Quick Start a Personalized Recommendation Engine with AWS
November 13 @ 09:00:00 - 12:00:00
Time : Wed, 13 November 2019 9:00-12:00
Venue : AWS Office Singapore (23 Church st. Singapore 049481)
- Language : English
In the internet world with fierce competition, one of the keys to success of business is whether you can capture the customer stickiness by having them carrying out repeated purchase and repeated viewing of the contents.
To do so, the creation of personalized content to attract visitors to become frequent consumers would be important. Data is hence essential to help you understand your consumers tastes better and provide you insights in what kinds of personalized contents / marketing can be applied to attract and retain the consumers.
Netflix has saved $1B per year by developing a highly customized recommendation engine on the viewer’s behaviors and interests, and now 80% of subscribers choose the contents suggested by the engine. Another example like Amazon.com, 35% of what consumers purchase comes from its product recommendation system. However,many marketers are facing two main challenges:
- Insufficient customer behavior data
- Unaware of how recommendation engine is being built in this workshop
In this workshop eCloudvalley will introduce 3 machine learining algorithms which are commonly used for making recommendations under different scenarios. eCloudvalley will also give a demo on how to leverage Amazon Personalize to quickly implement a recommendation engine for article recommendations for your company.
Reason to Attend
- To learn how to do personalized engagements and make data driven decisions
- To Learn how to apply data to real-world results to improve the customer experience and deliver a highly competitive diversity.
- To learn how to quickly build a data lake that can enable data management and accelerate analytics.
|9:30-10:10||How to collect user data for Customer Journey Analysis|
|10:10-10:25||Demo with Tableau: Customer Journey Analysis|
|10:40-11:10||How to build a recommendation engine from Customer insights|
|11:10-11:40||Intro to 3 types of recommendation engine and Amazon Personalize|