1. COPYRIGHTJAYWINGPLC2015

2. COPYRIGHTJAYWINGPLC2015
The new face of
search
Data, audience and the
programmatic marketer

3. Why We Need a Cross-
Device Solution
ALESSANDRA ALARI, GOOGLE

4. Understanding Performance Used to be Easy in 2007
Moment Search Conversion Report
$

5. ~10 Years Later

6. Example Forecast
Desktop
2012 2013 2014 2015 2016
Mobile
SearchQueries
Mobile Moment
Tablet
2017
Mobile Queries Overtook Desktop Queries Globally in 2015

7. 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

8. Quarter of Year
ConversionRate
Desktop
Mobile
Tablet
Source: Monetate Ecommerce Quarterly - http://www.monetate.com/resources/research/
On Paper, Mobile Seems to be Underperforming

9. Nearly
80%
of online users
use multiple devices
Source: Google / Ipsos Connect, March 2016, GPS Omnibus, n=2,013 US online respondents 18+

10. Today’s customer journeys are much more fragmented
Moment Search Conversion Report
Understanding Performance in a Multi-Device World is Complicated

11. © 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

12. How can we
capture
multi-screen
behaviour?

13. Moving Beyond Cookies

14. 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 Linking
or Probabilistic
Uses identifiers that a user
cannot control or reset -
e.g., fonts, screen resolutions
- to create a persistent,
unique ID for that device
Fingerprinting
Track users who log-in to a
service on multiple devices
User Log-In
or Deterministic

15. Benefits of the Google Solution
Tracking that Works Across the Entire Google Ecosystem
SCALE METHODOLOGY BREADTH
7x 1B+ Users
iOS
Attribution
360

16. Measurement
Today

17. Attribution
Cross Device
Last Click

18. 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

19. Cross-Device is
Not For Everyone

20. 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

21. 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

22. The Bright
Future

23. 9:34 AM
contentbazaar.co/20160314/pi-way
9:34 AM
contentbazaar.co/20160314/pi-way
Slow
Loading
Content
Shifting
Non-Responsive
Content
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
A Better Mobile Web

24. Attribution
PRECISION
SCALABILITY
Rules-based
Attribution
Data-driven
Attribution
Controlled
Experiments
FrequentOne-off
Correlational Causal

25. In Summary
Use Insights
to Value
Mobile
Go from Last
Click to
Cross Device
Accept the
Complexity

26. Thank You!
alari@google.com

28. Personalising your
customer journey
Chris Rowett, Director of Performance, Epiphany
Mitch Vidler, Head of Digital Analysis, Jaywing

29. COPYRIGHTJAYWINGPLC2015
The path to personalisation
Single
Customer
View
Audience
Segmentation
Tracking &
Rules
Personalisation

30. COPYRIGHTJAYWINGPLC2016
It’s Hard.

31. COPYRIGHTJAYWINGPLC2016
Single customer view

32. COPYRIGHTJAYWINGPLC2015
Customer interactionsCustomer interactions

33. COPYRIGHTJAYWINGPLC2016
Audiences – granular segmentation
Researching
Their First Pet
First Time
Pet Purchaser
Long Term
Pet Owner

34. COPYRIGHTJAYWINGPLC2016
Feed information into rules
Mitch & Chris can share the same audience, but still need individual creative
Mitch Chris

35. COPYRIGHTJAYWINGPLC2016
The challenge
Static
Customer
Data
Name
Address
DOB
Transactionalhistory
Loyaltydata
Dynamic
Behavioural
Data
Onlinedata
Browsingbehaviour
Clicks & views
Webtraffic
eCRManalytics
Hiddenawayin
productssuchasGA
Visiblebutnotuseable
Whattocapture?What
to dowithit?
Howtolinkwithcurrent
CRMactivity?
Usedtocreatea
SingleCustomerView

36. COPYRIGHTJAYWINGPLC2016
The challenge
Static
Customer
Data
Name
Address
DOB
Transactionalhistory
Loyaltydata
Dynamic
Behavioural
Data
Onlinedata
Browsingbehaviour
Clicks & views
Webtraffic
eCRManalytics

37. COPYRIGHTJAYWINGPLC2016
• Media Placement Found and
Purchased
• Blank framework calls the rules
engine
• Informationfrom the Single
CustomerView is loaded
• Personalisedcreative is served
Level 1 Level 2
Integrate a personalised rule set
Media Partner

38. COPYRIGHTJAYWINGPLC2016
Display personalisation

39. COPYRIGHTJAYWINGPLC2016
Hyper-personalised email

40. COPYRIGHTJAYWINGPLC2016
Onsite content personalisation
As 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

41. COPYRIGHTJAYWINGPLC2016
Better Product
Recommendations

42. COPYRIGHTJAYWINGPLC2015
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 features
Limited 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”)

43. COPYRIGHTJAYWINGPLC2016
Better predictions at all stages
Attribute Modelling
Collaborative Filtering
JaywingApproach
Customer/ Product Lifecycle
PredictivePower

44. COPYRIGHTJAYWINGPLC2015
Almanac
Almanac can bring together disparate data sources and feed this into an enhanced Single
Customer View
Mobile app
(storevisit)
Personalised
display/
retargeting
Personalised
email
Targeted content
Enhanced Single
Customer View

45. COPYRIGHTJAYWINGPLC2016
A virtuous circle of activity
RicherData
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

46. COPYRIGHTJAYWINGPLC2016
Many new customers but also loyal existing customers
KWD First
Visitor
79%
KWD
Return
Visitor
21%
KWD Visitor Type
KWD First Visitor KWD Return Visitor
2 Visits
21%
3-4
Visits
25%
5+
Visits
54%
Repeat KWD Visitor Breakdown
2 Visits
3-5 Visits
5+ Visits
Personalisation also presents the option to down weight spend on
existing loyal customers

47. COPYRIGHTJAYWINGPLC2016
Optimise campaigns for profit
SKU-levelProduct
ProfitabilityData
ProfitOptimised
Campaigns
Granular
SKU-levelProduct
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

49. Confidential & ProprietaryConfidential & Proprietary
Search in 2016
The Audience Revolution
Oli Petas, UK Search Audience & Automation Lead

50. 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 Data
I 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

51. 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

53. Maximising Marketing
Impact
Rohan Gifford, Research Manager, Google

54. Across 56 sales correlation studies
in EMEA YouTube had a Return
on Investment (ROI) higher than
TV in 43 (77% of total) studies

55. I Studies spanned countries and verticals
Automotive
Classifieds & Local
Consumer Packaged Goods
Media & Entertainment
Retail
Technology

56. I Three methodologies included
Media mix models
Consumer scan panel studies
Geo-tests

58. 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

59. YouTube
Optimal
Current
Spend

61. Making the Future Happen
7 Evidence Based Tips
Rohan Gifford, Research Manager, Google

62. 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

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

64. 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.

65. 2.Decay
Reach people regularly across
the campaign

66. 3.Device
Mobile delivers even higher
brand impact: plan mobile first

67. 4.Content
get higher brand lift next to the
most engaging content

68. 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

69. 5.Targeting
affinity targeting drives better
results

70. 6.Creative
Data is creative friend

71. 7.YouTube
and TV
AND not OR