隨著互聯(lián)網(wǎng)技術(shù)的不斷發(fā)展和普及，電商行業(yè)在過(guò)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(shí)代紅利的“以流量為主”階段，再到重視消費(fèi)者體驗(yàn)的“精細(xì)化運(yùn)營(yíng)”階段，電商市場(chǎng)正在進(jìn)入以消費(fèi)者為中心精細(xì)化運(yùn)營(yíng)時(shí)代，這要求電商企業(yè)從存量市場(chǎng)中挖掘潛力，從增量市場(chǎng)中尋找機(jī)會(huì)。

　　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(qū)動(dòng)目標(biāo)是利用數(shù)據(jù)來(lái)指導(dǎo)和支持業(yè)務(wù)決策，以實(shí)現(xiàn)提升營(yíng)銷(xiāo)效果、優(yōu)化運(yùn)營(yíng)效率、提升用戶(hù)體驗(yàn)、發(fā)現(xiàn)商機(jī)和創(chuàng)新等目標(biāo)。

　　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(qū)動(dòng)中有三個(gè)特別明顯的問(wèn)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(qū)動(dòng)的現(xiàn)存問(wèn)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(tái)眾多，每個(gè)平臺(tái)的數(shù)據(jù)獲取接口各不相同，這導(dǎo)致了企業(yè)在獲取數(shù)據(jù)時(shí)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(fèi)大量的時(shí)間和精力去從各個(gè)平臺(tái)手動(dòng)導(dǎo)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(zhǔn)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(gè)平臺(tái)、系統(tǒng)和部門(mén)中，沒(méi)有統(tǒng)一的儲(chǔ)存地方和標(biāo)準(zhǔn)化處理方式。這導(dǎo)致了數(shù)據(jù)加工整合的困難，需要耗費(fèi)大量的時(shí)間和資源來(lái)進(jìn)行數(shù)據(jù)清洗、轉(zhuǎn)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(wù)分析難

　　3. Difficulty in data business analysis

　　電商數(shù)據(jù)分析需要與實(shí)際業(yè)務(wù)場(chǎng)景相結(jié)合，以賦能企業(yè)在決策和運(yùn)營(yíng)中發(fā)揮數(shù)據(jù)的價(jià)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(chǎng)景和工具，無(wú)法將數(shù)據(jù)轉(zhuǎn)化為實(shí)際的業(yè)務(wù)洞察和行動(dòng)計(jì)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(duì)電商行業(yè)中的各種困境和挑戰(zhàn)，尋找切實(shí)可行的解決方案成為了企業(yè)前進(jìn)的關(guān)鍵。只有通過(guò)合理的策略和有效的措施，才能解決問(wèn)題，實(shí)現(xiàn)數(shù)據(jù)驅(qū)動(dòng)電商精細(xì)化運(yùn)營(yíng)的目標(biāo)，推動(dòng)業(yè)務(wù)的持續(xù)增長(zhǎng)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(duì)于電商企業(yè)的數(shù)據(jù)需求，我們從數(shù)據(jù)到應(yīng)用的框架出發(fā)，拆解得到如下三個(gè)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(chǔ)架構(gòu)，包括數(shù)據(jù)采集、存儲(chǔ)和處理等方面。確保數(shù)據(jù)的準(zhǔn)確性、完整性和及時(shí)性。整合各類(lèi)數(shù)據(jù)源，包括電商平臺(tái)數(shù)據(jù)、用戶(hù)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(biāo)中層

　　Mid level indicators

　　其次在指標(biāo)中層，我們需要將不同平臺(tái)的指標(biāo)映射到統(tǒng)一的標(biāo)準(zhǔn)指標(biāo)，確保它們具有相同的定義和計(jì)算方式。并建立相應(yīng)的指標(biāo)體系，選擇合適的指標(biāo)進(jìn)行跟蹤和監(jiān)測(cè)，例如銷(xiāo)售額、訂單轉(zhuǎn)化率、用戶(hù)活躍度等。確保指標(biāo)準(zhǔn)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(wù)

