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Marketing Automation 
and Big Data 
From Lotus Marketplace 
to Acxiom’s Aboutthedata.com and Beyond 
Peter O’Kelly 
Chief Data Officer, ShopAdvisor 
12/2/2014
Agenda 
• Context setting 
• A historical recap of big data in marketing 
automation 
• Today’s state-of-the-art 
• Some projections 
• Discussion 
2
Context Setting 
• Marketing automation 
– “The term ‘marketing automation’ has grown 
from referring to simple workflow tools to help 
companies and their partners manage campaigns 
to being used to cover a much broader and more 
amorphous set of capabilities.” 
3
Context Setting 
• Big data 
– Weirdly, there is no industry consensus on a detailed 
“big data” definition 
– The overall significance of big data market dynamics 
• Many data management technologies that used to be 
complex, expensive, and scarce are now almost absurdly 
accessible, affordable, and abundant 
– Unfortunately, “big data” as a meme has also probably 
been over-hyped into meaninglessness 
– For marketing automation concerns, just think of data 
– default big – and legacy data 
4
Agenda 
• Context setting 
• A historical recap of big data in marketing 
automation 
• Today’s state-of-the-art 
• Some projections 
• Discussion 
5
Snapshot: c1970 
• Marketing 
– Channels: print, radio, TV, out-of-home ads 
– Targeting: geographic, demographic… 
– Cycle times: often seasonal and campaign-based 
– Automation: not so much… 
• Data 
– Mainstream information technology: mainframes 
– Data sources: limited and expensive 
– Data scope and analytics: limited 
• Consumer perspectives: marketing seen as a mix of mostly 
mass market advertising and in-person sales engagements 
– With a high degree of information asymmetry 
6
Tech Snapshot: Nielsen 
7
Snapshot: c1990 
• Marketing 
– Channels: business as usual, for the most part 
– Targeting: still often geographic, demographic… 
– Cycle times: also mostly business as usual 
– Automation: expanding use of workflow tools 
• Data 
– Mainstream IT: mainframes, minicomputers, database machines, PCs 
– Data sources: expanding, and becoming more accessible and 
affordable 
– Data scope and analytics: PC-based tools augmenting traditional 
techniques 
• Consumer perspectives: some privacy concerns and growing 
awareness of data aggregators and brokers 
8
Tech Snapshot: Lotus Marketplace 
9
Snapshot: c2010 
• Marketing 
– Channels: major emphasis on Web and email 
– Targeting: geographic, demographic, psychographic, content profile-based, 
Web cookies… 
– Cycle times: more interactive and dynamic, extensive A/B tests 
– Automation: increasingly Web-centric and programmatic 
• Data 
– Mainstream IT: Web-centric, with the SaaS shift gaining momentum 
– Data sources: on the fast track to “big data”; also rapid expansion of 
data aggregators and brokers, and explosive social media growth 
– Data scope and analytics: rapidly expanding scope; powerful and 
predictive Web analytics 
• Consumer perspectives: many people annoyed by spam and 
ubiquitous ads; growing concerns about privacy and security 
10
11
Agenda 
• Context setting 
• A historical recap of big data in marketing 
automation 
• Today’s state-of-the-art 
• Some projections 
• Discussion 
12
Today’s State of the Art 
• As if things weren’t already moving fast enough… 
recent enablers/drivers include 
– Commodity hardware 
– Cloud platforms and services 
– Smartphones and other mobile devices 
– Social media 
– Open source 
– Open data 
– Data services 
– Beacon and other proximity-related technologies 
13
Today’s State of the Art 
• Some trends with significant momentum 
– Programmatic marketing 
• With ad markets now resembling high-frequency trading modus 
operandi 
– Native advertising 
• In content, apps, social media streams, … 
– Combining on-line and off-line profiles and activity data 
– Proximity-based mobile marketing 
• Back to the future trend: major focus on driving consumer traffic 
to physical stores 
– “Internet of Things” 
– “Digital anthropology” 
14
Another Big Data Twist 
• Google, Facebook, and other service providers 
are strongly rewarding quality and relevant 
content 
– As rated by their criteria, based on their analysis of 
user and content activity patterns 
• Within ad marketplaces they increasingly dominate 
• Examples 
– Google organic search results and stringent quality 
criteria for ad placement bids 
– Facebook’s policy (starting 1/2015) for “reducing 
overly promotional page posts in news feed” 
15
Consumer-Related Reactions 
• Many consumers likely annoyed by retargeting 
• Calls for expanded privacy and security regulation 
• Some vendors making consumer privacy a top 
priority and competitive differentiator 
– Especially Apple 
• And yet some paradoxical dimensions, e.g., a 
recent Pew Research Center survey summarized 
in the New York Times as “Americans say they 
want privacy, but act as if they don’t” 
16
Consumer-Related Reactions 
17
Recap: Today’s State of the Art 
• Marketing 
– Channels: everything… with a major focus on mobile and social 
– Targeting: a cumulative build, adding retargeting, social graph models, 
proximity, and much more… 
– Cycle times: ad auctions measured in milliseconds; proximity-based 
offers made in real-time 
– Automation: full-spectrum and mission-critical 
• Data 
– Mainstream IT: real-time, omni-channel, and cloud-centric 
– Data sources: aggregators/brokers and on-line leaders partnering for 
“onboarding” 
– Data scope and analytics: in some respects perhaps leading the NSA… 
