1. System of Topics
1.1. semantic technology to suggest the topics to the chatter message author
1.2. identify people mentioned
1.3. graph of topics
1.4. recommendations
1.4.1. of
1.4.1.1. groups
1.4.1.2. topics
1.4.1.3. ppl
1.5. trending topics
1.6. auto-suggested in search
1.7. topics also decorate objects, enabling aggregation of data
2. chatter
2.1. recommendations
2.1.1. on
2.1.1.1. topics
2.1.1.2. files
2.2. topics
2.2.1. makes sens of the feed & make it referencable
2.3. influence & expertise
2.3.1. use signals to identify influence & expertise
2.3.1.1. mentions
2.3.1.2. likes
2.3.1.3. endorsements
2.3.2. identify subject matter experts
2.3.2.1. "Knowledgable People"
3. social intelligence
3.1. 3 problems
3.1.1. content & conversation separated from business process
3.1.2. disconnected customers, partners & employees
3.1.3. expertise & ideas are buried
3.2. definition
3.2.1. art of harnessing the vehavior & social graph of every user to adaptively deliver the right content in right context
3.2.2. examples
3.2.2.1. amazon recommendations
3.2.2.1.1. 35% of revenues comes from that
3.2.2.2. pandora
3.2.2.2.1. ppl whol like this also like this
3.2.2.3. social networks
3.2.2.3.1. facebook using social graph
3.2.2.3.2. twitter using interest graph
4. Jon Pappas, sr pm social enterprise
4.1. @sfdcjp
5. Case studies
5.1. Maxim Integrated
5.1.1. 9300 employees, very smart ppl
5.1.2. don't know much on them
5.1.3. silo'd business units
5.1.4. geographically dispersed
5.1.4.1. 40 design centers
5.1.5. knowledge ("campfire knowledge") will be lost when people retire
5.1.6. @rlacis
5.2. Allianz global investors
5.2.1. @sberexa