predicting(:) the future of tracking - insight innovation · 2014-12-17 · turnaround . 4.21 base...

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© TNS 2014 Predicting(:) the future of tracking [email protected] [email protected]

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Predicting(:) the future of tracking [email protected] [email protected]

1 The trouble with tracking

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The trouble with tracking

Poor links to what people actually do, or business performance

Declining response rates and poor quality data

Insights not actionable (or acted on)

Flat-lined metrics

Long, vague surveys that serve too many masters

Far from real-time turnaround

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The client response

Inertia Scale back

Seek alternative

Cancel entirely

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Social channels – evolution, fragmentation

1999 2003 2004 Today

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2000 2014

Social Listening solutions history

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Full range of opinions unfiltered

Sensitive to events “Real-time”

Social media data tracking—an attractive alternative?

Lower cost?

LIVE!

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For many, the answer has been “maybe”—finding the signal in the noise has been a challenge

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Out of the box – it doesn’t work

buzz equity

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Some people have questioned the use of social media data because they don’t ‘represent’ the total population…

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The real issue, though, in the present context, is not ‘representativeness’… …It is predictability.

2 Getting social

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Can we model Survey KPIs from social media data?

From asking …to listening

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Brand health

Yup Alfa Romeo

Audi

B.M.W.

Chevrolet

Citroen

Fiat

Ford

Honda

Hyundai

Kia

Lexus

Mazda

Mercedes

Nissan

Peugeot

Renault

Skoda

Toyota

Vauxhall

Volks-wagen

Volvo

Survey Modelled

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4.0 5.0 6.0 7.0 8.0 9.0

10.0 11.0 12.0 13.0

Actual Predicted

Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug

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Brand health

Success across a number of categories

Automotive Brand

CPG – Short Purchase Cycle

Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct

2012 2013

2012 2013

1.7 1.8 1.9 2.0 2.1 2.2 2.3 2.4

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Modeled data shows signs of being predictive

Week…

Lead time to get the jump on competition

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10 10

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Modelled data from engaged ‘leading indicator’ group indicates your brand health will be “10” in X weeks.

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Survey responses amongst mass market measure your brand health to be “10”.

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The results of your survey based tracker tell you your brand health was “10” three weeks ago.

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We can predict results after the period we used to build the model. n  4 and 8 week predictions have been

very accurate

n  12 week projections only slightly worse

Brand S: 4/8/12-Week Forecasts

What do we mean by predictive

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4 8 12

Observed BHI Predicted BHI

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Taking it further: socialising MMM

Business actions

Contextual situation

Casual factors

External influencers

What consumers Buy

Purchase consumption

Business outcomes

What consumers think

What consumers say

What consumers do online

Intermediary variables

Social media data

Brand tracking

Online behaviors

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Taking it further—modelling sales R2 = 70%

Example sales model - FMCG

$4,000  

$5,000  

$6,000  

$7,000  

$8,000  

$9,000  

$10,000  

$11,000  

$12,000  

1   4   7   10   13   16   19   22   25   28   31   34   37   40   43   46   49   52   55   58   61   64   67   70   73   76   79   82   85   88   91   94   97  100  103  106  109  112  115  Week  

Observed Predicted

Drivers Inertia Health Own Spend Competitor Spend Distribution Promotion Positive Buzz Negative Buzz

*Using marketing and Twitter inputs

3 Reinventing tracking

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The new tracking model

Predictive spine

Slimmer survey

Modular deep dives

Situational equity

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How else we can leverage social data?

With Twitter, ability to look at particular cohorts or audiences and understand their opinions specifically

! …

Social campaign evaluation - who are you reaching and what is the impact?

New product launches - get an early read on market reactions.

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No more rear view mirror: let’s look to the future

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Bob Burgoyne, TNS [email protected]

Ryan France, TNS [email protected]