Uploaded byCraig Chao
Business Opportunities, Challenges, Strategies and Execution in Big Data Era --- A Case of Mobile Ads Big Data
The document discusses the opportunities, challenges, strategies, and execution related to big data, particularly in mobile advertising. It highlights the importance of data quality and context in understanding user behavior, while addressing common challenges such as tool costs and organizational metrics. The overall message underscores that big data is not just a technological issue, but also involves organizational culture and ecosystem dynamics.
More Related Content
Similar to Business Opportunities, Challenges, Strategies and Execution in Big Data Era --- A Case of Mobile Ads Big Data
![제 23회 보아즈(BOAZ) 빅데이터 컨퍼런스 - [MBOAX] : ABSA를 활용한 소비자 반응 분석 기반 운영 효율화 대시보드 설계](https://cdn.slidesharecdn.com/ss_thumbnails/3-1boaz23rdconferencemboax-260203102709-9d519923-thumbnail.jpg?width=640&height=640&fit=bounds)
Business Opportunities, Challenges, Strategies and Execution in Big Data Era --- A Case of Mobile Ads Big Data
- 1. Big Data 的商機、挑戰、策略與執行 ---以移動廣告大數據為例 Vpon 行動科技 數據科學家 趙國仁 Data Scientist Craig Chao craig.chao@vpon.com, chaocraig@gmail.com Business Opportunities, Challenges, Strategies and Execution in Big Data Era --- A Case of Mobile Ads Big Data
- 2. Prelog – Mythsof Big Data Big Data, Big Hype? Machine Learning & Statistics have been used in many places, nothing new in Big Data? Big Data is Hadoop / Open Source?
- 3. Agenda • Innovative Casesof BIG DATA • What is the BIG DATA eventually? • A Case of Big Data in Mobile Ads • Yes! We have lots of DATA?! • Big Data is not only about Technology but also Org.+Culture+Eco-system • Summary
- 4.
- 5.
- 6.
- 7. Big Data -Google Now
- 8.
- 9. What is theBIG DATA eventually?
- 10. Outlook of BigData Hard to be handled by traditional RDB/SQL DB Sources Intranet：Machine logs Extranet：Internet users & machines Difficult to be utilized by only statistical sampling “If you have people in the loop, it’s not real time.” Joe Hellerstein, Chancellor’s Professor of Computer Science at UC Berkeley
- 11. Challenges of BigData - 4V 資料量大 資料多樣性 資料輸入 和處理速度快 資料真實性
- 12. The Revolution ofBig Data DATA Hypotheses Statistical Analysis BIG DATA Hypotheses Machine Learning Data Mining Machine-generated Sampling, Multi-variant… All, Hyper space, … Volume, Velocity, Variety, Veracity Human-explainable
- 13. Top Truth ofBig Data Source: HP(2013)
- 14. A Case ofBig Data in Mobile Ads
- 15. Mobile Big Datain Vpon • Profile • Classification • Recommendation Retargeting 2B+ in China 6M+ in HK 17M+ in TW User Behavior Data Mine 20GB/day 20TB/year MLDM to mine the data value
- 16.
- 17.
- 18.
- 19. Pricing Engine Framework Kafka HDFS ApacheSpark Jenkins Realtime processors ( Spark Streaming) DataInjection Speed Layer Batch Layer ServingLayer Kafka DataStreaming Couchbase Docker Container Avro Avro Akka/Scala Actors
- 20. Data Science Performance Performance(CTR, CVR, CPI) of and and DSP Data Algorithms Tools
- 21. Data Science Performance Problem-solving Thinking Performance(CTR, CVR, CPI) of AdNet and DSP Data Algorithms Tools
- 22. Yes! We have lotsof DATA?!
