Selection of Articles Using Data Analytics for Behavioral Dissertation Resear...PhD Assistance
Outcomes in health-related issues including psychological, educational, Behavioral, environmental, and social are intended to sustain positive change by digital interferences. These changes may be delivered using any digital device like a phone or computer, and make them gainful for the provider. Complex and large-scale datasets that contain usage data can be yielded by testing a digital intervention. This data provides invaluable detail about how the users interact with these interventions and notify their knowledge of engagement, if they are analyzed properly. This paper recommends an innovative framework for the process of analyzing usage associated with a digital intervention .
PhD Assistance is an Academic The Best Dissertation Writing Service & Consulting Support Company established in 2001. specialiWeze in providing PhD Assignments, PhD Dissertation Writing Help , Statistical Analyses, and Programming Services to students in the USA, UK, Canada, UAE, Australia, New Zealand, Singapore and many more.
Website Visit: https://bit.ly/3dANXUD
Contact Us:
UK NO: +44-1143520021
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GPS for Chemical Space - Digital Assistants to Support Molecule Design - Chem...ChemAxon
Boehringer Ingelheim's Nils Weskamp discusses eDesign: a computational platform for molecule design and optimization. This presentation explaing how to combine data, algorithms and user experience to impact compound design, and gives a glimpse into the agile and interdisciplinary teamwork as facilitated by Design Hub as a success factor for the development of digital tools.
Selection of Articles Using Data Analytics for Behavioral Dissertation Resear...PhD Assistance
Outcomes in health-related issues including psychological, educational, Behavioral, environmental, and social are intended to sustain positive change by digital interferences. These changes may be delivered using any digital device like a phone or computer, and make them gainful for the provider. Complex and large-scale datasets that contain usage data can be yielded by testing a digital intervention. This data provides invaluable detail about how the users interact with these interventions and notify their knowledge of engagement, if they are analyzed properly. This paper recommends an innovative framework for the process of analyzing usage associated with a digital intervention .
PhD Assistance is an Academic The Best Dissertation Writing Service & Consulting Support Company established in 2001. specialiWeze in providing PhD Assignments, PhD Dissertation Writing Help , Statistical Analyses, and Programming Services to students in the USA, UK, Canada, UAE, Australia, New Zealand, Singapore and many more.
Website Visit: https://bit.ly/3dANXUD
Contact Us:
UK NO: +44-1143520021
India No: +91-8754446690
Email: info@phdassistance.com
GPS for Chemical Space - Digital Assistants to Support Molecule Design - Chem...ChemAxon
Boehringer Ingelheim's Nils Weskamp discusses eDesign: a computational platform for molecule design and optimization. This presentation explaing how to combine data, algorithms and user experience to impact compound design, and gives a glimpse into the agile and interdisciplinary teamwork as facilitated by Design Hub as a success factor for the development of digital tools.
[Research] deploying predictive models with the actor framework - Brian GawaltPAPIs.io
Build a better, faster, more efficient predictive API with the Actor model of programming. Latency, logging, full utilization are all easily handled with this framework. Upwork (formerly Elance-oDesk) freelancer availability model — anticipating who's looking for work right now — is now a real-time service, without costly or complicated build-out of our stack or our datacenter, thanks to the Actor model.
Big wins with small data. PredictionIO in ecommerce - David JonesPAPIs.io
There’s a lot of noise about big data and cutting edge algorithms optimisations. Returning to the basics, this presentation shows you might not need as much data as you think to get real world benefits. Learn about machine learning in ecommerce, PredictionIO and how we used off the shelf, well implemented algorithms to get a 71% increase in revenue with an online wine retailer.
통계분석연구회 2016년 겨울맞이 추천 도서와 영상
2016년 겨울 방학 및 휴가철을 맞이하여서 추천 도서와 영상 자료를 공유합니다. 올해에는 다양한 분야에서 분석 관련 도서가 출간이 되었네요. 다양한 분석 관련 서적의 선택을 고민하시는 분들에게 좋은 지침이 되리라 생각이 됩니다.
