Social Media Marketing Analytics ( 社 群 媒 體 行 銷 分 析 )

Transcription

1 Tamkang University Social Compu,ng and Big Data Analy,cs 社 群 運 算 與 大 數 據 分 析 Tamkang University Social Media Marketing Analytics ( 社 群 媒 體 行 銷 分 析 ) 1042SCBDA08 MIS MBA (M2226) (8628) Wed, 8,9, (15:10-17:00) (B309) Min-Yuh Day 戴 敏 育 Assistant Professor 專 任 助 理 教 授 Dept. of Information Management, Tamkang University 淡 江 大 學 資 訊 管 理 學 系 tku.edu.tw/myday/

2 2 課 程 大 綱 (Syllabus) 週 次 (Week) 日 期 (Date) 內 容 (Subject/Topics) /02/17 Course OrientaKon for Social CompuKng and Big Data AnalyKcs ( 社 群 運 算 與 大 數 據 分 析 課 程 介 紹 ) /02/24 Data Science and Big Data AnalyKcs: Discovering, Analyzing, Visualizing and PresenKng Data ( 資 料 科 學 與 大 數 據 分 析 : 探 索 分 析 視 覺 化 與 呈 現 資 料 ) /03/02 Fundamental Big Data: MapReduce Paradigm, Hadoop and Spark Ecosystem ( 大 數 據 基 礎 :MapReduce 典 範 Hadoop 與 Spark 生 態 系 統 )

3 課 程 大 綱 (Syllabus) 週 次 (Week) 日 期 (Date) 內 容 (Subject/Topics) /03/09 Big Data Processing PlaYorms with SMACK: Spark, Mesos, Akka, Cassandra and Ka]a ( 大 數 據 處 理 平 台 SMACK: Spark, Mesos, Akka, Cassandra, Ka]a) /03/16 Big Data AnalyKcs with Numpy in Python (Python Numpy 大 數 據 分 析 ) /03/23 Finance Big Data AnalyKcs with Pandas in Python (Python Pandas 財 務 大 數 據 分 析 ) /03/30 Text Mining Techniques and Natural Language Processing ( 文 字 探 勘 分 析 技 術 與 自 然 語 言 處 理 ) /04/06 Off-campus study ( 教 學 行 政 觀 摩 日 ) 3

4 課 程 大 綱 (Syllabus) 週 次 (Week) 日 期 (Date) 內 容 (Subject/Topics) /04/13 Social Media MarkeKng AnalyKcs ( 社 群 媒 體 行 銷 分 析 ) /04/20 期 中 報 告 (Midterm Project Report) /04/27 Deep Learning with Theano and Keras in Python (Python Theano 和 Keras 深 度 學 習 ) /05/04 Deep Learning with Google TensorFlow (Google TensorFlow 深 度 學 習 ) /05/11 SenKment Analysis on Social Media with Deep Learning ( 深 度 學 習 社 群 媒 體 情 感 分 析 ) 4

5 課 程 大 綱 (Syllabus) 週 次 (Week) 日 期 (Date) 內 容 (Subject/Topics) /05/18 Social Network Analysis ( 社 會 網 絡 分 析 ) /05/25 Measurements of Social Network ( 社 會 網 絡 量 測 ) /06/01 Tools of Social Network Analysis ( 社 會 網 絡 分 析 工 具 ) /06/08 Final Project PresentaKon I ( 期 末 報 告 I) /06/15 Final Project PresentaKon II ( 期 末 報 告 II) 5

6 Social Media Marke,ng Analy,cs 6

7 Digital Marketing Analytics: Making Sense of Consumer Data in a Digital World, Chuck Hemann and Ken Burbary, Que Source: 7

8 8 Consumer Psychology and Behavior on Social Media

9 How consumers think, feel, and act Source: Philip Kotler & Kevin Lane Keller, MarkeKng Management, 14th ed., Pearson,

10 Analyzing Consumer Markets The aim of markekng is to meet and saksfy target customers needs and wants beger than compektors. Marketers must have a thorough understanding of how consumers think, feel, and act and offer clear value to each and every target consumer. Source: Philip Kotler & Kevin Lane Keller, MarkeKng Management, 14th ed., Pearson,

11 Customer Perceived Value, Customer Sa,sfac,on, and Loyalty Customer Perceived Performance Customer Perceived Value Customer SaKsfacKon Customer Loyalty Customer ExpectaKons Source: Philip Kotler & Kevin Lane Keller, MarkeKng Management, 14th ed., Pearson,

