The Future of Community Insights
•Download as PPTX, PDF•
0 likes•416 views
Online Communities are changing. They're getting larger, sprawling and multifaceted. At the same time, they're also getting smaller, and more specific. They're become more diverse, interconnected, multiplatform - and the tools needed to interact with, find and manage these communities are having to change with them. We present three fundamentally important technologies: Machine Learning (A.I.), Network Analysis and Text Mining - that we believe will underpin the future of community insight. What they are, why they are important, and how you can use them to bring your brand and its community closer together.
Recommended
More Related Content
Viewers also liked
Viewers also liked (9)
Similar to The Future of Community Insights
Similar to The Future of Community Insights (20)
Recently uploaded
Recently uploaded (20)
The Future of Community Insights
- 1. The future of Community Insights Michael Conroy Senior Analyst, Tempero @mickyconroy
- 2. So who am I then? Mick Conroy Senior Social Media Analyst Tempero
- 4. Let’s get started! 1. Network Analysis 2. Machine Learning (A.I.) 3. Natural Language Processing
- 5. CM and Insights together Community Mapping (Networks) Conversation Analysis (Text Mining) Community Engagement Community Outreach (M. Learning) Community Insights Community Management
- 8. Thinking in “networks” Social networks are networks. Duh. If we start analysing social media like networks new and useful insights arise
- 9. This is the New York Times
- 10. ...and this is Forbes
- 11. Positive Negative Neutral Fans @mention Barlow their love of the new track “Barlow can stick those Percy Pigs...” gets RTs Behind the scenes photos shared Barlow like “slashing the Mona Lisa with a platinum machete”. Conversation Analysis
- 12. The power of networks Influencers can be found by their place in the network Spread content in a way that it gets seen
- 13. The power of networks Virality can be measured Going viral can be induced
- 14. Going viral is a science
- 15. Going viral is a science Find the influencers that connect different communities Subcommunities: Find any interests groups within your community to determine how to seed your content
- 16. The tools mappr
- 17. Start with a tight community
- 19. 10% is enough
- 20. Be relevant Your message should be closely related to the purpose that brings the target network together Acknowledge the group’s mindset by bringing in some novelty
- 21. London’s Social Media Map mappr
- 22. UK Fashion and Lifestyle Luxury Fashion Brands Home and Decor Magazine/ Design companies Art auction houses , publications and a museum Luxury Travel Blogs, sites Luxury watch brands, blogs mappr
- 23. Summary Network analysis helps us find the people and conversations that matter, and take the right actions By knowing the structure of the network, you can better model how to influence it
- 24. Machine Learning
- 25. This is the Internet
- 26. You look at this much
- 27. Intro to Machine Learning Train artificial intelligence on a big dataset AI makes a prediction based on what it currently “knows” AI recalculates what it “knows” based on the new information Get feedback on whether the prediction was right or wrong
- 28. It’s everywhere!
- 29. It’s also handy for Social Social media monitoring 2.0 AI can be trained to look for concepts or themes in text, independent of keywords Human level intelligence – at scale
- 31. A few tagging examples
- 32. The new way Train the AI around a bespoke classification taxonomy One trained, the AI is able to tag mentions autonomously Keep human experts on hand for minor corrections
- 33. Sample Taxonomy Message-type News Technical Query Comparison Automated share Advert Contest Opinion Product Fault Customer Service Product Characteristics Price Ease-of-use Compatibility design/build Availability Energy Efficiency Person Customer Rival customer Press Public Staff/Sony Product feature sound set up internal software apps Screen Size Resolution/Picture Quality Media Playback Mobile/tablet/PC connectivity Brand Sony Bravia Samsung LG Sharp Product-type 3D Smart/Internet TV LED TV Plasma
- 34. The end result Sony Samsung Apple Customer Service Sentiment by Brand Worldwide
- 35. The end result Purchase Intent by Competitor 0 5000 10000 15000 20000 25000 28-Aug 29-Aug 30-Aug 31-Aug 01-Sep 02-Sep 03-Sep 04-Sep 05-Sep Sony Samsung Panasonic
- 36. Deep insights, at a glance
- 37. Summary Human-level intelligence at scale Deep insights; Fast. The basis for a laser-targeted engagement strategy
- 39. We’re all drowning in text!
- 40. CM’s have their fair share Twitter status updates Facebook comments …there’s interaction data everywhere
- 41. It’s not just “us”…
- 46. How Overview works Activists use drones to track endangered wildlife Apple rejects iPhone App
- 50. Football fans at a match
- 51. Summary A new toolset is needed to analyse the sheer volume of conversation we all have to deal with Natural Language Processing algorithms are an ideal way to find meaning in the clutter
- 52. In closing...
- 53. Pulling it all together Community Mapping (Networks) Conversation Analysis (Text Mining) Community Engagement Community Outreach (M. Learning) Community Insights Community Management
- 54. There’s a lot more to it
- 55. We can help!
- 56. Michael Conroy Senior Analyst, Tempero @mickyconroy