기억에 남는 통계 관련 추천 도서 또는 통계 관련 영상에 대한 많은 공유 부탁 드립니다. 아래 게시판에 통계 관련 추천 도서 또는 동영상에 대한 정보를 공유 또는 추천을 해주시면 검토 후에 다음 자료 업데이트 시에 반영하도록 하겠습니다.
(통계분석 연구회 통계도서게시판 / 통계영상자료실)
통계 관련 주제로 글을 연재로 올려주시면 글을 모아서 회원들과 공유하는 장을 만들도록 하겠습니다. 관심 있는 분들의 많은 참여 부탁 드립니다.
( 2016년 추가 : 갈색 표시, 2016년 하반기 추가 : 파란색 )
* 통계분석연구회 ( http://cafe.daum.net/statsas )
* 통계분석연구회 페이스북 (https://www.facebook.com/groups/statsas)
꿈꾸는 데이터 디자이너 시즌2 교육설명회 슬라이드 입니다. 시즌2에 대한 정보와 시즌1에서의 결과에 대한 설명입니다.
www.facebook.com/datadesigner2015
https://www.facebook.com/groups/datadesigner/
www.datadesigner.org
2015년 여름맞이 추천 도서를 건너뛰고 겨울맞이 추천 도서와 영상 자료를 공유합니다. 데이터 사이언티스트 관련 도서가 많이 번역되어 발간되었으며, 좋은 R-Project 관련 도서가 많이 출판되었습니다. 통계와 첫 만남을 준비하는 통계인을 위한 추천 도서(고려대 입학처 제공)를 추가하였습니다. 통계학과 입학을 준비하시는 분이나 통계에 관심 있는 분들에게 통계에 대하여 쉽게 다가서도록 도와주는 책들입니다.
이번 자료에는 통계분석연구회의 SASMaster님께서 “통계진로정보” 게시판에 올려주신 주옥 같은 글을 정리하여 추가하였습니다. 진로에 고민하시는 분들에게 많은 도움이 되리라 생각됩니다.
1. 통계추천 도서
2. 통계와 첫 만남을 준비하는 통계인을 위한 추천 도서(고려대 입학처)
3. SASMaster님의 통계진로
4. 데이터사이언티스트를 찾아서 (컴퓨터월드 기획기사)
5. 통계추천 영상
6. 통계분석연구회 게시판 모음
7. 최근 인기 글
기억에 남는 통계 관련 추천 도서 또는 통계 관련 영상에 대한 많은 공유 부탁 드립니다. 아래 게시판에 통계 관련 추천 도서 또는 동영상에 대한 정보를 공유 또는 추천을 해주시면 검토 후에 다음 자료 업데이트 시에 반영하도록 하겠습니다.
(통계분석 연구회 통계도서게시판 / 통계영상자료실)
통계 관련 주제로 글을 연재로 올려주시면 글을 모아서 회원들과 공유하는 장을 만들도록 하겠습니다. 관심 있는 분들의 많은 참여 부탁 드립니다.
* 통계분석연구회 ( http://cafe.daum.net/statsas )
* 통계분석연구회 페이스북 (https://www.facebook.com/groups/statsas)
R은 데이터 분석 분야에서 널리 사용되고 있는 무료 도구입니다. 뛰어난 기능과 확장성 등으로 인해 다양한 분야에서 널리 활용되고 있지만 대용량 데이터를 직접 다루는 데 한계가 있다는 약점이 있었습니다. 스파크는 클러스터 환경에서 동작하는 대용량 분산 데이터 처리 시스템입니다. 뛰어난 성능과 더불어 다양하고 유용한 데이터 처리 함수를 제공하며 R, 하둡, Hive 등 기존 데이터 분석 도구등과 연동하여 사용할 수 있는 다양한 기능을 제공합니다.
이 문서에서는 R과 스파크를 연동하는 방법과 함께 R 스크립트에서 R과 스파크 함수를 함께 사용하는 방법을 소개합니다. 또한 웹 브라우저 기반의 작업 환경을 제공하는 제플린과의 연동을 통해 다수의 사용자가 시간과 공간의 제약 없이 자유롭게 서버에 접속하여 데이터를 분석하고 그 결과를 공유할 수 있는 방법에 대해서도 소개합니다. 스파크와 R, 제플린을 적절히 조합하여 사용한다면 다른 유료 분석 툴 부럽지 않은 분석 환경을 구축할 수 있을 것입니다.