12 Social Media Marke,ng Analy,cs Social Media Listening Search AnalyKcs Content AnalyKcs Engagement AnalyKcs 12

13 The Convergence of Paid, Owned & Earned Media Paid Media Traditional Ads Promoted Brand Content Converged Media Owned Media Corporate Ads Sponsored Customer Brands that ask for shared Press Coverage Earned Media Organic Source: The Converged Media Imperative: How Brands Will Combine Paid, Owned and Earned Media, Altimeter Group, July 19, 2012) 13

14 Converged Media Top 11 Success Criteria Social Listening / Analysis of Crowd Source: The Converged Media Imperative: How Brands Will Combine Paid, Owned and Earned Media, Altimeter Group, July 19, 2012) 14

15 Content Tool Stack Hierarchy Source: Rebecca Lieb, "Content marketing in research, not predictions", December 16,

16 Compe,,ve Intelligence Gather compekkve intelligence data 16

17 Google Alexa Compete Which audience segments are compektors reaching that you are not? What keywords are successful for your compektors? What sources are driving traffic to your compektors websites? 17

18 Compe,,ve Intelligence Facebook compekkve analysis Facebook content analysis YouTube compekkve analysis YouTube channel analysis Twiger profile analysis 18

19 Web Analy,cs (Clickstream) Content AnalyKcs Mobile AnalyKcs 19

20 Mobile Analy,cs Where is my mobile traffic coming from? What content are mobile users most interested in? How is my mobile app being used? What s working? What isn t? Which mobile playorms work best with my site? How does mobile user s engagement with my site compare to tradikonal web users engagement? 20

21 Iden,fying a Social Media Listening Tool Data Capture Spam PrevenKon IntegraKon with Other Data Sources Cost Mobile Capability API Access Consistent User Interface Workflow FuncKonality Historical Data 21

22 Search Analy,cs Free Tools for CollecKng Insights Through Search Data Google Trends YouTube Trends The Google AdWords Keyword Tool Yahoo! Clues Paid Tools for CollecKng Insights Through Search Data The BrightEdge SEO PlaYorm 22

23 Owned Social Metrics Facebook page Twiger account YouTube channel 23

24 Own Social Media Metrics: Facebook Total likes Reach Organic Paid reach Viral reach Engaged users People taking about this (PTAT) Likes, comments, and shares by post 24

25 Own Social Media Metrics: TwiYer Followers Retweets Replies Clicks and click-through rate (CTR) Impressions 25

26 Own Social Media Metrics: YouTube Views Subscribers Likes/dislikes Comments Favorites Sharing 26

27 Own Social Media Metrics: SlideShare Followers Views Comments Shares 27

28 Own Social Media Metrics: Pinterest Followers Number of boards Number of pins Likes Repins Comments 28

29 Own Social Media Metrics: Google+ Number of people who have an account circled +1s Comments 29

30 Earned Social Media Metrics Earned conversakons In-network conversakons 30

31 Earned Social Media Metrics: Earned conversa,ons Share of voice Share of conversakon SenKment Message resonance Overall conversakon volume Source: 31

32 Demys,fying Web Data Visits Unique page views Bounce rate Pages per visit Traffic sources Conversion 32

33 Searching for the Right Metrics Paid Searches Organic Searches 33

34 Paid Searches Impressions Clicks Click-through rate (CTR) Cost per click (CPC) Impression share Sales or revenue per click Average posikon 34

35 Organic Searches Known and unknown keywords Known and unknown branded keywords Total visits Total conversions from known keywords Average search posikon 35

36 Aligning Digital and Tradi,onal Analy,cs Primary Research Brand reputakon Message resonance ExecuKve reputakon AdverKsing performance TradiKonal Media Monitoring TradiKonal CRM Data 36

37 Social Media Listening Evolu,on LocaKon of conversakons SenKment Key message penetrakon Key influencers 37

38 Social Analy,cs Lifecycle (5 Stages) 1. Discover 2. Analyze 3. Segment 4. Strategy 5. ExecuKon 38

39 Social Analy,cs Lifecycle (5 Stages) 1. Discover 1. Discover 2. Analyze 3. Segment Social Web (blogs, social networks, forums/ message boards, Video/phone sharing) 4. Strategy 5. ExecuKon 39

40 Social Analy,cs Lifecycle (5 Stages) 1. Discover Social Web (blogs, social networks, forums/message boards, Video/phone sharing) 2. Analyze Distill relevant signal from social noise 3. Segment 4. Strategy 5. ExecuKon 40

41 Social Analy,cs Lifecycle (5 Stages) 1. Discover Social Web (blogs, social networks, forums/message boards, Video/phone sharing) 2. Analyze Distill relevant signal from social noise 3. Segment Data Segmentation (Filter, Group, Tag, Assign) 4. Strategy Strategic Planning Product Development Corps CommunicaKon MarkeKng & AdverKsing Customer Care Sales 5. ExecuKon Strategic Tactical 41