　　Top level business

　　在業(yè)務(wù)，整合和標(biāo)準(zhǔn)化后的指標(biāo)數(shù)據(jù)可以在數(shù)據(jù)儀表板和報(bào)告中進(jìn)行展示和分析。通過(guò)數(shù)據(jù)儀表板，可以直觀地查看不同平臺(tái)的指標(biāo)趨勢(shì)和關(guān)聯(lián)性，幫助電商基于數(shù)據(jù)分析結(jié)果，深入理解業(yè)務(wù)運(yùn)營(yíng)狀況，并制定相應(yīng)的業(yè)務(wù)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(jié)合以往落地客戶(hù)的實(shí)踐，針對(duì)客戶(hù)需求，拆解出從數(shù)據(jù)源到指標(biāo)體系、終到數(shù)據(jù)應(yīng)用級(jí)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(guò)RPA+API的方式，實(shí)現(xiàn)全自動(dòng)的數(shù)據(jù)抓取，能夠覆蓋包括電商平臺(tái)數(shù)據(jù)、業(yè)務(wù)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(gè)場(chǎng)景的分析提供了高精準(zhǔn)度、廣范圍和細(xì)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(duì)于從各平臺(tái)獲取的全域數(shù)據(jù)，進(jìn)一步進(jìn)行數(shù)據(jù)清洗和加工，對(duì)不同平臺(tái)的含義相同但命名方式不同的字段進(jìn)行關(guān)聯(lián)整合，不同平臺(tái)之間的指標(biāo)差異，建立一個(gè)統(tǒng)一的指標(biāo)體系，并構(gòu)建通用的、及各個(gè)場(chǎng)景下的業(yè)務(wù)數(shù)據(jù)分析包，以確保數(shù)據(jù)的準(zhǔn)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(jí)的用戶(hù)，視角及關(guān)注點(diǎn)均不相同，決策層及管理層大多分析維度由宏觀明細(xì)，定位經(jīng)營(yíng)異常；操作層用戶(hù)多關(guān)注明細(xì)數(shù)據(jù)，進(jìn)行實(shí)際業(yè)務(wù)整改——所有用戶(hù)都需要在特定場(chǎng)景下進(jìn)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(duì)分析場(chǎng)景化，在通用場(chǎng)景的粗粒度指標(biāo)外，需要固化不同的分析場(chǎng)景下的指標(biāo)體系，支撐特定場(chǎng)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(wù)，主要是將底層的原始數(shù)據(jù)和中層的整合指標(biāo)與業(yè)務(wù)目標(biāo)進(jìn)行對(duì)接，從而幫助企業(yè)實(shí)現(xiàn)數(shù)據(jù)驅(qū)動(dòng)的業(yè)務(wù)增長(zhǎng)。E數(shù)通提供電商精細(xì)化運(yùn)營(yíng)全場(chǎng)景包，通過(guò)標(biāo)準(zhǔn)化的報(bào)告，將關(guān)鍵指標(biāo)和績(jī)效結(jié)果呈現(xiàn)給決策者和相關(guān)團(tuán)隊(duì)，以支持業(yè)務(wù)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(duì)于有一定分析基礎(chǔ)的企業(yè)用戶(hù)，還可以通過(guò)自助分析創(chuàng)新工具，為企業(yè)和組織個(gè)性化打造分析思路，在不同場(chǎng)景下，通過(guò)數(shù)據(jù)分析和展現(xiàn)，快速發(fā)現(xiàn)問(wèn)題并推進(jìn)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(tái)，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(tái)數(shù)據(jù)、業(yè)務(wù)系統(tǒng)數(shù)據(jù)、廣告數(shù)據(jù)、用戶(hù)行為數(shù)據(jù)等。為用戶(hù)提供全局的數(shù)據(jù)視角，以了解整個(gè)業(yè)務(wù)運(yùn)營(yíng)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(biāo)準(zhǔn)——數(shù)據(jù)儲(chǔ)存和標(biāo)準(zhǔn)化處理：統(tǒng)一儲(chǔ)存、整理數(shù)據(jù)，確保全維度的數(shù)據(jù)準(zhǔn)確；標(biāo)準(zhǔn)化+定制化底層數(shù)倉(cāng)模型，將多平臺(tái)、多維度數(shù)據(jù)標(biāo)轉(zhuǎn)化整理，滿(mǎn)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(shí)時(shí)場(chǎng)景數(shù)據(jù)監(jiān)控：提供電景包，通過(guò)可視化的方式，進(jìn)行各個(gè)場(chǎng)景的數(shù)據(jù)洞察、監(jiān)控、復(fù)盤(pán)；幫助用戶(hù)理解和利用電商數(shù)據(jù)，實(shí)現(xiàn)精細(xì)和智能的運(yùn)營(yíng)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(mǎn)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(yù)測(cè)功能，使企業(yè)能夠做出更準(zhǔn)確和具有競(jìng)爭(zhēng)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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