Your SlideShare is downloading. ×
Making sense of Twitter - MSM 2010 London
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×

Saving this for later?

Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime - even offline.

Text the download link to your phone

Standard text messaging rates apply

Making sense of Twitter - MSM 2010 London

1,575
views

Published on

Making sense of Twitter: A monitoring and analysis case study …

Making sense of Twitter: A monitoring and analysis case study


0 Comments
2 Likes
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total Views
1,575
On Slideshare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
31
Comments
0
Likes
2
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 1. MAKING SENSE OF TWITTER: A MONITORING & ANALYSIS CASE STUDY
  • 2. SOCIAL MEDIA: THE NEW VOICE OF THE MARKET?
  • 3. BRANDS ARE AT THE HEART OF SOCIAL MEDIA CONVERSATIONS 88% of French web users expect brands to enter to enter the conversations (CSA – March 2009) 59% of French web users frequently visit branded communities and hubs (Performics – June 2010) 40% of French Facebook users like at least one brand (OpinionWay – Octobre 2010)
  • 4. THE 4 GOLDEN RULES OF CONVERSATION 1. LISTENING ≠ HEARING 2. UNDERSTANDING ≠ MEASURING 3. ENGAGING ≠ ADVERTISING 4. LONG TERM ≠ INSTANT
  • 5. TWITTER: A PRECURSOR OF FUTURE WEB USAGE? Creating and transmitting information: journalists and blogers, PR professionals, brands Sending automated tweets (RSS feeds, twittbots): media, web news sites, e-commerce Sharing private life, personal feeling, web discoveries, buzz : personal users and people Monitoring and sharing professional information: digital marketing, SM and MarCom professionals
  • 6. TWITTER IN A SMM PROJECT Monitoring and analysing Twitter is useful for three main reasons: For itself to analyse the content of the messages and images shared To detect links to interesting information As an alert system on hot topics and emerging issues Furthermore, thanks to its open API, monitoring set up is very easy
  • 7. CASE STUDY
  • 8. INTRODUCTION A FMCG market Brands with IRL points of sales A global ongoing SM Monitoring & Analysis project 9 competitors monitored 20,000 different sources monitored and more than 100,000 documents collected, qualified and analysed per year Twitter’s weight among all documents collected: 82%
  • 9. METHODOLOGY (1/3) Collection of 45,438 tweets from the 01/01 to 15/09 24,042 factual tweetsAnalysis Tweets with url Tweets without url Analytical sample 1,000 tweets Analysis base 21,396 meaningful tweets Analytical sample 1,000 tweets
  • 10. METHODOLOGY (2/3) 1,000 tweets with url Qualification Main expression theme: brand, corporate/communication, products, services & employment Subject/news having ignited conversation
  • 11. METHODOLOGIE (3/3) 1,000 tweets without url Qualification Intention: initial tweeting reason (expression, question, transmission or comment on an information) Main expression theme: brand, corporate/communication, products, services & employment Attitude, behaviour or opinion expressed around main expression theme 12 patterns detected 3 opinions: positive, negative and neutral opinion 3 behaviours: consumption, buzz, chit chat 6 attitudes: expectation, announcement, question, notification, confirmation, recommendation
  • 12. ANALYSIS – THE WEIGHT OF FACTUAL TWEETS 53% of all tweets are factual vs. 31% for other types of documents: users only quote brands/products or their visit to a specific point of sales  No analytical value except for Share of Voice assessment
  • 13. ANALYSIS – MEANINGFUL TWEETS 47% of all tweets: beyond products/brands and points of sales, an action, a sentiment, an opinion is expressed  85 meaningful tweets on average per day: a 200% increase over 8 months !!!
  • 14. ANALYSIS – MEANINGFUL TWEETS 11,818 «twitterers » for 21,396 tweets : 1,8 tweet on average per user 68% only twitted once 4% twitted more often than once a month Only 10 of them twitted more than 30 times: this top 10 accounts for 5% of all tweets. The top 100 accounts for 17% of all tweets  No 20 80 rule: a few hardcore users, but lots of casual twitterers
