隨著互聯(lián)網(wǎng)技術的不斷發(fā)展和普及，電商行業(yè)在過去幾十年中取得了巨大的發(fā)展和變革。從供小于求的“以商品為主”階段，到享受時代紅利的“以流量為主”階段，再到重視消費者體驗的“精細化運營”階段，電商市場正在進入以消費者為中心精細化運營時代，這要求電商企業(yè)從存量市場中挖掘潛力，從增量市場中尋找機會。

　　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.

　　電商行業(yè)的數(shù)據(jù)驅動目標是利用數(shù)據(jù)來指導和支持業(yè)務決策，以實現(xiàn)提升營銷效果、優(yōu)化運營效率、提升用戶體驗、發(fā)現(xiàn)商機和創(chuàng)新等目標。

　　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.

　　但隨著電商行業(yè)的數(shù)字化發(fā)展，電商行業(yè)的數(shù)據(jù)驅動中有三個特別明顯的問題。

　　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

　　電商行業(yè)數(shù)據(jù)驅動的現(xiàn)存問題

　　The Existing Problems of Data Driven E-commerce Industry

　　1. 電商數(shù)據(jù)獲取難

　　1. Difficulty in obtaining e-commerce data

　　電商平臺眾多，每個平臺的數(shù)據(jù)獲取接口各不相同，這導致了企業(yè)在獲取數(shù)據(jù)時面臨困難。缺乏統(tǒng)一的數(shù)據(jù)接口和集成方案，使得企業(yè)需要花費大量的時間和精力去從各個平臺手動導出數(shù)據(jù)，這不僅效率低下，還容易出現(xiàn)數(shù)據(jù)遺漏和不準確的情況。

　　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. 數(shù)據(jù)加工整合難

　　2. Difficulty in data processing and integration

　　電商數(shù)據(jù)分散在各個平臺、系統(tǒng)和部門中，沒有統(tǒng)一的儲存地方和標準化處理方式。這導致了數(shù)據(jù)加工整合的困難，需要耗費大量的時間和資源來進行數(shù)據(jù)清洗、轉換和整合，以形成可用于分析和決策的統(tǒng)一數(shù)據(jù)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. 數(shù)據(jù)業(yè)務分析難

　　3. Difficulty in data business analysis

　　電商數(shù)據(jù)分析需要與實際業(yè)務場景相結合，以賦能企業(yè)在決策和運營中發(fā)揮數(shù)據(jù)的價值。然而，許多企業(yè)在這方面還存在不足，缺乏有效的數(shù)據(jù)分析場景和工具，無法將數(shù)據(jù)轉化為實際的業(yè)務洞察和行動計劃。

　　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.

　　面對電商行業(yè)中的各種困境和挑戰(zhàn)，尋找切實可行的解決方案成為了企業(yè)前進的關鍵。只有通過合理的策略和有效的措施，才能解決問題，實現(xiàn)數(shù)據(jù)驅動電商精細化運營的目標，推動業(yè)務的持續(xù)增長和發(fā)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

　　解決方案框架

　　Solution Framework

　　對于電商企業(yè)的數(shù)據(jù)需求，我們從數(shù)據(jù)到應用的框架出發(fā)，拆解得到如下三個層面：

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

　　數(shù)據(jù)底層

　　Data underlying layer

　　在數(shù)據(jù)底層，我們需要建立健全的數(shù)據(jù)基礎架構，包括數(shù)據(jù)采集、存儲和處理等方面。確保數(shù)據(jù)的準確性、完整性和及時性。整合各類數(shù)據(jù)源，包括電商平臺數(shù)據(jù)、用戶行為數(shù)據(jù)等，以支持的數(shù)據(jù)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.

　　指標中層

　　Mid level indicators

　　其次在指標中層，我們需要將不同平臺的指標映射到統(tǒng)一的標準指標，確保它們具有相同的定義和計算方式。并建立相應的指標體系，選擇合適的指標進行跟蹤和監(jiān)測，例如銷售額、訂單轉化率、用戶活躍度等。確保指標準確、可比較和可衡量。

　　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.