• Consumer perspectives: 
– Likely to dread “Minority Report” scenarios on mobile devices 
– Consumer privacy control is now a competitive differentiator 
18
Agenda 
• Context setting 
• A historical recap of big data in marketing 
automation 
• Today’s state-of-the-art 
• Some projections 
• Discussion 
19
Projections 
• New opportunities and imperatives 
• Incredible innovation in related products and 
services 
• Consumer information symmetry and 
personal information control 
• Back to data basics 
20
New Opportunities and Imperatives 
• William Gibson: “The future is already here – it’s just not 
evenly distributed” 
• Opportunities 
– Incredible precision in targeting and customer journey/funnel 
phase tracking 
– Database technology and services making it possible to maintain 
360-degree perspectives 
• But also new critical success factors – competitive 
imperatives 
– New perspectives and skills required 
– Unprecedented degrees of integration and coordination 
– Privacy and security done wrong can be job (or company) killers 
21
New Opportunities and Imperatives 
• Also key to add value with content, products, and 
services – relevant, timely, focused, competitive… 
• And to clearly and purposefully communicate core value 
propositions 
• Google and Facebook modus operandi are 
important leading indicators 
– Qualified/filtered presentation – based on what they 
determine is most likely to be relevant and useful 
• Assessed by a huge number of metrics 
– Many of which you don’t directly control 
22
Product/Service Innovation 
• Reduced barriers to entry, in combination with 
cloud, open data, and other market dynamics, 
have led to incredible product/service 
innovation 
• But this can be a mixed blessing, with 
significant disruption and churn, along with 
new opportunities 
23
Innovation in Products and Services
Today’s State of the Art 
25
Today’s State of the Art 
26
But Wait, There’s More…
Consumer Info Symmetry and Control 
• Consumers have unprecedented access to high 
quality and timely information resources 
– Making it simpler to find the best offerings and deals 
• In almost any context 
• New and increasingly elaborate privacy and 
security expectations 
– With personal information management now a 
mainstream competitive differentiator 
• And new advertising id models potentially supplanting Web 
cookies and other identity schemes, over time 
28
Back to Data Basics 
• Fundamental price/performance improvements 
and new capabilities 
– And lots of room for continued innovation ahead 
• Making it more important than ever before to 
develop skills in 
– Data modeling 
– Query formulation 
– Data analytics – increasingly “democratized” 
• Overall: a paradox of abundance in related 
products and services, but only helpful if used 
effectively 
29
Agenda 
• Context setting 
• A historical recap of big data in marketing 
automation 
• Today’s state-of-the-art 
• Some projections 
• Discussion 
30
Discussion 
• This presentation can be downloaded from 
the conference Web site 
31

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Marketing Automation and Big Data Trends

  • 1. Marketing Automation and Big Data From Lotus Marketplace to Acxiom’s Aboutthedata.com and Beyond Peter O’Kelly Chief Data Officer, ShopAdvisor 12/2/2014
  • 2. Agenda • Context setting • A historical recap of big data in marketing automation • Today’s state-of-the-art • Some projections • Discussion 2
  • 3. Context Setting • Marketing automation – “The term ‘marketing automation’ has grown from referring to simple workflow tools to help companies and their partners manage campaigns to being used to cover a much broader and more amorphous set of capabilities.” 3
  • 4. Context Setting • Big data – Weirdly, there is no industry consensus on a detailed “big data” definition – The overall significance of big data market dynamics • Many data management technologies that used to be complex, expensive, and scarce are now almost absurdly accessible, affordable, and abundant – Unfortunately, “big data” as a meme has also probably been over-hyped into meaninglessness – For marketing automation concerns, just think of data – default big – and legacy data 4
  • 5. Agenda • Context setting • A historical recap of big data in marketing automation • Today’s state-of-the-art • Some projections • Discussion 5
  • 6. Snapshot: c1970 • Marketing – Channels: print, radio, TV, out-of-home ads – Targeting: geographic, demographic… – Cycle times: often seasonal and campaign-based – Automation: not so much… • Data – Mainstream information technology: mainframes – Data sources: limited and expensive – Data scope and analytics: limited • Consumer perspectives: marketing seen as a mix of mostly mass market advertising and in-person sales engagements – With a high degree of information asymmetry 6
  • 8. Snapshot: c1990 • Marketing – Channels: business as usual, for the most part – Targeting: still often geographic, demographic… – Cycle times: also mostly business as usual – Automation: expanding use of workflow tools • Data – Mainstream IT: mainframes, minicomputers, database machines, PCs – Data sources: expanding, and becoming more accessible and affordable – Data scope and analytics: PC-based tools augmenting traditional techniques • Consumer perspectives: some privacy concerns and growing awareness of data aggregators and brokers 8
  • 9. Tech Snapshot: Lotus Marketplace 9
