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

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 criteria

eg CPR>$x

volume of data

social media data narrative will be fuzzy

“solve wicked problems”

social media

in

professional

communities

avatars

as

interfaces

to

data worlds

risk analysisoutcome prediction

Support Vector Machine (SVM)

Predicting outcome variables from datasets with complex

multiple and dynamic correlations and patchy/noisy data

analysis by hyperplane fit

applications

PropheZy

major gift prediction

(Big Lottery Fund)

straight-through-

processing in dealing

rooms

union branch

performanceDynamic Anomaly and Pattern Response

(DAPR)

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

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

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

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.

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).

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.

thanks

Z/Yen Group Limited www.zyen.com

stephen_haggard@zyen.com