　　業(yè)務

　　Top level business

　　在業(yè)務，整合和標準化后的指標數(shù)據(jù)可以在數(shù)據(jù)儀表板和報告中進行展示和分析。通過數(shù)據(jù)儀表板，可以直觀地查看不同平臺的指標趨勢和關聯(lián)性，幫助電商基于數(shù)據(jù)分析結果，深入理解業(yè)務運營狀況，并制定相應的業(yè)務決策和優(yōu)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.

　　我們結合以往落地客戶的實踐，針對客戶需求，拆解出從數(shù)據(jù)源到指標體系、終到數(shù)據(jù)應用級別的產(chǎn)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

　　在數(shù)據(jù)底層，通過RPA+API的方式，實現(xiàn)全自動的數(shù)據(jù)抓取，能夠覆蓋包括電商平臺數(shù)據(jù)、業(yè)務系統(tǒng)數(shù)據(jù)、行業(yè)數(shù)據(jù)在內(nèi)的全域電商數(shù)據(jù)，釋放大量人工整理數(shù)據(jù)的精力，為各個場景的分析提供了高精準度、廣范圍和細粒度的數(shù)據(jù)支撐。

　　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.

　　對于從各平臺獲取的全域數(shù)據(jù)，進一步進行數(shù)據(jù)清洗和加工，對不同平臺的含義相同但命名方式不同的字段進行關聯(lián)整合，不同平臺之間的指標差異，建立一個統(tǒng)一的指標體系，并構建通用的、及各個場景下的業(yè)務數(shù)據(jù)分析包，以確保數(shù)據(jù)的準確性、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.

　　在電商企業(yè)內(nèi)，不同層級的用戶，視角及關注點均不相同，決策層及管理層大多分析維度由宏觀明細，定位經(jīng)營異常；操作層用戶多關注明細數(shù)據(jù)，進行實際業(yè)務整改——所有用戶都需要在特定場景下進行特定的數(shù)據(jù)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.

　　針對分析場景化，在通用場景的粗粒度指標外，需要固化不同的分析場景下的指標體系，支撐特定場景下的數(shù)據(jù)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.

　　在業(yè)務，主要是將底層的原始數(shù)據(jù)和中層的整合指標與業(yè)務目標進行對接，從而幫助企業(yè)實現(xiàn)數(shù)據(jù)驅動的業(yè)務增長。E數(shù)通提供電商精細化運營全場景包，通過標準化的報告，將關鍵指標和績效結果呈現(xiàn)給決策者和相關團隊，以支持業(yè)務決策和優(yōu)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，對于有一定分析基礎的企業(yè)用戶，還可以通過自助分析創(chuàng)新工具，為企業(yè)和組織個性化打造分析思路，在不同場景下，通過數(shù)據(jù)分析和展現(xiàn)，快速發(fā)現(xiàn)問題并推進解決。

　　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.

　　我們E數(shù)通作為電商數(shù)據(jù)分析的平臺，能夠提供以下能力：

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

　　——數(shù)據(jù)匯總和整合：整合的數(shù)據(jù)源，包括電商平臺數(shù)據(jù)、業(yè)務系統(tǒng)數(shù)據(jù)、廣告數(shù)據(jù)、用戶行為數(shù)據(jù)等。為用戶提供全局的數(shù)據(jù)視角，以了解整個業(yè)務運營情況

　　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

　　標準——數(shù)據(jù)儲存和標準化處理：統(tǒng)一儲存、整理數(shù)據(jù)，確保全維度的數(shù)據(jù)準確；標準化+定制化底層數(shù)倉模型，將多平臺、多維度數(shù)據(jù)標轉化整理，滿足數(shù)據(jù)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

　　直觀——實時場景數(shù)據(jù)監(jiān)控：提供電景包，通過可視化的方式，進行各個場景的數(shù)據(jù)洞察、監(jiān)控、復盤；幫助用戶理解和利用電商數(shù)據(jù)，實現(xiàn)精細和智能的運營管理

　　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：滿足企業(yè)不斷變化的數(shù)據(jù)分析需求，創(chuàng)新的算法和模型能力，以及智能化的數(shù)據(jù)處理和預測功能，使企業(yè)能夠做出更準確和具有競爭力的決策

　　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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