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Social biz dashboard
- 1. Towards Social Analytics 2.0 June 4th 2012 OPENKNOWLEDGE SRL MILANO ď¸LONDON ď¸SYDNEY ď¸SHANGHAI
- 2. The odd one out is? Holistic relationship view Newtonian thinking Open Knowledge srl â Towards Social Analytics 2.0 @ Social Business Forum 2012 (p. 2)
- 3. Analytics: not just a buzzword Open Knowledge srl â Towards Social Analytics 2.0 @ Social Business Forum 2012 (p. 3)
- 10. Social Analytics â1.0â is quantity over quality Complex Social Communities Individual System Phenomenon ⢠No. of members ⢠No. of friends ⢠No. of forum posts ⢠No. of followers Analytics ⢠No. of home page hits ⢠No. of web posts ⢠No. of âlikesâ of community page ⢠No. of Linkedin connections ⢠No. of mentions ⌠More Heat than Light? Open Knowledge srl â Towards Social Analytics 2.0 @ Social Business Forum 2012 (p. 12)
- 11. Social Analytics 2.0 Complex Social Relationships thinking System Phenomenon ⢠No. of relationships Analytics ⢠Strength of relationships ⢠Diversity of connections ⢠Density of connections ⢠Inward/outward connection balance ⢠Brokerage/Bridging ⢠Central Connection ⢠Reciprocated (trust) connections ⢠Weak vs Strong ties Open Knowledge srl â Towards Social Analytics 2.0 @ Social Business Forum 2012 (p. 13)
- 12. Who is more influential? Abbot Costello Social Analytics 1.0 Social Analytics 2.0 Costello is more influential. While he has Abbott is more influential. He has 7 less followers, the 2 he has can mobilize followers, Costello only has 2. substantially more co-operative action. Open Knowledge srl â Towards Social Analytics 2.0 @ Social Business Forum 2012 (p. 14)
- 13. Who can we least afford to lose? Social Analytics 1.0 Abbot Abbott has many more connections. Clearly he is the one we couldnât afford to lose. Social Analytics 2.0 But if we lose Costello we will lose an important Costello bridge between two important units. Most of Abbots connections are covered by others. Open Knowledge srl â Towards Social Analytics 2.0 @ Social Business Forum 2012 (p. 15)
- 14. Which idea is the most prospective? Business Unit A Social Analytics 1.0 Business Unit B Business Unit C Business Unit D This idea has more Business Unit E votes/support so it is more likely to be implemented Social Analytics 2.0 This idea has less votes but support is more broad- based with some of the supporters being senior executives Open Knowledge srl â Towards Social Analytics 2.0 @ Social Business Forum 2012 (p. 16)
- 15. Innovation Context Social Analytics 1.0 Social Analytics 2.0 No. of ideas Social context from which ideas emerge No. of idea likes Diversity of support for ideas No. of idea comments Diversity of participation in progression of ideas No of ideas approved for No. of idea promoters and implementation exploiters participating? Open Knowledge srl â Towards Social Analytics 2.0 @ Social Business Forum 2012 (p. 17)
- 16. Social Business Dashboard Examples - Innovation Open Knowledge srl â Towards Social Analytics 2.0 @ Social Business Forum 2012 (p. 18)
- 17. Social Business Dashboard Examples - Innovation Open Knowledge srl â Towards Social Analytics 2.0 @ Social Business Forum 2012 (p. 19)
- 18. Social Business Dashboard Examples - Innovation Open Knowledge srl â Towards Social Analytics 2.0 @ Social Business Forum 2012 (p. 20)
- 19. Social Business Dashboard Examples - Innovation Open Knowledge srl â Towards Social Analytics 2.0 @ Social Business Forum 2012 (p. 21)
- 20. Social Business Dashboard Examples - Innovation Open Knowledge srl â Towards Social Analytics 2.0 @ Social Business Forum 2012 (p. 22)
- 21. Customer Insights Social Analytics 1.0 Social Analytics 2.0 No. of likes, followers, friends Social context from which contact is made No. of brand mentions Diversity of client connections No. posts and comments on Density of community of customer forums connections around brand No. of new on-line clients Nature of community that on- line clients are part of Open Knowledge srl â Towards Social Analytics 2.0 @ Social Business Forum 2012 (p. 23)
- 22. Social Business Dashboard â CRM Example Internal Installers Software Other Wholesalers Open Knowledge srl â Towards Social Analytics 2.0 @ Social Business Forum 2012 (p. 24)
- 23. Social Business Dashboard â CRM Example 2-Way Interaction Internal Installers Software Other Wholesalers Open Knowledge srl â Towards Social Analytics 2.0 @ Social Business Forum 2012 (p. 25)