[우리가 데이터를 쓰는 법] 좋다는 건 알겠는데 좀 써보고 싶소. 데이터! - 넘버웍스 하용호 대표Dylan Ko
Gonnector(고넥터) 고영혁 대표가 주최한 스타트업 데이터 활용 세미나 '우리가 데이터를 쓰는 법' 의 첫 번째 발표 자료
세미나 : 우리가 데이터를 쓰는 법 (How We Use Data)
일시 : 2016년 4월 12일 화요일 10:00 ~ 18:00
장소 : 마루180 (Maru180) B1 Think 홀
제목 : 좋다는 건 알겠는데 좀 써보고 싶소. 데이터!
연사 : 넘버웍스 하용호 대표
한국 표준(?) 자바셋(Java 1.6+Spring 3.x+MyBatis)과 Monolithic 아키텍처를 사용하고 있었던 제 조직 내에서 기술적 변화를 이끌어가는 것에 관련된 내용입니다.
변화를 유도하기 위해서 어떻게 해야 하는지가 핵심이며,
Architecture, Frontend, Backend, 방법론/프로세스의 영역을 각각의 단계로 나누어서 Phase1을 수행한 것과 Phase2를 수행 중인 내용에 대해서도 다룹니다.
Phase1
- Architecture : Frontend / Backend 명시적 분리
- Frontend : Angular.js, Grunt, Bower 도입
- Backend : Java 1.7/Spring4, ORM 도입
- 방법론/프로세스 : Scrum, Git
Phase2
- Architecture : Micro-Service Architecture(MSA)
- Frontend : Content Router, E2E Test
- Backend : Polyglot, Multi-Framework
- 방법론/프로세스 : Scrum+JIRA, Git Branch Policy, Pair Programming, Code Workshop
[Research] deploying predictive models with the actor framework - Brian GawaltPAPIs.io
Build a better, faster, more efficient predictive API with the Actor model of programming. Latency, logging, full utilization are all easily handled with this framework. Upwork (formerly Elance-oDesk) freelancer availability model — anticipating who's looking for work right now — is now a real-time service, without costly or complicated build-out of our stack or our datacenter, thanks to the Actor model.
Big wins with small data. PredictionIO in ecommerce - David JonesPAPIs.io
There’s a lot of noise about big data and cutting edge algorithms optimisations. Returning to the basics, this presentation shows you might not need as much data as you think to get real world benefits. Learn about machine learning in ecommerce, PredictionIO and how we used off the shelf, well implemented algorithms to get a 71% increase in revenue with an online wine retailer.
통계분석연구회 2016년 겨울맞이 추천 도서와 영상
2016년 겨울 방학 및 휴가철을 맞이하여서 추천 도서와 영상 자료를 공유합니다. 올해에는 다양한 분야에서 분석 관련 도서가 출간이 되었네요. 다양한 분석 관련 서적의 선택을 고민하시는 분들에게 좋은 지침이 되리라 생각이 됩니다.
기억에 남는 통계 관련 추천 도서 또는 통계 관련 영상에 대한 많은 공유 부탁 드립니다. 아래 게시판에 통계 관련 추천 도서 또는 동영상에 대한 정보를 공유 또는 추천을 해주시면 검토 후에 다음 자료 업데이트 시에 반영하도록 하겠습니다.
(통계분석 연구회 통계도서게시판 / 통계영상자료실)
통계 관련 주제로 글을 연재로 올려주시면 글을 모아서 회원들과 공유하는 장을 만들도록 하겠습니다. 관심 있는 분들의 많은 참여 부탁 드립니다.
