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The new face of search

Data, audience and the programmatic

marketer

Why We Need a Cross-Device Solution

ALESSANDRA ALARI, GOOGLE

Understanding Performance Used to be Easy in 2007

Moment Search Conversion Report

$

~10 Years Later

Example Forecast

Desktop

2012 2013 2014 2015 2016

Mobile

Sear

ch Q

uerie

s

Mobile Moment

Tablet

2017

Mobile Queries Overtook Desktop Queries Globally in 2015

Across millions of websites using Google Analytics today, we're seeing more than half of all web traffic now coming from smartphones and tablets

Google Analytics Data, US, Q1 2016

Quarter of Year

Conv

ersio

n Ra

teDesktop

Mobile

Tablet

Source: Monetate Ecommerce Quarterly - http://www.monetate.com/resources/research/

On Paper, Mobile Seems to be Underperforming

Nearly

80% of online users use multiple devices

Source: Google / Ipsos Connect, March 2016, GPS Omnibus, n=2,013 US online respondents 18+

Today’s customer journeys are much more fragmented

Moment Search Conversion Report

Understanding Performance in a Multi-Device World is Complicated

© Google Inc. 2016. All rights reserved.

User clicks on Retailer’s desktop ad

Later orders flowers on his

tablet

Web to Mobile Browser to Browser

Buys three pairs of running shoes

on Retailer’s Mobile site

User clicks on Mother’s

Day flowers ad on his laptop in the office

What’s a Cross Device Conversion?

3

How can we capture

multi-screen behaviour?

Moving Beyond Cookies

Industry Options Available

Algos based on non-PII public info (e.g., device

type, IP address) to approximate device links;

does not create persistent ID

Statistical Linkingor Probabilistic

Uses identifiers that a user cannot control or reset - e.g., fonts, screen resolutions - to create a persistent, unique ID for

that device

FingerprintingTrack users who log-in to

a service on multiple devices

User Log-In or Deterministic

Benefits of the Google Solution

Tracking that Works Across the Entire Google Ecosystem

SCALE METHODOLOGY BREADTH

7x 1B+ Users

iOS

Attribution 360

Measurement Today

Attribution

Cross Device

Last Click

No. In Reality, We Are Here:

6% Our current solution provides an adequate single view of our customers

47% We have a solution in place but it still has gaps in coverage of devices, touch-points and data

32% We have not yet implemented a solution to achieve a single view of our customers

Source: Signal, “Preparing for Cross-Channel Success: Solving the Identity Puzzle” , March 2015

19% We have implemented a solution but its shortcomings severely limit its practical effectiveness

Cross-Device is Not For

Everyone

Photobox proves the value of mobile search ad campaigns through geo experiment

5x Incremental ROI driven by mobile

“Now we have a view of the holistic performance of

mobile search advertising on our overall sales numbers, this

has enabled us to more effectively attribute the spend

to sales.” James Duggan, Head of Digital Marketing, PhotoBox

UK

Proprietary + Confidential

Made.com used Adwords cross-device reports to gain insight into the devices their customers use to search, browse and buy

+204% YoY Increase

in mobile conversions

+21% Increase in

overall conversions

+105%

Increase in mobile ROAS

The Bright Future

9:34 AM

contentbazaar.co/20160314/pi-way

9:34 AM

contentbazaar.co/20160314/pi-way

Slow Loading

ContentShifting

Non-ResponsiveContent

9:34 AM

contentbazaar.co/20160314/pi-way

Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.

A Better Mobile Web

Attribution

PRECISION

SCAL

ABIL

ITY

Rules-based

Attribution

Data-driven Attribution

Controlled Experiments

Freq

uent

One

-off

Correlational Causal

In Summary

Use Insights to Value Mobile

Go from Last Click toCross Device

Accept the Complexity

Thank You!alari@google.com

Personalising your customer journey

Chris Rowett, Director of Performance, EpiphanyMitch Vidler, Head of Digital Analysis, Jaywing

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The path to personalisation

Single Customer

View

Audience Segmentation

Tracking & Rules

Personalisation

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It’s Hard.

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Single customer view

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HT

JAYW

ING

PLC

2015

Customer interactionsCustomer interactions

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Audiences – granular segmentation

Researching Their First Pet

First Time Pet Purchaser

Long Term Pet Owner

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Feed information into rulesMitch & Chris can share the same audience, but still need individual creative

Mitch Chris

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The challenge

Static Customer

DataName

AddressDOB

Transactional historyLoyalty data

Dynamic Behavioural

DataOnline data

Browsing behaviour Clicks & views

Web trafficeCRM analytics

Hidden away in products such as GA

Visible but not useableWhat to capture? What

to do with it?How to link with current

CRM activity?

Used to create a Single Customer View

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The challenge

Static Customer

DataName

AddressDOB

Transactional historyLoyalty data

Dynamic Behavioural

DataOnline data

Browsing behaviour Clicks & views

Web trafficeCRM analytics

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• Media Placement Found and Purchased

• Blank framework calls the rules engine

• Information from the Single Customer View is loaded

• Personalised creative is served

Level 1 Level 2

Integrate a personalised rule set

Media Partner

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Display personalisation

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Hyper-personalised email

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Onsite content personalisationAs with media creative, your own website can be personalised to suit the user, increasing engagement and conversion.

