This document discusses how Rakuten utilizes growing data sources. It notes that Rakuten Ichiba transaction and item data is growing to over 160 million. It then outlines how Rakuten analyzes trends in this data to better understand customer demand, discover event-related needs, and improve services through applications like keyword suggestion and attribute extraction from item pages. The goal is to organize and further analyze this data across Rakuten services for useful insights.
Intelligent Electronic Commerce Service Based on Understanding of User BehaviorsRakuten Group, Inc.
□Author
Yu Hirate
Rakuten Institute of Technology, Rakuten, Inc.
□Description
In Rakuten, since various big data have been generated, we are using these big data for accelerate our business.
In this slide, I will introduce an approach based on the user behavior analysis such as a product search support system, search keyword analysis.
Intelligent Electronic Commerce Service Based on Understanding of User BehaviorsRakuten Group, Inc.
□Author
Yu Hirate
Rakuten Institute of Technology, Rakuten, Inc.
□Description
In Rakuten, since various big data have been generated, we are using these big data for accelerate our business.
In this slide, I will introduce an approach based on the user behavior analysis such as a product search support system, search keyword analysis.
The Website Globalization and E-Business Series includes a series of brief reports on country-specific website globalization and e-business topics. This series of reports is meant to be a primer on e-commerce as well as a collection of language, culture and website globalization facts by country.
You can check overview about this event in our Dewina Journal.
http://dewina-journal.foutap.com/may-the-native-be-with-you/
0. The future of ad tech and digital marketing in Asia:P3 - P18
1. About FreakOut Group:P19 - P29
2. What's Native Ads:P30 - P40
3. The Challenge of Mobile Advertising:P41 - P53
4. What Native can do:P54 - P66
5. Japanese Case:P67 - P73
6. Hike Indonesia:P74 - P82
FreakOut dewina Indonesia
id@foutap.com
http://id.foutap.com/
Answer to the most commonly used terminology Data Science with their areas of crucial roles in solving issues with case studies.
Likewise, let me know if anything is required. Ping me at google #bobrupakroy
State of Local: Then & Now - Greg Sterling, Local Search Association #LSS2016Rio SEO
Local Insights & Strategies
Impact of Digital on Offline Consumer Behavior
Evolution of Local Search
Search to Assisted Discovery
Directories to Marketplaces
ROI Speculation to Actual Measurement
Inferred and Expressed Intent
Ambiguity Regarding Intent
Specific Site Categories
Ranking on the SERP
Search = Directional Intent
Online Influencing Online = Reviews
Internet Reach Extended
Mobile Enables "Location Analytics"
Multiple Sources and Multiple Devices
Mobile Now the Primary Device
New Kinds of SEO (LBO/PVO)
From SEO to Data Optimization
AI + NLP + VA + UI + New Search Experiences
Screen Free Search, Connected Cars, New Realities
Conversational Commerce
AI / Bots / Assistants
#LSS2016
I social media sono ormai parte integrante del marketing mix aziendale ma qual’ė l’approccio corretto per le società di oggi e quali sono le best practice da tenere in considerazione nella pianificazione di una presenza social vincente?
Presentation material on the turnaround project for mixi, Inc., a public tech company in Japan. This material was created by Kenta Takeuchi, former GM of mixi, Inc. and current MBA student at Wharton School.
Understanding and implementing SEO User Intent - Part 1Authoritas
A lot of SEOs are talking about the importance of understanding your buyers' search intent and how this can help you build a winning SEO and content marketing strategy. In this presentation and in-depth blog post which accompanies it, Laurence O'Toole, CEO of Authoritas (a leading SEO SaaS platform), discusses how you can do this automatically and some of the pitfalls and limitations he has seen in SEO advice on this topic.
First presented at Search London in February 2020.
Big Data Explained - Case study: Website Analyticsdeep.bi
This is an example case study showing what big data can mean for a small website that generates just 5000 visits a day.
It all depends on what we want do get from our assets like website traffic. If we only measure the number of people who visited our site, then we do not need to worry about “big data”. We just have to count total visits (5000 a day, 150 000 monthly).
But by using just the simple measure we know nothing about our visitors / customers. So, it pretty useless.
On the following slides we present what a website owner can gain from advanced website analytics and why big data technologies are recommended.
3. Growing Data in Rakuten
Ichiba GMS (∝ # of transaction)
160 M
# of items # of reviews
4. …will be happening in Brazil as well
Source: eMarketer Jan 2014 “Retail Ecommerce Sales in Brazil to See Double-Digit Growth This Year”
5. Growing services with
Growing Data
Vol.01 Oct/14/2014
Yoichi Yoshimoto | Mario
Rakuten Institute of Technology, Rakuten Inc.
http://rit.rakuten.co.jp/
6. • Yoichi Yoshimoto
• Lead Coordinator
Rakuten Institute of Technology
Rakuten, Inc.
• So what’s my role?
Connecting R&D projects to business and tech teams
Currently focusing on “Data Mining” and “Natural
Language Processing” areas.
19. 【Keyword Trend】Peak Season Identification
Re-discovering peak season from time series data
Jan. 1st
Dec. 31st
“School Bags” have 2 peak seasons
Grandparents start looking for
school bags as gifts for their
grandchildren after their visit
during summer vacation.
Maybe!
23. 【Keyword Trend】Discovering event related demands
Burst keywords after Great East Japan Earthquake
We can see demands that aren’t reflected in POS data.
28. Attribute Extraction
Item pages in Rakuten are created by merchants
-> They Contain lots of unstructured text.
For better service, we need structured data.
Hard to see wine’s attributes Easy to see wine’s attributes
29. Attribute Extraction
Item pages in Rakuten are created by merchants
-> They Contain lots of unstructured text.
For better service, we need structured data.
Hard to see wine’s attributes Easy to see wine’s attributes
30. Attribute Extraction
We can extract attributes regardless of categories or
languages as long as table data is available
32. GEAP: Global Event Analysis Platform
• Collecting log data from any devices and services
with single platform.
• Big data analysis of cross services