解決方案架構:個人化行銷解決方案

個人化行銷對於創造客戶忠誠度及持續獲利而言是不可或缺的。觸及客戶並讓他們參與,變得比以往都難,一般優惠更是容易被錯過或忽略。目前的行銷系統無法利用資料協助解決這個問題。

使用智慧系統及分析大量資料的行銷人員,能夠傳遞具有高相關性及個人化程度的優惠給各個使用者,進而突破僵局並驅動參與。例如,零售商可依據各客戶的獨特興趣和喜好來提供優惠與內容,將產品呈現在最有可能購買的人面前。

將您的優惠個人化,您將能為每位現有或潛在客戶傳遞個人體驗,進而激發參與並提升客戶轉化率、終生價值及忠誠度。

部署到 Azure

使用下列預先建置的範本,將此架構部署到 Azure

部署到 Azure

瀏覽 GitHub

個人化行銷解決方案 尋找以個人化優惠來行銷產品的必要技術。使用巨量資料深入解析來賦予行銷個人特色,以獲得更多客戶回應。 Cosmos DB (Azure Services) Dashboard Browser Azure Stream Analytics (Near Real-Time Aggregates) Input Events Event Hub Cold Start Product Affinity Maching Learning (Product Affinity) Raw Stream Data Personalized Offer Logic

實作指南

產品 文件

事件中樞

Event Hubs ingests raw click-stream data from Functions and passes it on to Stream Analytics.

串流分析

Stream Analytics aggregates clicks in near real-time by product, offer, and user to write to Azure Cosmos DB and also archives raw click-stream data to Azure Storage.

Azure Cosmos DB

Azure Cosmos DB stores aggregated data of clicks by user, product, and offer as well as user-profile information.

儲存體

Azure Storage stores archived raw click-stream data from Stream Analytics.

Functions

Azure Functions takes in user clickstream data from website and reads existing user history from Azure Cosmos DB. These data are then run through the Machine Learning web service or used along with the cold-start data in Redis Cache to obtain product-affinity scores. Product-affinity scores are used with the personalized-offer logic to determine the most relevant offer to present to the user.

機器學習服務

Machine Learning helps you easily design, test, operationalize, and manage predictive analytics solutions in the cloud.

Redis 快取

Redis Cache stores pre-computed cold-start product affinity scores for users without history.

Power BI

Power BI Visualizes user activity data as well as offers presented by reading in data from Cosmos DB.

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