A good Conversion Rate Optimisation program can feed into site personalisation

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Better Product Recommendations

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Existing approaches

Rules-based Attribute Modelling

Collaborative Filtering

Simple rules-based approach where data

volume and complexity is low

Incorporates customer demographic information and

product featuresLimited incorporation of

purchase history Does not scale well to large

product inventories Does not identify patterns in

purchase behaviour

Scales to large product inventories

Identifies patterns in purchase behaviour

Does not incorporate demographic information or

product features (suffers from “colds starts”)

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Better predictions at all stages

Attribute ModellingCollaborative FilteringJaywing Approach

Customer / Product Lifecycle

Pred

ictive

Powe

r

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AlmanacAlmanac can bring together disparate data sources and feed this into an enhanced Single Customer View

Mobile app (store visit)

Personalised display/

retargetingPersonalised

emailTargeted content

Enhanced Single Customer View

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A virtuous circle of activity

Richer Data

Better Recommendations

Putting optimal product recommendations in front of customers drives more sales

An increased propensity to buy builds deeper relationships with customers, who move more of their buying to youDeeper customer relationships

generate more useful data on purchase patterns and behaviours

Our advanced algorithm uses the richer data to further improve recommendations

More Purchases

Deeper Relationships

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Many new customers but also loyal existing customers

KWD First Visitor79%

KWD Return Visi-tor

21%

KWD Visitor Type

KWD First Visitor KWD Return Visitor

2 Visits21%

3-4 Vis-its

25%

5+ Visits54%

Repeat KWD Visitor Breakdown

2 Visits3-5 Visits5+ Visits

Personalisation also presents the option to down weight spend on existing loyal customers

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Optimise campaigns for profit

SKU-level Product Profitability Data

Profit Optimised Campaigns

Granular SKU-level Product Recommendations

Product 1

Product 2

Product 3

Product 4

Product 1

Product 2

Product 3

Product 4

Product 1

Product 2

Product 3

Product 4

Confidential & ProprietaryConfidential & Proprietary

Search in 2016The Audience RevolutionOli Petas, UK Search Audience & Automation Lead

7x Increase in Click Through Rate

7x Increase in Conversions

Return of £6 for every £1 invested

Goals Approach ResultsGoals Approach Results

Using On-Site DataI Clarks

“Search retargeting gives the consumer a much more personalised experience.” John Ashton, Head of Multi-Channel Retail, Clarks

Increase the impact of web campaigns

Get more in-depth analysis of customer activity online

Improve the conversion rate

Placed Remarketing Lists for Search Ads (RLSA) tags on every page of the Clarks website

Analysed the behaviour of website visitors

Tailored keywords, creative and bids for individual users

Full Case Study | Video

13% Lower Cost per Acquisition

52% Higher Click Conversion Rate

12% More Revenue per Conversion

32% Higher Click Through Rate

Goals Approach ResultsGoals Approach Results

Connecting CRM Data through Customer Match I Pegasus Airlines

“Customer Match empowered us to tailor our messages to both our clients and prospective clients. The strategy has increased our revenues and decreased cost per sale.” Didem Namver, Digital Marketing Manager, Pegasus Airlines

Decrease cost per ticket sale

Increase traffic

Take advantage of new optimisation opportunities

Implemented Customer Match in Adwords

Automatically adjusted bids and messaging to connect with high-value customers

Full Case Study

Maximising Marketing Impact

Rohan Gifford, Research Manager, Google

Across 56 sales correlation studies in EMEA YouTube had

a Return on Investment (ROI) higher than TV in 43

(77% of total) studies

I Studies spanned countries and verticals

AutomotiveClassifieds & LocalConsumer Packaged GoodsMedia & EntertainmentRetailTechnology

I Three methodologies included

Media mix models

Consumer scan panel studies

Geo-tests

Across 17 studies where optimisation was undertaken the recommendation was to

increase YouTube spend in 100% of cases

In 14/17 optimisations (82%) the recommendation was to more than double

YouTube spend

YouTubeRe

venu

e

OptimalCurren

t

Spend

Making the Future Happen7 Evidence Based Tips

Rohan Gifford, Research Manager, Google

I

TrueView Skippable ads on YouTube

View Through Rate % of TrueView ads that are not skipped

Brand Lift brand lift experiments built into YouTube

Three terms to know

1.FrequencyFirst impressions count most, but brand lift builds with frequency up to 6 a week

Top of Mind Ad Recall

Control 1 2 3 4 5 6 7 8 9 10

0%

3%

Frequency

Source: Google/eye square research 2015/2016.

2.DecayReach people regularly across the campaign

3.DeviceMobile delivers even higher brand impact: plan mobile first

4.Contentget higher brand lift next to the most engaging content

Ad Recall Lift increases with increasing Preference Score

Source: Internal Google Data, based on 5,500 TrueView Brand Lift studies which ran between April and October 2015. Confidence interval represents 95% CI

5.Targetingaffinity targeting drives better results

6.CreativeData is creative friend

7.YouTube and TVAND not OR