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Using Data-Driven Insight to Improve
Outdoor Advertising Placement and Reach
Ciaran Abel, Business Intelligence & Analytic...
#inspireeurope16
Session Speaker
Agenda
• About me
• Our Story
• Market Context
• Why Alteryx?
• Key Uses
• What’s next?
• Examples
• Finishing up…
#inspir...
#inspireeurope16
To watch a recording of this session from Inspire Europe 2016, visit
alteryx.com/inspire-europe-2016-trac...
About me
WHAT I DO
6
• BACKGROUND – BA GEOGRAPHY
• PRIMARY EXPERIENCE IN MARKETING & GEO-SPATIAL ANALYSIS
• TITLE – BI & ANALYTICS ...
OURTEAM
7
BI & ANALYTICS
TEAM
MARKETING
SALES
RESEARCH INSIGHT
INVENTORY
DIGITAL
SCHEDULING
FINANCE
HR
IT
FRANCHISE /
ESTA...
Our Story
WHATWE DO
9
10,000+ Global Brands
OUR NEWS
10
• Bringing TfL’s rail assets together under one contract for the first time.
• The new partnership will span t...
OUR NEWS
11
12
COMING SOON
13
COMING SOON
14
COMING SOON
15
COMING SOON
16
COMING SOON
Market Context
ADVERTISING EXPENDITURE, 2015
18
GROWTH OF MOBILEAD-EXPENDITURE
19
COMPLIMENTARY MEDIA
20
STRATEGY – INVESTING IN ‘BEST IN CLASS’ DATA SOURCES
21
STRATEGY – DATA IS KEYTO MONETISINGAUDIENCES
22
Targeting
Effective &
Accountable
Flexible Delivery
Measuring
 Mining aud...
Why Alteryx?
#inspireeurope16
WHY ALTERYX?
#inspireeurope16
Existing Capability Potential Capability
Multiple Data Sources,
Unused and Unloved,
+150%
Three Fundament...
Key Uses
28
DATA SOURCES
29
DATA OUTPUTS
30
TARGETINGAUDIENCESWITH OUTDOOR ADVERTISING
RESIDENCE
SOCIAL &
RETAIL
WORK & COMMUTE
EXPENDITURE
DIGITAL
FOOTPRINTS &
ON...
CLIENT SCENARIO 1 – AFFLUENT FASHIONABLE FEMALES
31
CLIENT SCENARIO 2 – FOOTBALL ENTHUSIASTS
32
CLIENT SCENARIO 3 – SURREY BELT COMMUTERS
33
What’s next?
THE FUTURE OF OUT OF HOME ADVERTISING
35
AUTOMATION /
PROGRAMMATIC
INVENTORY
AVAILABILITY
DYNAMIC
PRICING
INDUSTRY
DATA
3R...
IT
Marketing, Research & Commercial Insight
Sales & Insight
Leadership Team
Finance, Commercial Insight & Leadership
1. Da...
EXPANDING SCOPE OFWORK
37
TFL API,
live data Data Viz
Data
Cleaning &
Fusing
Tool
Building
Spatial
Analytics
Salesforce
/ ...
REQUIREMENTS CREEP
38
ALTERYX SERVER
ANALYTICAPPS
CUSTOMER
INTERROGATION
I
N
T
E
R
N
A
L
E
X
T
E
R
N
A
L
Example workflows / Apps
EXAMPLE 1 – PROPRIETARY DATA
40
10 mins build
6.2 Seconds run
OBJECTIVE: DATA CLEANING, FUSING, INPUT / OUTPUT / QUERY
41
30 Mins build
23.2 Seconds run
EXAMPLE 2 – CLIENT DATA - FORECASTING
42
EXAMPLE 3 – 3rd PARTY DATA
Half a day build
Now run across floor
43
EXAMPLE 4 – 3rd PARTY DATA
STATION NAME 6_Sheet TA HOUSEHOLDS TA %
HIGH STREET KENSINGTON 16 6,119 100%
CANARY WHARF (D...
