Generate real-time personalized offers on a retail website to engage more closely with customers.
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Daniel Ciborowski a99c419da2 Update README.md 2017-09-11 13:35:47 -04:00
Solution Overview for Business Audiences copying materials over 2016-11-04 14:33:26 -04:00
Technical Deployment Guide Update README.md 2017-09-11 13:35:47 -04:00
README.md copying materials over 2016-11-04 14:33:26 -04:00

README.md

Personalized Offers in Online Retail – A Cortana Intelligence Solution

In todays highly competitive and connected environment, modern businesses can no longer survive with generic, static online content. Furthermore, marketing strategies using traditional tools are often expensive, hard to implement and do not produce the desired return on investment. These systems often fail to take full advantage of the data collected to create a more personalized experience for the user.

Creating personal and custom-tailored websites has become essential to build customer loyalty and remain profitable. On a retail website, customers desire intelligent systems which provide offers and content based on their unique interests and preferences.

Todays digital marketing teams can build this intelligence using the data generated from all types of user interactions. By analyzing massive amounts of data, marketers have the unique opportunity to deliver highly relevant and personalized offers to each user. However, building a reliable and scalable big data infrastructure, and developing sophisticated machine learning models that personalize to each user is not trivial.

The Cortana Analytics Personalized Offers for Online Retail Solution Package contains the resources youll need to deploy a functional, end-to-end pipeline for generating personalized offers on a retail website. Using this guide, users can understand the technical steps required to develop their own solution. This package utilizes a simulated data generator to imitate an online retail website with customer traffic.

Leveraging data to create a personalized, custom tailored user experience

On a typical retail website, the webpage will log event data to track how users browse their site and products. With todays capabilities for massive scale data ingestion, storage, processing and advanced analytics in the Cortana Intelligence suite, companies can quickly and easily implement new scenarios to better target their customers.

Personalized offers on a retail website offers a competitive edge by presenting the user with personalized internal offers based off their recent browser history on the site. By surfacing offers relevant to each user, companies are able to customer enjoyment while also increasing revenue through high conversion rates and sales.

Whats under the hood

The personalized offers in online retail solution package utilizes key technologies within the Azure Cortana Intelligence Suite to deliver the full, operational pipeline within a few hours. The architecture we use here takes in events generated by the retail website (specifically, that a given logged-in user was viewing a particular product), the correlates and aggregates these eventas to generate recommendations using Azure Machine Learning.

Every time a user loads a product page on the retail website, the website sends an event to Azure Event Hub which is then consumed by Azure Stream Analytics. Event Hub can scale up to process millions of events per second, allowing support for enterprises of any size.

Stream Analytics pushes the raw data to Azure Blob Storage for later processing, and aggregates recent events to write totals to Azure Table Storage. These numbers are used as input to Azure Machine Learning to generate recommendations to users. By using Azure Stream Analytics to update these numbers in real time, we ensure that users get the most up to date recommendations possible.

Azure Data Factory runs hourly to copy the raw data into an Azure SQL Data Warehouse for later analysis, [and also runs a Hive script using HDInsight On Demand to process the data into a format that can be used to retrain the Azure Machine Learning model, updating the recommendations based on the latest user activity. Using Azure Data Factory and HDInsight On Demand allows Azure to bring up computing resources as needed and shut them down when processing is finished, meaning youre only charged for the computation you need, rather than having to manually allocate and de-allocate resources].

Finally, Power BI reads in data from both Azure Stream Analytics and Azure SQL Data Warehouse to a rich dashboard of visualizations for managers to monitor current and historical performance of the campaign.

Getting Started

This solution template contains materials to help both technical and business audiences understand our personalized offers solution for the retail industry built on the Cortana Intelligence Suite.

Business Audiences

In this repository you will find a folder labelled Solution Overview for Business Audiences. This folder contains:

  • Infographic: Covers the benefits of using advanced analytics for personalized offers in online retail
  • Solution At-a-glance: An introduction to a Cortana Intelligence Suite solution for personalized offers
  • Walking Deck: In-depth exploration of the solution for business audiences

For more information on how to tailor Cortana Intelligence to your needs connect with one of our partners.

Technical Audiences

See the Technical Deployment Guide folder for a full set of instructions on how to deploy the end-to-end pipeline, including a step by step walkthrough, files containing all the scripts and other data youll need to deploy resources, and a data generator to simulate website activity. For technical problems or questions about deploying this solution, please post in the issues tab of the repository.