Laura Ludeña Head of Market Insights Google Spain

*La estadstica aplicada a la comprensin de las necesidades del consumidor: Google Analytics

Google Confidential and Proprietary

La estadstica aplicada a la comprensin de las necesidades del consumidor: True View*Preroll vs. TrueView at equal efficiencySource: GfK (randomised experiment with 18 brands and n = 3.184 respondents)Connected Life Panel Germany 2012 (n = 10.000 panelists)2.8x

Grfico1

0.01930.054

Preroll

TrueView

Sheet1

PrerollTrueView

Uplift on TOM Awareness(per paid view)2%5%279.7927461139900%


La estadstica aplicada a la comprensin de las necesidades del consumidor: Retargeting*


*Herramientas pblicas: Google Trends


*DIETAHerramientas pblicas: Google Trends


*DUKANHerramientas pblicas: Google Trends


*-DUKANHerramientas pblicas: Google Trends


Herramientas pblicas: Flu Trends


Herramientas pblicas: Bank of England


*Conclusiones

1La libre eleccin de informacin ha cambiado el modo en el que el consumidor est dispuesto a acceder al contenido publicitario.2La estadstica tiene un papel clave para conectar las necesidades del consumidor al contenido y formato publicitario que est dispuesto a aceptar y valorar en ltima instancia. 3Existen herramientas pblicas accesibles para todos que permiten entender y optimizar nuestra oferta, y ejemplos que nos inspiran al uso de la estadstica para mejorar la gestin de mltiples productos y servicios.


Uso de la estadstica para medir la efectividad del marketing*


La escalabilidad es clave


*El consenso tambin


Tres dimensiones fundamentales


Fusion process is based on similarities among Kantar Media (TV) and Kantar Worldpanel (online) panelists. Twins are created based on sociodemographics, attitudes, TV chanel profile, day band profile (multidimensional reduction) and 200 additional exclusive clusters.*Reach: via fusin de datos fija


Only YouTube added an average of 2.9 extra reach points to TVSource: Kantar Worldpanel cross media analysis internet users Base: 10 YouTube campaigns in 2012/2013Overlap TV + YouTube 11.4%Only TV67.7%Exclusive Youtube reach:+2.9 p%Average TV reach (1+)79.1%

Average YT reach (1+)14.3%*Reach: via fusin de datos fija


More than 20% of total YouTube reach is not doubling up with TV . YouTube share of incremental reach goes higher while looking at effective TV reach*Media Reach by different levels of TV OTS (%) 20.2%55.9%YouTube share of incremental reach (100= Total YouTube reach)41.2%Source: Kantar Worldpanel cross media analysis internet users Base: 10 YouTube campaigns in 2012/2013Reach: via fusin de datos fija

Grfico1

67.740532392211.36010548672.8977276818

50.90354527798.45.9315399458

37.3733767346.37.9916295828

TV

TV+Youtube overlap

Youtube

Sheet1

TVTV+Youtube overlapYoutube

1 OTS TV67.711.42.9

+3 OTS TV50.98.45.9

+5 OTS TV37.46.38.0100


WEB media is particularly effective at delivering additional GRPs among lower TV exposed audienceAlmost 75% of online GRPs delivered out of high TV exposedSource: Kantar Worldpanel cross media analysis internet users Base: 12 campaigns in 2012/2013*Reach: via fusin de datos fija

Grfico1

100100100

64.42726.7

26.225.723.7

9.427.126.3

020.223.2

TV campaign

Total Web (YouTube&GDN)

Only YouTube

GRPs distribution by different levels of TV exposure (%)

Sheet1

TV campaignTotal Web (YouTube&GDN)Only YouTube

Total100100100

High TV exposed64.427.026.7

Medium TV exposed26.225.723.7

Low TV exposed9.427.126.3

Non TV0.020.223.2

To resize chart data range, drag lower right corner of range.


*What we did:Analyzed 80 TV campaigns for different products of leading advertisers TV media plans by Ebiquity, YouTube media plans simulated by GoogleData source: Single-source panel: Measurement of individual TV & online behavior from same consumers in 5,000 households within GfK Media Efficiency Panel

Reach: via Single Source Panel


For illustration only*reallocation of wasted TV OTS to unexposed audiencesReach: via Single Source Panel

Grfico1

Not exposedNot exposed

0.5low frequency

1medium frequency

10.6

OTS

OTS "wastage"

Sheet1

OTSOTS "wastage"desc4desc5desc6

Not exposed15.0%15.0%15.0%

low frequency50.0%15.0%15.0%15.0%

medium frequency100.0%15.0%15.0%15.0%

high frequency100.0%60.0%15.0%15.0%15.0%

text515.0%15.0%15.0%15.0%15.0%


Benefits of Share-Shift from a Budget-Point of View *Source: Share shift analysis Google, based on GfK MEP and Ebiquity dataTV + YOUTUBETV ONLY NET REACH: 69%NET REACH: 69%COSTS COSTS Share Shift Stable Reach Reach: via Single Source Panel


*Brand Impact: ejemplo preroll vs skippableLooking at recall, awareness and recognition no major differences in impact can be seen between the two formats. If any, prerolls perform sligthly stronger. indicates differences at 90% significance level to controlSource: GfK / nurago 2011, n = 1.023 interviewss. Audi and Redbull AdsNurago Impact Panel Germany (n = 10.000 panelists)% of respondents per cell