44
OBJECTIVE: BRAND MENTIONS BY LOCATION / SENTIMENT ANALYSIS
TweetPostedTime TweetBody TweetSource
TweetFa
voritesC
ount
...
45
EXAMPLE 5 – SOCIAL / OPEN DATA
1 hour build
£200k Revenue
46
EXAMPLE 6 – PARTNER DATA
30min build
1 Happy Client
47
EXAMPLE 7 – PARTNER DATA
1 hour build
No real reason
Finishing up..
KEY LESSONS
49
1.HIRE STAFF WITH RELEVANT SKILLSETS AND INTRIGUE
FOR ANALYTICS
2.SHARE USAGE OF ALTERYX WITH MULTIPLE
DEPA...
KEY BENEFITS
50
1. DATA QUALITY GOVERNANCE
2. SELF-SERVICE
3. TIME-SAVED
4. NO PROGRAMMING / CODING
5. ANALYTICS CULTURE
6...
Thanks
CIARAN ABEL
ciaran.abel@exterionmedia.co.uk |Twitter @ EXM_CA
13 September 2016
#inspire16
alteryx.com/trial
You can also achieve the incredible
benefits described in this slide deck
Download a FREETria...
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Using Data-Driven Insight to Improve Outdoor Advertising Placement and Reach: Exterion Media, Inspire Europe 2016

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One of the best in the world, the London public transportation system lets advertisers reach millions of potential customers each day in the London Underground, DLR, Elizabeth Line (formerly Crossrail), London Overground, and London Tramlink. This session will explore how Alteryx helps Transport for London (TfL) leverage data insights to provide sophisticated solutions for advertising partners.

To watch a recording of this session from Inspire Europe 2016, visit alteryx.com/inspire-europe-2016-tracks

Published in: Data & Analytics
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Using Data-Driven Insight to Improve Outdoor Advertising Placement and Reach: Exterion Media, Inspire Europe 2016

  1. 1. Using Data-Driven Insight to Improve Outdoor Advertising Placement and Reach Ciaran Abel, Business Intelligence & Analytics Manager, Exterion Media 13 September 2016
  2. 2. #inspireeurope16 Session Speaker
  3. 3. Agenda • About me • Our Story • Market Context • Why Alteryx? • Key Uses • What’s next? • Examples • Finishing up… #inspireeurope16
  4. 4. #inspireeurope16 To watch a recording of this session from Inspire Europe 2016, visit alteryx.com/inspire-europe-2016-tracks
  5. 5. About me
  6. 6. WHAT I DO 6 • BACKGROUND – BA GEOGRAPHY • PRIMARY EXPERIENCE IN MARKETING & GEO-SPATIAL ANALYSIS • TITLE – BI & ANALYTICS MANAGER • TEAM OF 3
  7. 7. OURTEAM 7 BI & ANALYTICS TEAM MARKETING SALES RESEARCH INSIGHT INVENTORY DIGITAL SCHEDULING FINANCE HR IT FRANCHISE / ESTATES CONNECTIVITY
  8. 8. Our Story
  9. 9. WHATWE DO 9 10,000+ Global Brands
  10. 10. OUR NEWS 10 • Bringing TfL’s rail assets together under one contract for the first time. • The new partnership will span the next 8.5 years, starting 1st October 2016 • The new concession encompasses advertising at 400+ stations across the London transport network, which supports over 4.35 million journeys every day
  11. 11. OUR NEWS 11
  12. 12. 12 COMING SOON
  13. 13. 13 COMING SOON
  14. 14. 14 COMING SOON
  15. 15. 15 COMING SOON
  16. 16. 16 COMING SOON
  17. 17. Market Context
  18. 18. ADVERTISING EXPENDITURE, 2015 18
  19. 19. GROWTH OF MOBILEAD-EXPENDITURE 19
  20. 20. COMPLIMENTARY MEDIA 20
  21. 21. STRATEGY – INVESTING IN ‘BEST IN CLASS’ DATA SOURCES 21
  22. 22. STRATEGY – DATA IS KEYTO MONETISINGAUDIENCES 22 Targeting Effective & Accountable Flexible Delivery Measuring  Mining audiences out of mass transit flows How?  Segmenting audiences based on both demographics (e.g. Mosaic, Route) and behavioural factors (e.g. Telefonica)…  … and overlaying this with volumetric data on passenger usage of the LU / TfL Estate (e.g. RODS, Gate, Telefonica)  Displaying content in the best places at the right times How?  Inventory optimisation intelligence  Content distribution system (Broadsign) and management  Measuring Actuals vs Plan – Quantity and quality of audience delivered in near real-time How?  Use of “connected infrastructure” (e.g. WiFi, Small cell) to provide (near) real-time actuals of passengers: – Moving past assets (corridor traffic) – Interacting with assets (e.g. search and web activity) – Dwelling by assets (i.e. cross-track)  Accessing third party data on transactions (e.g. Beyond Analysis)
  23. 23. Why Alteryx?