42 Social Analy,cs Lifecycle (5 Stages) 1. Discover Social Web (blogs, social networks, forums/message boards, Video/phone sharing) 2. Analyze Distill relevant signal from social noise 3. Segment Data Segmentation (Filter, Group, Tag, Assign) 4. Strategy Insights drive focused business strategies 5. ExecuKon 42

43 Social Analy,cs Lifecycle (5 Stages) 1. Discover Social Web (blogs, social networks, forums/message boards, Video/phone sharing) 2. Analyze Distill relevant signal from social noise 3. Segment Data Segmentation (Filter, Group, Tag, Assign) 4. Strategy Insights drive focused business strategies 5. ExecuKon InnovaKon Future DirecKon ReputaKon Management Campaigns CRM Customer SaKsfacKon Improvements 43

44 Social Analy,cs Lifecycle (5 Stages) 1. Discover Social Web (blogs, social networks, forums/message boards, Video/phone sharing) 2. Analyze Distill relevant signal from social noise 3. Segment Data Segmentation (Filter, Group, Tag, Assign) 4. Strategy Insights drive focused business strategies 5. ExecuKon InnovaKon Future DirecKon ReputaKon Management Campaigns CRM Customer SaKsfacKon Improvements 44

45 How consumers think, feel, and act Source: Philip Kotler & Kevin Lane Keller, MarkeKng Management, 14th ed., Pearson,

46 Emo,ons Love Anger Joy Sadness Surprise Fear Source: Bing Liu (2011), Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data, Springer, 2nd Edition, 46

47 Maslow s Hierarchy of Needs Source: Philip Kotler & Kevin Lane Keller, MarkeKng Management, 14th ed., Pearson,

48 Maslow s hierarchy of human needs (Maslow, 1943) Source: Backer & Saren (2009), MarkeKng Theory: A Student Text, 2 nd EdiKon, Sage 48

49 Maslow s Hierarchy of Needs Source: 49

50 Social Media Hierarchy of Needs Source: 50

51 Social Media Hierarchy of Needs Source: 51

52 The Social Feedback Cycle Consumer Behavior on Social Media Marketer-Generated User-Generated Awareness Considera,on Purchase Use Form Opinion Talk Source: Evans et al. (2010), Social Media Marketing: The Next Generation of Business Engagement 52

53 The New Customer Influence Path Awareness Considera,on Purchase Source: Evans et al. (2010), Social Media Marketing: The Next Generation of Business Engagement 53

54 Example of Opinion: review segment on iphone I bought an iphone a few days ago. It was such a nice phone. The touch screen was really cool. The voice quality was clear too. However, my mother was mad with me as I did not tell her before I bought it. She also thought the phone was too expensive, and wanted me to return it to the shop. Source: Bing Liu (2011), Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data, Springer, 2nd Edition, 54

55 Example of Opinion: review segment on iphone (1) I bought an iphone a few days ago. (2) It was such a nice phone. (3) The touch screen was really cool. (4) The voice quality was clear too. (5) However, my mother was mad with me as I did not tell her before I bought it. +Positive Opinion (6) She also thought the phone was too expensive, and wanted me to return it to the shop. -Negative Opinion Source: Bing Liu (2011), Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data, Springer, 2nd Edition, 55

56 Attensity: Track social sentiment across brands and competitors

57 Sen,ment Analysis vs. Subjec,vity Analysis Sentiment Analysis PosiKve NegaKve Subjectivity Analysis SubjecKve Neutral ObjecKve 57

58 Example of Sen,WordNet POS ID PosScore NegScore SynsetTerms Gloss a beaukful#1 delighkng the senses or excikng intellectual or emokonal admirakon; "a beaukful child"; "beaukful country"; "a beaukful painkng"; "a beaukful theory"; "a beaukful party a best#1 (superlakve of `good') having the most posikve qualikes; "the best film of the year"; "the best solukon"; "the best Kme for plankng"; "wore his best suit r unhappily#2 sadly#1 in an unfortunate way; "sadly he died before he could see his grandchild r woefully#1 sadly#3 lamentably#1 deplorably#1 in an unfortunate or deplorable manner; "he was sadly neglected"; "it was woefully inadequate r sadly#2 with sadness; in a sad manner; "`She died last night,' he said sadly" 58

59 References Chuck Hemann and Ken Burbary, Digital MarkeKng AnalyKcs: Making Sense of Consumer Data in a Digital World, Que