predictive modelling
and
benchmarking
with
social media metrics
a
road map for development
Stephen Haggard, Z/Yen Group Li...
over half of marketing budgets
spend <1% on social media Emailvision 2010
social media data framework and weighting
we do not have:
known relationships
standard regressions
organising principles
comparable datasets
we do have:
outcome cri...
“solve wicked problems”
social media
in
professional
communities
avatars
as
interfaces
to
data worlds
risk analysis
outcom...
Support Vector Machine (SVM)
Predicting outcome variables from datasets with complex
multiple and dynamic correlations and...
analysis by hyperplane fit
applications
PropheZy
major gift prediction
(Big Lottery Fund)
straight-through-
processing in dealing
rooms
union branch
...
media applications / buzzdeck by AWAL
/rural network for MTN Zambia
DAPR machine - PropheZy
 best execution trading: compliance analysis
 live demonstration available – contact me
next - data
 Historical data tests
 gauge reliability
 build confidence
 Build model of correlation
 training sets
 ...
next– format ?
 proprietary specialist tool
 industry-standard tool
 data sharing club
next - what is it ?
 benchmark ...
Scenarios in social media - prediction
 Scenario: predictive use.
 Working with the target values for domains given in I...
Scenarios in social media - evaluation
 Scenario: evaluation.
 The client is midway through an SM campaign that
has attr...
Scenarios in social media - analysis
 Scenario: analysis.
 SM campaigns on Platform X are achieving
bookmarking and refe...
thanks
Z/Yen Group Limited www.zyen.com
stephen_haggard@zyen.com
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Iab sm measurement talk slides 19 oct slideshare version

  1. 1. predictive modelling and benchmarking with social media metrics a road map for development Stephen Haggard, Z/Yen Group Limited Presentation to IAB Social Media Council, Research and Measurement Group 19 October 2010
  2. 2. over half of marketing budgets spend <1% on social media Emailvision 2010
  3. 3. social media data framework and weighting
  4. 4. we do not have: known relationships standard regressions organising principles comparable datasets we do have: outcome criteria eg CPR>$x volume of data social media data narrative will be fuzzy
  5. 5. “solve wicked problems” social media in professional communities avatars as interfaces to data worlds risk analysis outcome prediction
  6. 6. Support Vector Machine (SVM) Predicting outcome variables from datasets with complex multiple and dynamic correlations and patchy/noisy data
  7. 7. analysis by hyperplane fit
  8. 8. applications PropheZy major gift prediction (Big Lottery Fund) straight-through- processing in dealing rooms union branch performanceDynamic Anomaly and Pattern Response (DAPR)
  9. 9. media applications / buzzdeck by AWAL
  10. 10. /rural network for MTN Zambia
  11. 11. DAPR machine - PropheZy  best execution trading: compliance analysis  live demonstration available – contact me
  12. 12. next - data  Historical data tests  gauge reliability  build confidence  Build model of correlation  training sets  Harvest ongoing campaign datasets across time  non-numerical is OK eg sentiment index, content tags,  external content OK eg weather, interest rate, other media, Google Analytics
  13. 13. next– format ?  proprietary specialist tool  industry-standard tool  data sharing club next - what is it ?  benchmark for performance  prediction and planning tool  analysis and evaluation tool for insight
  14. 14. Scenarios in social media - prediction  Scenario: predictive use.  Working with the target values for domains given in IAB metrics proposal, the Agency is planning a SM campaign with KPIs: {i(A) Appreciation: CPE < €0.20} and {i(A) Action CPL but {i(A) Awareness n 3}  The DAPR machine looks in the data for instances that yield this outcome and assigns a p=20% probability of success for the input scenario. But the DAPR machine has also established (by benchmarks across the whole community of data) that variables d, g and j are reliable predictors of the target CPE for this product type. So the Agency enters new target values for fields d, g, j and this yields p=50%, allowing the planners to target their resources more smartly.
  15. 15. Scenarios in social media - evaluation  Scenario: evaluation.  The client is midway through an SM campaign that has attracted 275,000 visitors so far (on target) but has yielded only 900 plays of its video, way below target. How bad is this, and should the Agency be fired?  DAPR will spot the extent to which this outcome is anomalous and below/above trend for this point in a campaign of this or any kind. Data points can be tracked in 3 dimensions (time, number, type).
  16. 16. Scenarios in social media - analysis  Scenario: analysis.  SM campaigns on Platform X are achieving bookmarking and referrals at 12 % more per visitor than those on Platform Y. Why ?  DAPR will identify the strongest patterns around bookmarking and referrals both across the entire data pool, and then on Platforms X and Y; the clusters of difference that correlate best with bookmarking and referral scores will be identified for both Platforms X and Y. Histogram analysis shows best predictors of bookmarking referral.
  17. 17. thanks Z/Yen Group Limited www.zyen.com stephen_haggard@zyen.com
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