  24. 24. #inspireeurope16 WHY ALTERYX?
  25. 25. #inspireeurope16 Existing Capability Potential Capability Multiple Data Sources, Unused and Unloved, +150% Three Fundamental Business Challenges 3. Creativity 2. Usage of Data 1. Speed WHY ALTERYX?
  26. 26. Key Uses
  27. 27. 28 DATA SOURCES
  28. 28. 29 DATA OUTPUTS
  29. 29. 30 TARGETINGAUDIENCESWITH OUTDOOR ADVERTISING RESIDENCE SOCIAL & RETAIL WORK & COMMUTE EXPENDITURE DIGITAL FOOTPRINTS & ONLINE BEHAVIOURS
  30. 30. CLIENT SCENARIO 1 – AFFLUENT FASHIONABLE FEMALES 31
  31. 31. CLIENT SCENARIO 2 – FOOTBALL ENTHUSIASTS 32
  32. 32. CLIENT SCENARIO 3 – SURREY BELT COMMUTERS 33
  33. 33. What’s next?
  34. 34. THE FUTURE OF OUT OF HOME ADVERTISING 35 AUTOMATION / PROGRAMMATIC INVENTORY AVAILABILITY DYNAMIC PRICING INDUSTRY DATA 3RD PARTY DATA OPEN SOURCE DATA CASE STUDY INFO 1. LOCATION INTELLIGENCE 2. AUDIENCE 3. TIMING 4. OBJECTIVE
  35. 35. IT Marketing, Research & Commercial Insight Sales & Insight Leadership Team Finance, Commercial Insight & Leadership 1. Data Quality Governance 2. Marketing Analytics 3. Geo-Spatial Analytics 4. Sales Tracking, Reporting 5. Financial Forecasting EXPANDING DEPARTMENTAL DEPENDENCIES 36 Business intelligence (BI) is an umbrella term that includes the applications, infrastructure and tools, and best practices that enable access to and analysis of information to improve and optimize decisions and performance.
