The document discusses the value of measuring conversations to convert them into opportunities by contrasting quantity versus quality. It provides insights into software available for tracking conversations and emphasizes the importance of metadata and algorithms in understanding engagement. Additionally, it highlights the challenge of integrating various software solutions and the potential perils of using 'freeware' that may compromise data ownership.

1. Measuring Conversations Where is the value? Alan Patrick (@freecloud)

2. Agenda How it can help you convert conversations into opportunities • Difference in measuring quantity and quality Should it be governed and controlled through state of the art software, dedicated staff, or a combination? • An overview of the software available for measuring

3. Converting Conversations into Opportunities Its just another Channel...

4. Converting Conversations into Opportunities The same old... but Different Feedback Loop Interlinked, and you are not the “star” They watch you too

5. Quantity v Quality - Where is the value? Revenue Cost Reduction Capex Efficiency Value $$ 1 1 1 1

6. Revenue Market Making Sales Volumes ARPU Increasing Value

7. Operating Costs Churn Labour Costs Attributed Costs Company Specific

8. Capex Utilisation Efficiency Capex Savings Speed of Return

9. Pareto Not all conversations are equal Potential Proclivity Probabilities…..

10. Meme Machine Memes point to: Hot or Not Sentiment Shift Pointillist Patterns

11. Social Capitalists Connect the Dots (From Mat Morrison) Some people are more connected than others This is not the same as being influential Gladwellian vs Wattsian models

12. Measuring the Metadata Metadata Metrics (From Mat Morrison) We live in Tribes We can predict you from your links (friends, activities etc) Metadata predicts data too

13. Algorithms v People Algorithms People People Integrated Systems People Algorithms Variety & Change volume

14. The Software Market Segmentation Online Offline User Data Medium (Blogs, Twitter, Search Engines, Databases) Speed (Real time, most popular, discrete event Sources (eg Feeds, Platforms) Subject (eg a meme across platforms) Trends (eg Buzz, Traffic) Comparisons (eg positional shifts vs competitors) Statistics (First level analysis) Data Sifting (2 nd Level – Pattern Finding) Analyse Track Aggregate Search Identify

15. The Wetware You can go quite a long way on open source / share ware / freely available software Beware “freeware” from providers that then require you to hand over your data Software packages – most don’t integrate to give a total picture System design houses – build and integrate solutions Open source Packages Bespoke middleware MeasurementCamp measurementcamp.wikidot.com Resources and Wiki Case Studies Meetings

16. There’s more on the Blog blog: www.broadstuff.com