( 2016년 추가 : 갈색 표시, 2016년 하반기 추가 : 파란색 )
* 통계분석연구회 ( http://cafe.daum.net/statsas )
* 통계분석연구회 페이스북 (https://www.facebook.com/groups/statsas)
꿈꾸는 데이터 디자이너 시즌2 교육설명회 슬라이드 입니다. 시즌2에 대한 정보와 시즌1에서의 결과에 대한 설명입니다.
www.facebook.com/datadesigner2015
https://www.facebook.com/groups/datadesigner/
www.datadesigner.org
2015년 여름맞이 추천 도서를 건너뛰고 겨울맞이 추천 도서와 영상 자료를 공유합니다. 데이터 사이언티스트 관련 도서가 많이 번역되어 발간되었으며, 좋은 R-Project 관련 도서가 많이 출판되었습니다. 통계와 첫 만남을 준비하는 통계인을 위한 추천 도서(고려대 입학처 제공)를 추가하였습니다. 통계학과 입학을 준비하시는 분이나 통계에 관심 있는 분들에게 통계에 대하여 쉽게 다가서도록 도와주는 책들입니다.
이번 자료에는 통계분석연구회의 SASMaster님께서 “통계진로정보” 게시판에 올려주신 주옥 같은 글을 정리하여 추가하였습니다. 진로에 고민하시는 분들에게 많은 도움이 되리라 생각됩니다.
1. 통계추천 도서
2. 통계와 첫 만남을 준비하는 통계인을 위한 추천 도서(고려대 입학처)
3. SASMaster님의 통계진로
4. 데이터사이언티스트를 찾아서 (컴퓨터월드 기획기사)
5. 통계추천 영상
6. 통계분석연구회 게시판 모음
7. 최근 인기 글
기억에 남는 통계 관련 추천 도서 또는 통계 관련 영상에 대한 많은 공유 부탁 드립니다. 아래 게시판에 통계 관련 추천 도서 또는 동영상에 대한 정보를 공유 또는 추천을 해주시면 검토 후에 다음 자료 업데이트 시에 반영하도록 하겠습니다.
(통계분석 연구회 통계도서게시판 / 통계영상자료실)
통계 관련 주제로 글을 연재로 올려주시면 글을 모아서 회원들과 공유하는 장을 만들도록 하겠습니다. 관심 있는 분들의 많은 참여 부탁 드립니다.
* 통계분석연구회 ( http://cafe.daum.net/statsas )
* 통계분석연구회 페이스북 (https://www.facebook.com/groups/statsas)
R은 데이터 분석 분야에서 널리 사용되고 있는 무료 도구입니다. 뛰어난 기능과 확장성 등으로 인해 다양한 분야에서 널리 활용되고 있지만 대용량 데이터를 직접 다루는 데 한계가 있다는 약점이 있었습니다. 스파크는 클러스터 환경에서 동작하는 대용량 분산 데이터 처리 시스템입니다. 뛰어난 성능과 더불어 다양하고 유용한 데이터 처리 함수를 제공하며 R, 하둡, Hive 등 기존 데이터 분석 도구등과 연동하여 사용할 수 있는 다양한 기능을 제공합니다.
이 문서에서는 R과 스파크를 연동하는 방법과 함께 R 스크립트에서 R과 스파크 함수를 함께 사용하는 방법을 소개합니다. 또한 웹 브라우저 기반의 작업 환경을 제공하는 제플린과의 연동을 통해 다수의 사용자가 시간과 공간의 제약 없이 자유롭게 서버에 접속하여 데이터를 분석하고 그 결과를 공유할 수 있는 방법에 대해서도 소개합니다. 스파크와 R, 제플린을 적절히 조합하여 사용한다면 다른 유료 분석 툴 부럽지 않은 분석 환경을 구축할 수 있을 것입니다.
[우리가 데이터를 쓰는 법] 좋다는 건 알겠는데 좀 써보고 싶소. 데이터! - 넘버웍스 하용호 대표Dylan Ko
Gonnector(고넥터) 고영혁 대표가 주최한 스타트업 데이터 활용 세미나 '우리가 데이터를 쓰는 법' 의 첫 번째 발표 자료
세미나 : 우리가 데이터를 쓰는 법 (How We Use Data)
일시 : 2016년 4월 12일 화요일 10:00 ~ 18:00
장소 : 마루180 (Maru180) B1 Think 홀
제목 : 좋다는 건 알겠는데 좀 써보고 싶소. 데이터!