  36. 36. EXPANDING SCOPE OFWORK 37 TFL API, live data Data Viz Data Cleaning & Fusing Tool Building Spatial Analytics Salesforce / CRM Integration Client Data Predictive Analytics Internal Reporting Sentiment Analysis Data Investigation
  37. 37. REQUIREMENTS CREEP 38 ALTERYX SERVER ANALYTICAPPS CUSTOMER INTERROGATION I N T E R N A L E X T E R N A L
  38. 38. Example workflows / Apps
  39. 39. EXAMPLE 1 – PROPRIETARY DATA 40 10 mins build 6.2 Seconds run OBJECTIVE: DATA CLEANING, FUSING, INPUT / OUTPUT / QUERY
  40. 40. 41 30 Mins build 23.2 Seconds run EXAMPLE 2 – CLIENT DATA - FORECASTING
  41. 41. 42 EXAMPLE 3 – 3rd PARTY DATA Half a day build Now run across floor
  42. 42. 43 EXAMPLE 4 – 3rd PARTY DATA STATION NAME 6_Sheet TA HOUSEHOLDS TA % HIGH STREET KENSINGTON 16 6,119 100% CANARY WHARF (DLR) 44 1,061 99% CANARY WHARF (LUL) 13 1,061 99% KNIGHTSBRIDGE 25 4,152 99% ST PAULS 5 2,607 98% MARBLE ARCH 8 4,941 98% HYDE PARK CORNER 9 2,454 98% BAKER STREET 29 5,439 98% GLOUCESTER ROAD 3 6,574 98% LANCASTER GATE 2 3,305 97% PUTNEY BRIDGE 1 3,813 97% EAST PUTNEY 3 4,553 97% NOTTING HILL GATE 13 5,793 96% BOND STREET 70 2,906 96% EMBANKMENT 9 1,311 96% EARLS COURT 44 8,958 95% GREEN PARK 50 1,851 95% MANSION HOUSE 33 1,057 95% TURNHAM GREEN 4 4,078 94% BLACKFRIARS 2 1,634 93% TEMPLE 21 752 92% HOLLAND PARK 5 3,797 92% HIGHGATE 1 2,650 92% Half a day build Now run across floor
  43. 43. 44 OBJECTIVE: BRAND MENTIONS BY LOCATION / SENTIMENT ANALYSIS TweetPostedTime TweetBody TweetSource TweetFa voritesC ount TweetHashtags TweetPlaceFullNam e UserScreenN ame UserLocation Tue Sep 29 22:10:21 +0000 2015 Another day, another run. #nike #werunlondon #NRC #NTC @ Northbank https://t.co/9XzJp9XE39 Instagram 0 #nike #werunlondon #NRC #NTC City of London, London andyraye Bucks Tue Sep 29 18:33:44 +0000 2015 Them new Vicks heat with some fresh Nike grey sweats Tweetlogix 0 Camberwell, London ClassicBray Where the sun shines Tue Sep 29 17:53:12 +0000 2015 When you see yourself in-store @jdsportsfashion @nikesportswear @nike #model #modelling #nike @ https://t.co/ZGnoDZ17Zr Instagram 1 #model #modelling #nike London, England Devarnio Manchester (England) EXAMPLE 4 – SOCIAL / OPEN DATA 30 Seconds build 2mins run
  44. 44. 45 EXAMPLE 5 – SOCIAL / OPEN DATA 1 hour build £200k Revenue
  45. 45. 46 EXAMPLE 6 – PARTNER DATA 30min build 1 Happy Client
  46. 46. 47 EXAMPLE 7 – PARTNER DATA 1 hour build No real reason
  47. 47. Finishing up..
  48. 48. KEY LESSONS 49 1.HIRE STAFF WITH RELEVANT SKILLSETS AND INTRIGUE FOR ANALYTICS 2.SHARE USAGE OF ALTERYX WITH MULTIPLE DEPARTMENTS AS SOON AS POSSIBLE 3.TRY TO USE ALTERYX AS A COMPLIMENTARY TOOL TO WIDER IT INITIATIVES WHICH CANNOT BE BUILT TO DO EVERYTHING
  49. 49. KEY BENEFITS 50 1. DATA QUALITY GOVERNANCE 2. SELF-SERVICE 3. TIME-SAVED 4. NO PROGRAMMING / CODING 5. ANALYTICS CULTURE 6. BECOMING PRETTY USEFUL =
  50. 50. Thanks CIARAN ABEL ciaran.abel@exterionmedia.co.uk |Twitter @ EXM_CA 13 September 2016
  51. 51. #inspire16 alteryx.com/trial You can also achieve the incredible benefits described in this slide deck Download a FREETrial of Alteryx and experience self-service data analytics on your next data project

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