- 23. 3R: Reach, Richness,Range Reach Richness High High Low 使用者接觸量(DAU) 資料豐富度 (Behavioral data) Range High 使用者情境 (The audience affiliate of whole context)
- 24. Data Economy Traditional ->Internet Economy HighREACH RICHNESS High Low Traditional Economy Internet Economy (quality) (quantity)
- 25. Reach: The ValueFunnel CPM campaign: Revenue = N/1000 ⋅CPM CPC campaign: Revenue = N ⋅ CTR ⋅ CPC CPA campaign: Revenue = N ⋅ CTR ⋅ CVR⋅ CPA UU Reach (DAU) ARPU = Life-time Value
- 26. Richness Data Quality Predictive Power
- 27. Richness: Predictive Power APP類型偏好 使用裝置 使用時間 定位區域 廣告行為偏好 ConversionsLogs Behavioral Data Attribution Data
- 28. Richness Data QualityRichness Data Utilization Richness Download times vs. Activation days Data Model Richness
- 29.
- 30. Range - Roger Martin RothmanSchool of Management, Toronto If only attach importance to quantify the business model, it will not have the ability to find a potential growth opportunities: "The pursuit of quantifying the biggest problem is that people ignore the context of the behavior generated, detached from the context of the event, and have not been included in the model ignores variables effectiveness. " 企業若只重視量化模式， 將無法擁有尋得潛在成長 契機的能力：「追求量化 最大的問題在於，忽略人 們產生行為的脈絡，把事 件從情境中抽離，且忽略 沒有被納入模式中的變數 效力。」
- 31.
- 32.
- 33. Range Brand Awareness View Rating Reach TV campaign Conversions Click Impression Request Range MobileCampaign Actions Traffic Buzz Reach Offline Campaign Reach Richness Cross-screen Effect
- 34. 成功案例：掌握3R成效更優異！ Cross-screen synergy Bigdata synergy with Cross-screen effect。 +TV 0.00% 2.00% 4.00% 6.00% 8.00% 10.00% 12.00% 14.00% 16.00% 0 5000 10000 15000 20000 25000 30000 35000 40000 Thu Fri Sat Sun Mon Tue Wed Thu Fri Sat Sun APP下載率 優化轉換率 App Download Rate Optimized Conversion Rate
- 35. 3R: Reach, Richness,Range Reach Richness High High Low 使用者接觸量(DAU) 資料豐富度 (Behavioral data) Range High 使用者情境 (The audience affiliate of whole context)
- 36. World, Model &Theory Credit: John F. Sowa
- 38. Big Data isnot only about Technology but also Org. + Culture + Eco-system
- 39. Challenges of BigData Company Tools Commercial Big Data Tools is Expensive Open Source Tools need high-skill talents Organization Performance metric of developers Most people do not understand 3R of data Data BD, Campaign Manager, Data Engineer, Data Scientist Time Accumulate behavioral data, Tuning models, Org & Culture changes
- 40. Challenges of BigData Company BDSales + AS Sales + CM Data BD Data Engineer + Data Scientist Conversions + 3rd Tracking
- 41. After dis-intermediary / Re-intermediary 品牌會員資料庫 •姓名 • 年齡 • 電話 • email • 地址 • 購買產品 • 職業… 會員資料庫更新 不易，部份資料 參考性低 AdN資料庫 • 使用手機 • 經常出沒定位 • 使用APP • 使用時間 • 廣告偏好… 持續收集使用者 行動數據行為喜 好，發掘TA潛在 喜好需求 ! 連結 資料庫 M-CRM 活 化 First-party Data Third-party Data
- 42.
- 43. TA in aClosed Loop 數據分析 廣告投放 消費者輪廓 更有效的接觸 你的消費族群 APP類型偏好 使用裝置 使用時間 定位區域 廣告行為偏好 為下次的活動做足 準備，優化成效！
- 44.
- 45. Data-driven Performance Problem-solving Thinking Performance (CTR, CVR,CPI) of AdNet and DSP Data Algorit hms Tools Reach Richness High High Low 使用者接觸量 (DAU) 資料豐富度 (Behavioral data) Range High 使用者情境 (The audience affiliate of whole context)
- 46.
- 47.