，我们需要建立健全的数据基础架构，包括数据采集、存储和处理等方面。确保数据的准确性、完整性和及时性。整合各类数据源，包括电商平台数据、用户行为数据等，以支持的数据分析。

　　Firstly, at the data level, we need to establish a sound data infrastructure, including aspects such as data collection, storage, and processing. Ensure the accuracy, completeness, and timeliness of data. Integrate various data sources, including e-commerce platform data, user behavior data, etc., to support comprehensive data analysis.

　　指标中层

　　Mid level indicators

　　其次在指标中层，我们需要将不同平台的指标映射到统一的标准指标，确保它们具有相同的定义和计算方式。并建立相应的指标体系，选择合适的指标进行跟踪和监测，例如销售额、订单转化率、用户活跃度等。确保指标准确、可比较和可衡量。

　　Secondly, in the middle layer of indicators, we need to map indicators from different platforms to a unified standard indicator, ensuring that they have the same definition and calculation method. And establish a corresponding indicator system, select appropriate indicators for tracking and monitoring, such as sales revenue, order conversion rate, user activity, etc. Ensure that the indicators are accurate, comparable, and measurable.

　　业务

　　Top level business

　　在业务，整合和标准化后的指标数据可以在数据仪表板和报告中进行展示和分析。通过数据仪表板，可以直观地查看不同平台的指标趋势和关联性，帮助电商基于数据分析结果，深入理解业务运营状况，并制定相应的业务决策和优化策略。

　　Finally, at the top level of the business, the integrated and standardized indicator data can be displayed and analyzed in data dashboards and reports. Through the data dashboard, it is possible to intuitively view the trends and correlations of indicators on different platforms, helping e-commerce companies to gain a deeper understanding of business operations based on data analysis results, and formulate corresponding business decisions and optimization strategies.

　　我们结合以往落地客户的实践，针对客户需求，拆解出从数据源到指标体系、终到数据应用级别的产品功能框架：

　　We combine the best practices of our past clients and, based on their needs, break down the product functional framework from data sources to indicator systems, and ultimately to data application levels

　　在数据底层，通过RPA+API的方式，实现全自动的数据抓取，能够覆盖包括电商平台数据、业务系统数据、行业数据在内的全域电商数据，释放大量人工整理数据的精力，为各个场景的分析提供了高精准度、广范围和细粒度的数据支撑。

　　At the bottom of the data layer, fully automated data capture is achieved through RPA+API, which can cover all domain e-commerce data including e-commerce platform data, business system data, and industry data, freeing up a lot of manual data organization energy and providing high-precision, wide-ranging, and fine-grained data support for analysis in various scenarios.

　　对于从各平台获取的全域数据，进一步进行数据清洗和加工，对不同平台的含义相同但命名方式不同的字段进行关联整合，不同平台之间的指标差异，建立一个统一的指标体系，并构建通用的、及各个场景下的业务数据分析包，以确保数据的准确性、一致性、可用性。

　　For the global data obtained from various platforms, further data cleaning and processing are carried out, and fields with the same meaning but different naming conventions are associated and integrated across different platforms to eliminate differences in indicators between them. A unified indicator system is established, and a universal and scenario specific business data analysis package is constructed to ensure the accuracy, consistency, and availability of the data.

　　在电商企业内，不同层级的用户，视角及关注点均不相同，决策层及管理层大多分析维度由宏观明细，定位经营异常；操作层用户多关注明细数据，进行实际业务整改——所有用户都需要在特定场景下进行特定的数据分析。

　　In e-commerce enterprises, users at different levels have different perspectives and concerns. Decision makers and management mostly analyze dimensions from macro to detailed, positioning business anomalies; Operational layer users pay more attention to detailed data and make practical business improvements - all users need to conduct specific data analysis in specific scenarios.

　　针对分析场景化，在通用场景的粗粒度指标外，需要固化不同的分析场景下的指标体系，支撑特定场景下的数据分析。

　　For scenario based analysis, in addition to coarse-grained indicators for general scenarios, it is necessary to solidify indicator systems for different analysis scenarios to support data analysis in specific scenarios.

　　在业务，主要是将底层的原始数据和中层的整合指标与业务目标进行对接，从而帮助企业实现数据驱动的业务增长。E数通提供电商精细化运营全场景包，通过标准化的报告，将关键指标和绩效结果呈现给决策者和相关团队，以支持业务决策和优化。

　　At the top level of the business, it is mainly to connect the raw data at the bottom and the integrated indicators at the middle level with business goals, thereby helping enterprises achieve data-driven business growth. E-Softong provides a comprehensive package for refined e-commerce operations, presenting key indicators and performance results to decision-makers and relevant teams through standardized reports to support business decision-making and optimization.

　　另外，对于有一定分析基础的企业用户，还可以通过自助分析创新工具，为企业和组织个性化打造分析思路，在不同场景下，通过数据分析和展现，快速发现问题并推进解决。

　　In addition, for enterprise users with a certain analytical foundation, self-service analysis innovation tools can be used to create personalized analysis ideas for enterprises and organizations. Through data analysis and presentation in different scenarios, problems can be quickly identified and solved.

　　我们E数通作为电商数据分析的平台，能够提供以下能力：

　　As a platform for e-commerce data analysis, our E Data Platform can provide the following capabilities:

　　——数据汇总和整合：整合的数据源，包括电商平台数据、业务系统数据、广告数据、用户行为数据等。为用户提供全局的数据视角，以了解整个业务运营情况

　　Comprehensive - Data aggregation and integration: Integrate comprehensive data sources, including e-commerce platform data, business system data, advertising data, user behavior data, etc. Provide users with a global data perspective to understand the entire business operation situation

　　标准——数据储存和标准化处理：统一储存、整理数据，确保全维度的数据准确；标准化+定制化底层数仓模型，将多平台、多维度数据标转化整理，满足数据分析需求

　　Standards - Data Storage and Standardization Processing: Unify the storage and organization of data to ensure the accuracy of all dimensions of data; Standardization and customization of the underlying data warehouse model, transforming and organizing data from multiple platforms and dimensions to meet data analysis needs

　　直观——实时场景数据监控：提供电景包，通过可视化的方式，进行各个场景的数据洞察、监控、复盘；帮助用户理解和利用电商数据，实现精细和智能的运营管理

　　Intuitive - Real time scene data monitoring: Provides e-commerce scene packages, allowing for data insights, monitoring, and review of various scenarios through visualization; Help users understand and utilize e-commerce data to achieve refined and intelligent operational management

　　——前沿工具和功能：满足企业不断变化的数据分析需求，创新的算法和模型能力，以及智能化的数据处理和预测功能，使企业能够做出更准确和具有竞争力的决策

　　Leading - cutting-edge tools and features: meeting the constantly changing data analysis needs of enterprises, innovative algorithm and model capabilities, as well as intelligent data processing and prediction functions, enabling enterprises to make more accurate and competitive decisions

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