Grfico1

0.20002932550.29702510070.2817699185

0.28483870970.32625116210.3177485247

0.16098729230.40008134490.350470702

NOT exposed

Exposed to PREROLL

Exposed to SKIPPABLE

Sheet1

NOT exposedExposed to PREROLLExposed to SKIPPABLENOT exposedExposed to PREROLLExposed to SKIPPABLE

aidedAD RECALL20.0%29.7%28.2%24.2%30.0%25.6%

unaided brandAWARENESS28.5%32.6%31.8%31.9%38.6%38.4%

advertisingRECOGNITION16.1%40.0%35.0%52.1%56.1%53.8%

59.4%62.3%62.2%


Brand Impact: randomized experimentsIMPACT PANEL (10.000 panelists, used for ad effectiveness research). Unique methodology: nurago technology allows experimental study designs for ad effectiveness in a real live campaign settingFull control over reach and frequency of campaign deliveryExperiment embedded into the panelists natural online usageCampaigns can be fully simulated or boosted if needed within the panelPanelists (representative for onliners 18-69 years)Random pre campaign assignment into test and control groups, e.g. # only allowed to see skippable # only allowed to see prerollPanelists fall into groups depending on their natural online behaviour # did not see advertising# exposed to skippable# exposed to prerollSurvey


*Brand Impact: eficiencia preroll vs skippableHowever, in terms of efficiency (impact per GRP) skippable prerolls clearly outperform standard prerolls by factor 2.4 on average across brand metricsUplift to control per GRP Efficiency Index 248%Efficiency Index 234%Efficiency Index 234%Source: GfK / nurago 2011, n = 1.023 interviewss. Audi and Redbull AdsNurago Impact Panel Germany (n = 10.000 panelists)

Grfico1

0.00016762010.0004164761

0.00007156560.0001676786

0.00041318260.000965436

STANDARD Preroll

SKIPPABLE Preroll

Sheet1

STANDARD PrerollSKIPPABLE PrerollNOT exposedExposed to PREROLLExposed to SKIPPABLE

aidedAD RECALL0.01676%0.04165%24.2%30.0%25.6%

unaided brandAWARENESS0.007157%0.016768%31.9%38.6%38.4%

advertisingRECOGNITION0.041318%0.096544%52.1%56.1%53.8%

59.4%62.3%62.2%


Ventas: Economtricos online to store


4. Final check: the blue line is the reality (sales); the red line is the model (how it fits the sales) 1. Need to collect all the variables that may influence the sales2. An analyst uses judgement and SAS to produce different regresion models until best combination is identified3. So he is able to identify each variable influence to the salesVentas: Economtricos online to store


12% of visit to dealer are influenced by media. Other MediaDsplayVentas: Economtricos online to store

Grfico1

0.040.110.120.72Series 1

Magazines

Outdoor

TV F500

Web visits

Magazines 0.5%

Outdoor 1,4%

TV 1,5%

Web visits 8,9%

Sheet1

Series 1

Magazines4%

Outdoor11%

TV F50012%

Web visits72%

To update the chart, enter data into this table. The data is automatically saved in the chart.


Paid search is the most cost-effective.Monetary value of an application and actual returns not shown due to client confidentiality ROI data indexed *Offline ROIOnline ROIROI: Visits to dealer by 1 investedOther MediaDisplayVentas: Economtricos online to store


*Historical Media Mix*Improved Modelled Media MixVentas: Economtricos online to store


*Conclusiones

1La integracin de la medicin de audiencias y coberturas desde mltiples dispositivos y plataformas es el principal reto actual en la medicin de efectividad publicitaria.2El desarrollo de las nuevas tecnologas en la emisin de contenido publicitario permite la optimizacin y escalabilidad de la medicin del impacto publicitario en notoriedad e imagen de marca. 3Es fundamental explorar la medicin del ROI de las diferentes actividades de marketing de un modo integrado y comparable entre ellas. La creacin de modelos economtricos es una va slida aunque costosa en tiempo, dinero e infraestructuras de gestin de la informacin interna.


Preguntas?*

Consumidor actualCobertura y frecBrandingventas************Header: Relation2004-02-04Internal/Identier/File name**Nuevos modos de presentacin y distribucin de la informacinLa informacin que no se procesa no es efectiva: y los informes no se procesan. Los hbitos de lectura han cambiado. Los informes han de ser comprensibles, personalizables, intuitivos, usables, significativos,

******In France of those 35% who believe Google makes money by selling information on users, over half (51%), still rate Google as trustworthySubstantially less concern about Google selling personal data in ItalyEsto es el impacto global actualmente: internet llega a todos**Real time insights find the predictive query set you dont have to wait for a big econometric model update to get the pulse of changing consumer demand trends.***Esta es la situacin a la que aspiramos*This share-shift analysis is based upon a decent data base (80 TV campaigns) to find out the effect of adding online (YouTube in this case) to a TV-only campaign. Data comes from individual contacts with TV and YouTube from German Media Efficiency Panel. Analysis will be repeated with a larger sample to allow for more granular results. **The second analysis scenario takes out TV and adds YouTube to a media plan while keeping overall net reach stable. On average for 80 campaigns, the overall campaign costs can be reduced by share-shift by 7.4%.

****https://sites.google.com/a/google.com/mi-italy/econometrics/ikea

***

Documents

Laura Ludeña Head of Market Insights Google Spain