解决方案体系结构:个性化市场营销解决方案

个性化市场营销对于建立客户忠诚度和保持盈利至关重要。如今,对客户进行宣传并吸引客户比以往更加困难,一般的优惠容易被错过或忽略。当前的市场营销系统未能利用有助于解决此问题的数据。

使用智能系统并分析大量数据的市场营销人员可向每位用户提供高度相关的个性化优惠,从竞争对手中脱颖而出,吸引客户购买。例如,零售商可根据每个客户的独特兴趣和偏好提供优惠和内容,将产品展示在最有可能购买的消费者面前。

通过提供个性化的优惠,可向每位现有客户或潜在客户提供个性化体验,从而提高客户参与度、客户转换、客户终身价值和保留期。

部署到 Azure

使用下列预建模板将此体系结构部署到 Azure

部署到 Azure

在 GitHub 上浏览

个性化市场营销解决方案 发现通过个性化优惠推广产品的必备技术。使用大数据见解制定个性化市场营销方案,增强客户响应。 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

实施指南

产品 文档

事件中心

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

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.

存储

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

函数

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.

机器学习

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

Redis 缓存

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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