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Getting the edge/ The Magic of Blended Data презентация
Содержание
- 1. Getting the edge/ The Magic of Blended Data
- 4. @willmcinnes @brandwatch
- 5. @willmcinnes @brandwatch
- 6. Let’s get this straight.
- 7. Simple idea #1 As social moves from
- 8. Centralized Distributed Coordinated Multiple Hub & Spoke Holistic @willmcinnes @brandwatch
- 9. @willmcinnes @brandwatch
- 10. Simple idea #2 That getting an edge
- 11. Caution!
- 12. For every beer and nappies… @willmcinnes
- 13. ...there are 17 spurious correlations. @willmcinnes @brandwatch
- 14. 3 main risks with social data 1.
- 15. Pic of mike
- 16. Raw ingredients Great quality social data you
- 18. 7 stories to Enlightenment
- 19. Be real. These are all real examples. All but one are Brandwatch customers.
- 22. Goal: Blend social data with weather
- 24. Goal: Jump in to conversations about test
- 26. Goal: More effective ad spend and return
- 28. Goal: Change and Adapt Brand Perception How:
- 30. Goal: Understand which brands and items their
- 31. The point is that it’s not just
- 34. What about the plateau of productivity?
- 35. @willmcinnes @brandwatch
- 36. @willmcinnes @brandwatch
- 39. @willmcinnes
- 41. So what?
- 42. @willmcinnes @brandwatch
- 43. Come and say hello
- 44. now you know
@willmcinnes @brandwatch
Слайд 7Simple idea #1
As social moves from a silo to being better
through its connection to everything else in an organisation, so does social data.
Слайд 10Simple idea #2
That getting an edge matters.
And the best way to
get an edge
with social data is to blend it.
Слайд 143 main risks with social data
1. Sample/selection bias
Assuming people on social
are representative of the people you're interested
Assuming the people you're interested in are posting on social
2. Inference problems
Things like sentiment, gender, location, etc. are inferred with less than 100% accuracy
3. Being creepy
Assuming the people you're interested in are posting on social
2. Inference problems
Things like sentiment, gender, location, etc. are inferred with less than 100% accuracy
3. Being creepy
@willmcinnes @brandwatch
Слайд 16Raw ingredients
Great quality social data you can manipulate
Great quality other data
Analyst
or data science resource
Слайд 22Goal:
Blend social data with weather data to find insights
How:
Got
social data for customers talking about consuming their ice cream product using
Got weather data for the same period
Outcome:
Found there were meaningful increases in people talking about eating ice cream when the weather was bad.
Used that to inform their future advertising strategy
Got weather data for the same period
Outcome:
Found there were meaningful increases in people talking about eating ice cream when the weather was bad.
Used that to inform their future advertising strategy
@willmcinnes @brandwatch
Слайд 24Goal:
Jump in to conversations about test drives to signpost potential buyers
to local dealers
How:
Queries set up to locate social mentions that mention car model names with ‘test drive’, dealers names.
Using Rules, Categories and Tags to automatically filter these conversations by Colour, Model, Brand, Dealer etc.
Then matching CRM details of known customers with social handles to explore the potential of social CRM at scale (they already have a database of >1m customers on social).
Outcome:
Increase in car sales from test drives.
How:
Queries set up to locate social mentions that mention car model names with ‘test drive’, dealers names.
Using Rules, Categories and Tags to automatically filter these conversations by Colour, Model, Brand, Dealer etc.
Then matching CRM details of known customers with social handles to explore the potential of social CRM at scale (they already have a database of >1m customers on social).
Outcome:
Increase in car sales from test drives.
Слайд 26Goal:
More effective ad spend and return visits to their parks
How:
Identified
people who met demographic criteria in each of their theme park DMA region.
Identified topical areas of interest in those demographic segments, by region
Fed those topics into tailored regional advertising campaigns
Outcome:
Uplift in ticket sales + increase in per ticket revenues
Identified topical areas of interest in those demographic segments, by region
Fed those topics into tailored regional advertising campaigns
Outcome:
Uplift in ticket sales + increase in per ticket revenues
@willmcinnes @brandwatch
Слайд 28Goal:
Change and Adapt Brand Perception
How:
Matching offline physical event check-in data
with the social conversations around each of the physical events
Matching social handles to offline identities and then observing and learning
Outcome:
New evidence and insight into which events drive the most brand favourability change.
Matching social handles to offline identities and then observing and learning
Outcome:
New evidence and insight into which events drive the most brand favourability change.
@willmcinnes @brandwatch
Слайд 30Goal:
Understand which brands and items their existing customers
were talking about
publicly
How:
Acquired mentions for the key brands that they sell
Worked with a third party vendor to match social identities to their own CRM database
Outcome:
Used information to promote those brands and items via the website and email. ROI ‘made the leaderships’ jaws drop’
How:
Acquired mentions for the key brands that they sell
Worked with a third party vendor to match social identities to their own CRM database
Outcome:
Used information to promote those brands and items via the website and email. ROI ‘made the leaderships’ jaws drop’
@willmcinnes @brandwatch
Слайд 31The point is that it’s not just about social anymore
It’s about
the business
The customers
The market
Social is just part of it
The customers
The market
Social is just part of it
