let people visit my content? How to let people have action/buy? How to make people come back and buy? How to make people start to buy? How to let people refer new users to your content? Acquisition Activation Retention Revenue Referral 1.25 2.5 3.75 5
How to recommend OA push to users with limited number of pushes? How to attract most possibly new users to the service? How to value customers and Recommend based on values? How to deliver ad promotion with the best cost-profit balance? How do we understand users for AD targeting? Acquisition Activation Retention and activation AARRR Revenue
“link”. - Example: Graph Attention network with deepwalk 1 2 Acquisition - Users are represented as edge connections between entity with meaningful actions - Users1: article1 read -> shop item2 -> join campaign 3…. - Each node (entity) is aggregated by the weighted sum of neighbors.
SHOPPING, SPOT, POINTS, MUSIC - Embeddings show clustering effect w.r.t campaign Performance Case study on LINE POINTS : effectiveness and fully data orientation - ≥ 25% of new user growth comes from AI’s decision. - Easily fit for different scenario : How about recommend OA Push message to non-new users? - ≥200% CTR lift Acquisition Activation
Purchase or not Purchase or not Browsing behavior Demographics Purchase history OA engagement Ad event Sticker collections Treatment Group Control Group Retention
Purchase or not Purchase or not Browsing behavior Demographics Purchase history OA engagement Ad event Sticker collections Treatment Group Control Group If the campaign effect is significant If the sure thing exist Retention
Demographics Purchase history OA engagement Ad event Sticker collections Purchase probabilities Treatment Group Control Group $MBTTJ fi FS treatment = 1 treatment = 0 Purchase or not ≈ *OUVJUJWFBOEFBTZUPUSBJO -PXWBSJBODFBOE fl FYJCMF Retention
Browsing behavior Demographics Purchase history OA engagement Ad event Sticker collections $MBTTJ fi FS Purchase probability Purchase probability treatment = 1 treatment = 0 candidate Retention
Browsing behavior Demographics Purchase history OA engagement Ad event Sticker collections $MBTTJ fi FS treatment = 1 treatment = 0 Uplift Purchase probability candidate Retention
pro fi le OA interaction Feature Engineering Modeling Tags CLV High ↑ CLV Median CLV Low ↓ { > 50% High valued customers <= 50% Medium valued customers 0, Churned customers 450 days 180 days Now Cascade model Retention Revenue
CLV High ↑ CLV Median CLV Low ↓ { > 50% High valued customers <= 50% Medium valued customers 0, Churned customers 450 days 180 days Now User Embedding Demographic Browsing history Buying pro Retention Revenue
user understanding ! #MBDLCPY Who among them are more likely to come? Which content is more likely be click by whom? Who among them are more likely persuadable? How to estimate user values? How to refer friends of users to us?
Services LINE TODAY LINE SHOPPING LINE SPOT LINE MUSIC LINE Sticker LINE VOOM LINE Reward Official Account Fact Checker LINE HELP TW LINE Travel Ads 獨立的資料工程部門,提供資料科學解決方案 LINE TODAY
SPOT LINE MUSIC LINE Sticker LINE VOOM LINE Reward Fact Checker LINE HELP TW LINE Travel NLP Knowledg e Graph Uplift Modeling NER Classifier Duplication Detector Auto completion Keyword Extraction Related Search Text Generation User Tagging Data Analytics Recom- mendation CLV 從報表、分析洞見到預測模型 LINE TODAY
architectur e • Assemble large, com plex data sets that m eet requirements Data Engineer Data Analyst Big data infra, SQL, ET L, message queuing • Interpret data, analyz e results using statisti cal techniques • Identify, analyze, and interpret trends or pat terns in complex data sets Statistics, Data Visualiz ation, Business Knowle dge SKILL RESPONSIBILITY • Select appropriate da tasets and data repre sentation methods • Research and imple ment appropriate ML algorithms Data Scientist Machine learning, deep learning, CV, NLP, Spe ech ML Svc Engineer • Build and scale mach ine learning infrastruc ture • Monitor model perfor mance System infrastructure d esign, DevOps
pipeline architectur e • Assemble large, com plex data sets that m eet requirements Data Engineer Data Analyst Big data infra, SQL, ET L, message queuing • Interpret data, analyz e results using statisti cal techniques • Identify, analyze, and interpret trends or pat terns in complex data sets Statistics, Data Visualiz ation, Business Knowle dge SKILL RESPONSIBILITY Pipeline Biz • Select appropriate da tasets and data repre sentation methods • Research and imple ment appropriate ML algorithms Data Scientist Machine learning, deep learning, CV, NLP, Spe ech Model ML Svc Engineer • Build and scale mach ine learning infrastruc ture • Monitor model perfor mance System infrastructure d esign, DevOps Service