연사 : 넘버웍스 하용호 대표
한국 표준(?) 자바셋(Java 1.6+Spring 3.x+MyBatis)과 Monolithic 아키텍처를 사용하고 있었던 제 조직 내에서 기술적 변화를 이끌어가는 것에 관련된 내용입니다.
변화를 유도하기 위해서 어떻게 해야 하는지가 핵심이며,
Architecture, Frontend, Backend, 방법론/프로세스의 영역을 각각의 단계로 나누어서 Phase1을 수행한 것과 Phase2를 수행 중인 내용에 대해서도 다룹니다.
Phase1
- Architecture : Frontend / Backend 명시적 분리
- Frontend : Angular.js, Grunt, Bower 도입
- Backend : Java 1.7/Spring4, ORM 도입
- 방법론/프로세스 : Scrum, Git
Phase2
- Architecture : Micro-Service Architecture(MSA)
- Frontend : Content Router, E2E Test
- Backend : Polyglot, Multi-Framework
- 방법론/프로세스 : Scrum+JIRA, Git Branch Policy, Pair Programming, Code Workshop
Big data and macroeconomic nowcasting from data access to modellingDario Buono
Parallel advances in IT and in the social use of Internet-related applications, provide the general public with access to a vast amount of information. The associated Big Data are potentially very useful for a variety of applications, ranging from marketing to tapering fiscal evasion.
From the point of view of official statistics, the main question is whether and to what extent Big Data are a field worth investing to expand, check and improve the data production process and which types of partnerships will have to be formed for this purpose. Nowcasting of macroeconomic indicators represents a well-identified field where Big Data has the potential to play a decisive role in the future.
In this paper we present the results and main recommendations from the Eurostat-funded project “Big Data and macroeconomic nowcasting”, implemented by GOPA Consultants, which benefits from the cooperation and work of the Eurostat task force on Big Data and a few external academic experts.
Learn how to use Hootsuite, HubSpot Google Ads and Google Analytics in Teaching Digital Marketing. Understand how to integrate Google Analytics with Blogger for Engaged Learning.
Producing direct value for businesses via quantitative models.
New analytical tools such as Looker allow data analysts to speed up the dirty work around building data models—making it less painful to clean data, explore predictive factors, and evaluate results.
In this educational webinar from Data Science Central (DSC), Justin Palmer of LendingHome, a mortgage banking and marketing platform, joins Colin Zima, Chief Analytics Officer at Looker. Using a public-domain FAA dataset and the LendingHome platform as examples, they dig into the data modeling process and offer ideas for improvements.
- See more at: http://try.looker.com/resources/improving-data-modeling-workflow#sthash.2rGxwhJ7.dpuf
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
6. Step 2:
Targeting
Customer segmentation and
selection with goals at step 1
Based on demographic
information and log collections
Statistical methods and data
mining algorithms
7. Step 3:
Offering Benefit
(Campaign)
• Delivering proper benefits to
targeted customer groups
• Methods: Promotion, Event,
Advertisement and others
• Measurement and prediction
of campaign effects
10. Multivariate Testing
• Technique for testing a
hypothesis with multiple
variables
• Issues for offering
• Lack of long-term
prediction
• data, benefit limitations
(Image from https://www.ownedit.com/features)
11. Bayesian Interpretation
• Diachronic Interpretation
• Probability of the
hypotheses changes over
time
• Prior and posterior based
on background
information
• Good for simulation,
decision and prediction
(Image from http://en.wikipedia.org/wiki/Bayes%27_theorem)
12.
13. CausalImpact
• Based on the paper [Inferring causal impact using Bayesian
structural time-series models], Google, 2014
• CausalImpact Package in R
• https://github.com/google/CausalImpact
(Image from the paper)
14. CompareImpacts
• Integration of bayesian
time series prediction
model with multivariate
tests
• For simple comparison
of causal effects
19. • Offerings in a group with time differences
Use Case Results
20. Office Hour: 15:25~16:05, Table A
E-mail : cojette@gmail.com
New Tool for Offering Comparison:
Multivariate Test +
Bayesian Time-Series Analysis
Editor's Notes
Hello, everyone.