  • 15. ANALYSIS – MEANINGFUL TWEETS Top 20 twiterrers: 12 hardcore «twitterers » tweeting and retweeting on everything (users who twitted on average 30,000 times since they use Twitter) 4 buzz and digital marketing specialised blogers: interesting influencers for digital and offline campaigns 2 employees of one of the brands 1 corporate stakeholder who twitted because of 3 major corporate crisis 1 sector blog: specialised in brands and products  Large volumes from generalists and identification of interesting influencers
  • 16. ANALYSIS – MEANINGFUL TWEETS WITH URL 49% of all meaningful tweets An information transmission logic  Corporate information (business, finance, MarCom) is ahead. Information on products and services, mainly innovations, is shared by a user in 5. Corporate Products Services Employment
  • 17. ANALYSIS – MEANINGFUL TWEETS WITH URL Key news/events drive tweeting:  Almost half of meaningful tweets with url are due to 15 news/events with the two main accounting for 20% of them
  • 18. ANALYSIS – MEANINGFUL TWEETS WITH URL Among these 15 news/events, we found: 6 buzz on ads and digital campaigns (11% of the volume) 2 buzz on corporate crisis (18% of the volume) 2 buzz on new services (5% of the volume) 2 buzz on product launches (3% of the volume) 2 buzz on corporate issues (2% of the volume) 1 buzz on SM strategy of one of the brands (5% of the volume)  Helpful insights and measures for corporate crisis management and campaigns effectiveness assessment
  • 19. CRISIS MANAGEMENT
  • 20. ANALYSIS – MEANINGFUL TWEETS WITHOUT URL 51% of all meaningful tweets A spontaneous expression logic:  Two thirds of spontaneous expression for one third of information transmission, comment or seeking Spontaneous expression Information transmission Comments on information Information seeking
  • 21. ANALYSIS – MEANINGFUL TWEETS WITHOUT URL Information on every brand topics:  All themes from corporate to operational are covered 6% 7% 9% 9% 14% 16% 18% 21% Employé Référence Prix/promo Services Expérience PdV Marque Communication ProduitsProducts Communication Branding POS experience Services Price/promotion Pointless reference Employee feedback
  • 22. ANALYSIS – MEANINGFUL TWEETS WITHOUT URL A third of these tweets are expressing opinions:  Mainly negative comments on advertising campaigns, whereas products and services drive positive comments  An analytical value for ad testing and for products/services evaluation Negative comments Positive comments Balanced comments
  • 23. TRACKING OPINIONS TO CREATE BRANDED CONTENT
  • 24. ANALYSIS – MEANINGFUL TWEETS WITHOUT URL A third of these tweets are expressing behaviours:  Importance of pointless discussion about products. Lots of online buzz discussed. Interst in understanding specific usage and consumption situations Chit chat Buzz Consumption
  • 25. FROM BEHAVIOURS AND OPINIONS LISTENING TO ENGAGEMENT
  • 26. ANALYSIS – MEANINGFUL TWEETS WITHOUT URL A third of these tweets are expressing attitudes:  The expression of expectation is far ahead. Announcement and anticipation of new services and products follow. 3% of these tweets are actual recommendations Expectation Announcement Question Notification Confirmation Recommendation
  • 27. TRACKING AND MANAGING EXPECTATION
  • 28. RECOMMENDATION AS A PROMOTION DRIVER
  • 29. AS A CONCLUSION
  • 30. TWITTER AS AN INSIGHT SOURCE Useful and unique vs. other MR tools for understanding immediate experience with TV ads, point of sales, etc. Has to be taken in a global SMM perspective Has to be analysed in the long term to gather value information Despite its short format, needs to be analysed through specific qualification patterns Has to be processed through text mining AND human approach to detect language subtleties and nuances
  • 31. TWITTER AS A DIGITAL STRATEGY OPPORTUNITY A tool for alerting and measuring A tool for crowdsourcing content creation A tool for brand promotion A tool for customer care