- 24. Polls âCrowd Seedingâ Social Analytics 1.0 What are our biggest issues? Social Analytics 2.0 Who can we mobilise to address these issues? Open Knowledge srl â Towards Social Analytics 2.0 @ Social Business Forum 2012 (p. 26)
- 25. COME TO OUR BOOTH! June 4th 2012 OPENKNOWLEDGE SRL MILANO ď¸LONDON ď¸SYDNEY ď¸SHANGHAI
Hinweis der Redaktion
- Most people say grass âŚ.this is Newtonian thinking which focuses on decomposition or reductionism to understand complexity.. Grass is the odd one out as its vegetable not animal. Therefore in analysing a social system we decompose the system into its parts i.e. communities, then people. We then look to classify people in the social system by analysing attributes at each level. Eastern thinking takes a more holistic relationship view i.e. cow eats grass so chicken is odd one out.
- The question is how do you identify the âsignalâ inside all the ânoiseâ we are recording? Be it data on the CRM, log files, internal/external communities, âŚItâs not just an issue of managing information, but also to be able to use the right data at the right time.
- The question is what this phenomenon means. Is the proliferation of data simply evidence of an increasingly intrusive world? Or can big data play a useful economic role?
- One of the building blocks of the projects we deploy is Network Analysis. This quote is from Albert Laszlo Barbasi, an Hungarian mathematician who recently contributed in divulgating a methodology thatâs called Network Analysis.
- Networks help us getting and understanding the âbigger pictureâCorporations might look at a graph to verify that marketing and sales are communicating, urban planners to monitor the interconnectedness, or isolation, of neighborhoods, biologists to discover interactions between genes, and network analysts to monitor security.We are surrounded by network!
- Not to mentionthatpeople are connectedtoo
- This is the network of citations in academic papers. At a glance you can see that economics is an âisolateâ discipline, while, for instance, there are some interesting patterns between astronomy, neuroscience, molecular science.
- Companies can be linked tooâŚthis infographic represents connections between tech companies based on either co-membership on the board, acquisitions, or affinity.
- The good news is that this kind of insights and visualizations are now being applied more and more to organizations themselves.If we look at today's modern enterprise, its complexity differs greatly from that of the industrial era. The ecosystem that exists around and within the organization requires a methodological and a refined set of tools to understand how organize the work. If the business process analysis (BPM) is a technique suited to the company of the industrial age, the Social Network Analysis (SNA) is the ideal technique for the social business.
- Relationship thinking takes a more holistic perspective. It builds on Social Analytics 1.0 by adding relationship centre analyses (drawn from SNA). The results provide a more accurate identification of key influencers, opinion leaders etcâŚ
- Influence is about the ability to mobilize action, whether this is a purchase for a strong brand, the adoption of a new idea or instigating co-operative action. In the above case Costello will have the stronger influence, despite having far few followers than Abbott.
- Who is most critical to your organisation? While Abbott has many more connections than Costello, the loss of Costello is likely to do far more damage to the organisation than the loss of Abbott.
- Social Analytics 2.0 importantly collects organisational attributes along with relationship data. In the case above the network patterns may be the same but the organisational attributes would indicate that even a perceived less popular idea will be more attractive to management because of the diversity of support it has received.
- What is the community surrounding those that make contact round a brand. How diverse is the community and how dense. Is it appropriate for our brand i.e. are we growing or consolidating? If we are growing what is the viral potential based on the pattern of the network of the brand community.
- Polls typically provide a Pareto analysis of interest topics or challenges. If we frame our poll around issues or opportunities we can use social analytics 2.0 measures to better target communities to mobilise in support of the given mission (Crowd seeding).