I'm happy to give a presentation at Strata conference.
Today I’d like to talk about "Measuring benefit effect for customers with bayesian prediction modeling" .
The main keyword of this presentation is ‘Offering’.
I don’t know whether you have ever heard about “ Offering” or not, // but I’m sure you have experienced it at least once.
Nowadays there have been lots of 'offering benefit' events // in the mobile, online, and offline world.
You may have apps in your smartphone // that give you free or discounted coupons.
You could get personalized discount offers by e-mail, //and sometimes you may receive some paper coupons on the street.
Technologies are being developed, // and the variety of offering is being increased,
but the essence of offering is the same // - increasing whole customer value.
Therefore, all these examples are in the range of offering.
Of course, some of you may feel that offering is a kind of spam.
Sometimes you may be annoyed by these offering events.
But offering benefits is a well-known way // to inform customers about your products, // to differentiate your products from competitors // and to create value by meeting customers’ wider needs and deeper impressions //more than the competitors.
In short, offering benefits has been a very classical but important // and widely-used strategy //for acquisition of new customers and churn management.
The Offering Process is like this.
Let’s suppose these colorful objects are customers, // and this gray square is a group,// for example, this is a whole customer group.
Our strategy consists of three steps: // gathering of customer data , targeting relevant customers, and offering benefits.
First step is the customer data management.
In this step, we do not interact directly with the customers.
We just monitor customers' actions and observe their needs, // and define goals //that we wish to achieve by interacting with these customers.
We may keep track of customer data and information, // and set the dimensions and views for them.
Second, the targeting step.
In this step, we segment customers into groups // based on the purposes that we already set //in the previous step.
Customer segmentation can be based on // demographic informations, their action log, payment log and other data.
There are lots of methods for doing this // , mainly various statistical methods and algorithms // related to clustering and classification.
Many of you must be familiar with algorithms // such as K-means, SVM, EM and others.
These all algorithms can be used in the targeting step.
Moreover, on the business side, // there have been lots of strategy improvements // through decades of research on //Customer Relationship Management.
The customer segmentation has been the foundation of CRM for a long time.
Final step is actually offering benefits, //also known as campaign.
In this step,we try to deliver proper benefits to targeted customers // using promotions, events, advertisements and other methods.
This is the most important and difficult step, // because 'offering' is completed // when the relevant targeted customers get the appropriate benefits // and they are satisfied.
However, it is difficult to quantify and forecast // how much of the targeted customers will be satisfied // with the benefits they get.
In this process, especially during the final step, // there is a huge problem of decision making.
The most important and difficult question is this:// How do you measure and predict effects of benefits?
Many decision makers suffer for various reasons, // such as the lack of effective methods // for measuring and predicting the effects of benefits, // insufficient customer data, the wide range of available benefits, // temporary availability of benefits due to business concerns and, //most importantly, the difficulty of predicting the customer reactions.
Consequently, many just give up making decisions based on the data.
Instead, they claim that they can only rely on their business instincts.
Post hoc comparison of effects has even bigger problems.
It is rare to host only a single campaign.
Most companies would make various offerings, //refining their campaigns through trial and error //as they go along.
For example, //we can refine groupings by dividing a group to smaller groups // and treat each subgroup with different offerings.
However, in order to do this, //we need to make a precise comparison// between different groups, //which means that we need to control background variables.
There are not that many techniques that easily allow this, //and they are usually very hard to use.
We are beginning to see easier alternatives now.
You may have heard of A/B test, A/A test, multi-armed bandit test and more- // in fact, most of you already have heard // great talks about them before this session.
These are easily applicable and modifiable, //they are being widely used, //especially for functional and experience tests.
However, there are several issues //when we try to use them for customer offering.
For example, they lack of long term prediction methods; //there is only a limited scope of modifying the data //and comparing testing options quantitatively.
