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Business Opportunities, Challenges, Strategies and Execution in Big Data Era --- A Case of Mobile Ads Big Data

Business Opportunities, Challenges, Strategies and Executions in Big Data Era --- A Case of Mobile Ads Big Data
Cases: Duolingo, Google now, Google Flu Trends, JawBone, AppDynamics, SnapLogic, DropCam, Netflix, Ayasdi, Automatic, Nest, Wealthfront, Zephyr Health, OpenGov
3R: Reach, Richness, Range

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Business Opportunities, Challenges, Strategies and Execution in Big Data Era --- A Case of Mobile Ads Big Data

  1. 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. 2. Prelog – Myths of 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. 3. Agenda • Innovative Cases of 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. 4. Innovative Cases of BIG DATA
  5. 5. 全球最先進 的追蹤器: 活動追蹤、 睡眠追蹤、 Smart Coach 和心臟健康 記錄 iPaaS幫助各公司在雲端中及內部部 署連接企業應用程式 癌 症 分 析 視 覺 化
  6. 6. iPod 之 父Tony Fadell 創建的 恆溫器 智慧家 居公司 醫療資 料的整 合與分 析 政 府 支 出 公 開 平 台 開 車 更 省 油、 安 全 服務科 技領域 人士的 在線理 財咨詢 管理平 台
  7. 7. Big Data - Google Now
  8. 8. Big Data App
  9. 9. What is the BIG DATA eventually?
  10. 10. Outlook of Big Data  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. 11. Challenges of Big Data - 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. 12. The Revolution of Big 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. 13. Top Truth of Big Data Source: HP(2013)
  14. 14. A Case of Big Data in Mobile Ads
  15. 15. Mobile Big Data in 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. 16. In-database Processing(MPP)
  17. 17. Exploratory Architeture
  18. 18. Spark/Ha doop Cluster Exploratory Architecture Spark/Ha doop Cluster RRE In TD Multi- core RRE RRE In Spark Tableau Aggregate Export RRE In Spark
  19. 19. Pricing Engine Framework Kafka HDFS Apache Spark Jenkins Realtime processors ( Spark Streaming) DataInjection Speed Layer Batch Layer ServingLayer Kafka DataStreaming Couchbase Docker Container Avro Avro Akka/Scala Actors
  20. 20. Data Science Performance Performance (CTR, CVR, CPI) of and and DSP Data Algorithms Tools
  21. 21. Data Science Performance Problem-solving Thinking Performance (CTR, CVR, CPI) of AdNet and DSP Data Algorithms Tools
  22. 22. Yes! We have lots of DATA?!
  23. 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. 24. Data Economy Traditional -> Internet Economy HighREACH RICHNESS High Low Traditional Economy Internet Economy (quality) (quantity)
  25. 25. Reach: The Value Funnel 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. 26. Richness Data Quality  Predictive Power
  27. 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 Conversions Logs Behavioral Data Attribution Data
  28. 28. Richness  Data Quality Richness  Data Utilization Richness Download times vs. Activation days  Data Model Richness
  29. 29. Range
  30. 30. Range - Roger Martin Rothman School 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. 31. Range
  32. 32. Range
  33. 33. Range Brand Awareness View Rating Reach TV campaign Conversions Click Impression Request Range Mobile Campaign Actions Traffic Buzz Reach Offline Campaign Reach Richness Cross-screen Effect
  34. 34. 成功案例:掌握3R成效更優異! Cross-screen synergy  Big data 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. 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. 36. World, Model & Theory Credit: John F. Sowa
  37. 37. Big Data is not only about Technology but also Org. + Culture + Eco-system
  38. 38. Challenges of Big Data 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
  39. 39. Challenges of Big Data Company BDSales + AS Sales + CM Data BD Data Engineer + Data Scientist Conversions + 3rd Tracking
  40. 40. 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
  41. 41. Micro-Targeting 指定 投遞 指定 排除 曝光 頻次 APP 偵測 投放 情境 投放 廣告 偏好 指定 品牌 粉絲 產品 使用者 興趣 偏好 收集用戶行為數據 Micro-Targeting 1 2 3 大數據分析 找到潛在客群 優化投放
  42. 42. TA in a Closed 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!
  43. 43. Summary
  44. 44. 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)
  45. 45. BIG DATA Humility 謙虛 Humanity 人性 資料始終為了人性 Use Data, not be Used.
  46. 46. 謝謝大家! craig.chao@vpon.com, chaocraig@gmail.com

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