This document discusses social media netnography, which involves tapping into online conversations to gather insights. It outlines the process of traditional research compared to netnography. Netnography uses text analytics to extract terms and categorize them to analyze sentiment, emotions, and trends. Insights can be generated about target groups, brands, products, and customer experience. The document provides an example analysis of online conversations about seizure medications, highlighting strengths and weaknesses of different brands. It notes that social media netnography requires time and iteration to analyze conversations and should be used to complement traditional research methods.

1. Social Media Nethnography Managing and Mining the Consumer Conversation 1

2. 2 @steven_insites @nielsamz

3. Word of mouth

10. The belief Word-of-mouth is THE driver of growth

11. INTEGRATION of WOM in all marketing activities The goal

12. MANAGE word-of-mouth

13. The BIG worLd of mouth test!

14. The BIG worLd of mouth test! You use social networks at least weekly (e.g. Facebook, Twitter, LinkedIn) 1

15. The BIG worLd of mouth test! You sometimes check online buzz about your brand 2

16. The BIG worLd of mouth test! You have automated systems to monitor buzz about your brand(e.g. Google Alert, Tweetdeck) 3

17. The BIG worLd of mouth test! Do you perform in-depth analyses of social media for you brand / business? 4

18. The BIG worLd of mouth test! You participate in online conversations 5

19. A revolutionimpliesCHANGE

20. 20 Bestseller ‘The Conversation Manager’ Available on Amazon The first interactive management book for iPad, download available

21. NeedforRADICAL change

22. As a manager As a brand As a peer Observe Facilitate Join

24. limitations of interview based techniques

25. Would not we want to do this?

26. Life Caching User Generated Content

27. Social media netnography

28. Tapping into online conversations Social Media Nethnography Social Media Dashboards / Monitoring exploratory for enhanced quality

29. Social Media Nethnography Traditional research SURVEY/ TOPIC GUIDE DEVELOPMENT what do we want to ask SAMPLING selection of sources/situations you want to observe SAMPLING selection of participants we want to talk to DATA COLLECTION with the aid of research blogs or webscraping DATA COLLECTION using pre-defined research tools, traditional ones or innovative ones FRAMEWORK DEVELOPMENT what do we want to observe? which conversations interest us? ANALYSIS answering questions that were predefined TOP-DOWN & BOTTOM-UP ANALYSIS Combination of quantitative & qualitative analysis techniques + text analytics

30. TWO core principles

31. What is? Text analytics is the process of extracting knowledge and information from text Stage 1: Extraction of terms What do I want to use? Stage 2: Group terms in higher level concepts or categories

32. Extracting data Extraction = What will we use in further analysis and what will we ignore? Bottom-up Keep terms that sponaneously pop-up Top-down Keep terms based on the market knowledge

33. Categorization Categories are higher-level concepts that represent higher level ideas and information in the text // answers option in quantitative survey

34. Analysis process

35. Sentiment analysis Sentiment of theconversation Sentiment of abrand / topic Sentence level Conversation level 'I had a terribleday. I drank some Lipton tea and feltbetter 'I had a terrible day. I drank some Lipton tea and feltbetter 1 positive – 1 negative 1 positive

36. Model for measuring emotions Happiness Happy Joyful Cheerful Satisfied Proud Elated, Excited, Enthusiastic Excitement Relaxed Contented, Relaxed, Satisfied Social Trust, Love, Respect Surprise Amazed Surprised Astonished Beauty Beautiful, Admired, Attractive Fear Afraid Nervous Tense Worried Insecure Emotions Anger Angry Furious Mad Irritated Resentful Violence Aggressive, Abusive, Dominating Swear words Sadness Sad Disappointed Regretful Gloomy Pessimistic Agony, Despair, Ashamed Despair Tired , Bored, Weak Weak Loneliness Lonely, Rejected, Alienated Disgust Disgust Contempt Nasty

37. Insight generation What is my target group concerned about? What are trends in the market? What are your blind spots? Brand analyses What is the share-of-voice and sentiment of my brand online? What is my online brand positioning? How do my competitors perform? How is the impact of my advertising affecting online buzz? Advertising impact How is the impact of my advertising affecting online buzz? Product How is a new product launch evaluated? How is a new product diffusing in the market? Customer Experience How are different touch points evaluated? What are reasons to switch? How strong is recommendation behaviour / referral value?

38.  81.861

39. Major diseases when getting older? Top 20 discussed diseases physical psychological

41. Never endingClothes Housing Dog Payment Personal care Law Diseases Identity Treatment Telephone call Guilt Buzz volume

42. Alzheimer DrugsSentimeter All posts Posts about AH Drugs Posts about Aricept Posts about Namenda Posts about Reminyl Comparison with sentiment across brands Significantly higher No significant difference Significantly lower 60° 24° 9° 12° 66° N=81861 N=878 N=644 N=235 N=193

43.  39.812

44. 30 information segmentsbottom-up segmentation Diagnosis Health care Driving Confused Helpful Brand C Side effects Problem Seizures Job Having pain Control normal behaviour School Wishes for thefuture night Awareness & knowledge Husband Satisfaction Important Headache Scared Difficult Treatment Children Food & drinks Dosage Brand A Budget Brand B

45. 25% Natural & medical language about seizures Learn what consumers tell each other Find hidden patterns – blind spots Optimize online marketing strategy

47. People just stand and starevacantly

48. My mind was wandering, blank, in a daze

49. When asked to speak, would talk then just goblankand lose my entire train of thought and having no idea what I had implied

51.  Online Brandscape How are the different brands evaluated online?

52. Share brands in total amount of conversations Total sample = 1327 About brands = 327

53. BrandsSentiment & performance analysis % Emotionality % Positive % Negative Average rating review sites 73% 68% N = 101 28% 2,69 1,75 90% 81% N = 48 52% 79% 64% N = 39 1,1 41% 78% 78% N = 36 3 25%

54. BrandsIn-depth analysis B&J % Emotionality % Positive % Negative Average rating review sites 73% 68% N = 101 28% 2,69 Strengths Different&uniqueflavours Strong fanbaseonline: ‘the best’, ‘delicious’, B&J rules. However, fan base is less for the brand in general than for specificflavours. Weaknesses Too expensive for part of the consumers to consume it on a regular base Known for Quality of ice cream and perceived equal to Haägen Dasz Corporate social responsibility: press releases about attempts to reduce impact of produding ice cream on the environment are spread & discussed Vermont & cows

55.  71.230

56. 1 Input for future broadcasting Evaluation of specific aspects of the show Information needs 2

58. Answers to questions you did not ask Answers without asking questions

59. 55 Where does Text Analytics make a difference? Automational - Transformational Handling complexity Sentence parcing Dictionaries Co-occurence and linkage analyses

60. Languages

61. Lessons learned & limitations  Social Media Nethnography is not a short cut Analyse and audit, then track Time investment for client and agency required through iterative process  It is still developing Language & dictionaries Limited profile information Upfront feasibility check for critical mass  We still need to ask questions Only the question follows the answers – a bit like Jeopardy Complementary to interview based research

62. It’s time to jump and to become… The Conversation Manager

63. Not just aboutobserving & joining social media

64. integration of word-of-mouthall marketing thinking & acting

65. From control towards leadership

66. As a manager As a brand As a peer Observe Facilitate Join

68. 64 Thank you Contact us www.insites.eu +32 9 269 15 00 @steven_insites steven@insites.eu @nielsamz niels@insites.eu