They also need to be able to consider //the influence of external factors and treatments.
And moreover, the patterns of peoples’ behaviors are hard to control.
Therefore, other alternatives are necessary.
Inferring causal effects of benefits in the practice has been very hard.
What I want to suggest instead is Bayesian interpretation.
You may know bayesian interpretation.
The core concept of bayesian probability is diachroneity, //in other words, time flow.
The probability of the hypothesis changes over time, // and prior and posterior can be computed// based on background information.
Also, this concept provides inferences// without reliance on asymptotic approximation, //and we can use this concept //without any assumptions about the previous distribution.
Therefore, this concept is very useful for real world simulation.
Bayesian time series model has already been applied to business analysis.
Google uses this model to AdSense for measuring ROI.
After learning this,I thought that //this model would be effective// for measuring and comparing the effects of offerings.
One approach developed at Google // is based on Bayesian structural time-series models.
They used these models // to construct a synthetic control — // what would have happened to outcome metric // in the absence of the intervention.
This method is flexible and modular, // and it is very useful //to measure and compare causal inferences of actions.
They released the model and the related functions as an R package, //called CausaIlmpact.
The paper about CausalImpact was hard to understand, // but the package was easy to download. You can just get it from Github.
And I could integrate // this Bayesian time series prediction model and function // with multivariate tests.
I developed the CompareImpacts function //with local functions in CausalImpact package // and a modified plotting function in this package // to compare effects of benefits at a glance // using functions in ggplot2 package.
With these, we can create comparison reports and plots much more easily- // you only need to use a single R function, // and it will help people //comparing the predicted results of offerings more simply.
Anyone can use the results, //without understanding the complicated Bayesian method // and the underlying R implementation. These functions will be shared through Github soon.
This modification allows two offering types.
One case is about providing the same offering //to different target groups.
For example, some companies have offering with all customers, // and a few days later, // they divide their customers to some groups // and want to know // how those offerings effect to each customer segment.
Another case is about trying various offers// for one targeted customer group// sequentially.
Some companies do not segment their customers //and take offerings with all customers continuously.
Others have offerings for only a single group, such as their VIPs.
However, //if they want to know and compare effects of each offering, // it is very hard to measure.
Therefore, those offerings usually become only one-use campaign and // the companies cannot use their results for the next offering.
But with my design, you can compare causal effects //and use the result // to decide and to design offering more easily.
Now, let’s see the causal effects of offering results.
First, this is the original CausalImpact package usage.
You can use it for testing each causal effect of offering, // but comparing causal effects may be difficult for some people // because of different informations of each data and complicated Bayesian concepts.
These are the cases //with modified functions developed by me, //using Google’s Causallmpact R package and ggplot2 package as I mentioned.
I’m going to show you // how to apply the functions //for the two real cases of offering data.
First, this is a case of a promotion for three different target groups.
We can see original data being plotted // in the first plot.
And we can see different simulation results // and effects of the each benefit // with these dotted lines and color-shaded areas //in second and third plots
In this example, I want to know which group is best for this promotion.
However, // size, sales amount, visit periods and other data of each group are all different and // we don't know if they affect other factors.
With this plot, //we can easily know and compare the impact for each group //without these consideration.
Next, there are some promotions for one group with time difference.
As time goes by, each data continuously changes differently//even though it is for the same group.
Sometimes there are some statistically insignificant causal effect results.
Then the plot is omitted like this.
Originally, there were three promotion results, // but one result was omitted // because of insignificance.
Unfortunately, it is very common to have a case like this.
Of course these functions will be refined and dealt with more offering cases.
Offering benefits is a very classical, proven business strategy, // and it has evolved with the technology over time.
Nowadays we can gather related data more easily, // but it is still hard to use these data enough practically.
But there are techniques //that can be used to develop offering data evaluation,// such as the Bayesian analysis that I presented.
It is possible to wrap up these new techniques so that it is easy to use.
That was my presentation.
If you have any questions or comments, // please use the office hour after this session or send me an e-mail.
I would be happy to discuss any aspects of this work.
Thank you for listening.