  • 10. Snapshot: c2010 • Marketing – Channels: major emphasis on Web and email – Targeting: geographic, demographic, psychographic, content profile-based, Web cookies… – Cycle times: more interactive and dynamic, extensive A/B tests – Automation: increasingly Web-centric and programmatic • Data – Mainstream IT: Web-centric, with the SaaS shift gaining momentum – Data sources: on the fast track to “big data”; also rapid expansion of data aggregators and brokers, and explosive social media growth – Data scope and analytics: rapidly expanding scope; powerful and predictive Web analytics • Consumer perspectives: many people annoyed by spam and ubiquitous ads; growing concerns about privacy and security 10
  • 11. 11
  • 12. Agenda • Context setting • A historical recap of big data in marketing automation • Today’s state-of-the-art • Some projections • Discussion 12
  • 13. Today’s State of the Art • As if things weren’t already moving fast enough… recent enablers/drivers include – Commodity hardware – Cloud platforms and services – Smartphones and other mobile devices – Social media – Open source – Open data – Data services – Beacon and other proximity-related technologies 13
  • 14. Today’s State of the Art • Some trends with significant momentum – Programmatic marketing • With ad markets now resembling high-frequency trading modus operandi – Native advertising • In content, apps, social media streams, … – Combining on-line and off-line profiles and activity data – Proximity-based mobile marketing • Back to the future trend: major focus on driving consumer traffic to physical stores – “Internet of Things” – “Digital anthropology” 14
  • 15. Another Big Data Twist • Google, Facebook, and other service providers are strongly rewarding quality and relevant content – As rated by their criteria, based on their analysis of user and content activity patterns • Within ad marketplaces they increasingly dominate • Examples – Google organic search results and stringent quality criteria for ad placement bids – Facebook’s policy (starting 1/2015) for “reducing overly promotional page posts in news feed” 15
  • 16. Consumer-Related Reactions • Many consumers likely annoyed by retargeting • Calls for expanded privacy and security regulation • Some vendors making consumer privacy a top priority and competitive differentiator – Especially Apple • And yet some paradoxical dimensions, e.g., a recent Pew Research Center survey summarized in the New York Times as “Americans say they want privacy, but act as if they don’t” 16
  • 18. Recap: Today’s State of the Art • Marketing – Channels: everything… with a major focus on mobile and social – Targeting: a cumulative build, adding retargeting, social graph models, proximity, and much more… – Cycle times: ad auctions measured in milliseconds; proximity-based offers made in real-time – Automation: full-spectrum and mission-critical • Data – Mainstream IT: real-time, omni-channel, and cloud-centric – Data sources: aggregators/brokers and on-line leaders partnering for “onboarding” – Data scope and analytics: in some respects perhaps leading the NSA… • Consumer perspectives: – Likely to dread “Minority Report” scenarios on mobile devices – Consumer privacy control is now a competitive differentiator 18
  • 19. Agenda • Context setting • A historical recap of big data in marketing automation • Today’s state-of-the-art • Some projections • Discussion 19
  • 20. Projections • New opportunities and imperatives • Incredible innovation in related products and services • Consumer information symmetry and personal information control • Back to data basics 20
  • 21. New Opportunities and Imperatives • William Gibson: “The future is already here – it’s just not evenly distributed” • Opportunities – Incredible precision in targeting and customer journey/funnel phase tracking – Database technology and services making it possible to maintain 360-degree perspectives • But also new critical success factors – competitive imperatives – New perspectives and skills required – Unprecedented degrees of integration and coordination – Privacy and security done wrong can be job (or company) killers 21
  • 22. New Opportunities and Imperatives • Also key to add value with content, products, and services – relevant, timely, focused, competitive… • And to clearly and purposefully communicate core value propositions • Google and Facebook modus operandi are important leading indicators – Qualified/filtered presentation – based on what they determine is most likely to be relevant and useful • Assessed by a huge number of metrics – Many of which you don’t directly control 22
  • 23. Product/Service Innovation • Reduced barriers to entry, in combination with cloud, open data, and other market dynamics, have led to incredible product/service innovation • But this can be a mixed blessing, with significant disruption and churn, along with new opportunities 23
  • 24. Innovation in Products and Services
  • 25. Today’s State of the Art 25
  • 26. Today’s State of the Art 26
  • 28. Consumer Info Symmetry and Control • Consumers have unprecedented access to high quality and timely information resources – Making it simpler to find the best offerings and deals • In almost any context • New and increasingly elaborate privacy and security expectations – With personal information management now a mainstream competitive differentiator • And new advertising id models potentially supplanting Web cookies and other identity schemes, over time 28
  • 29. Back to Data Basics • Fundamental price/performance improvements and new capabilities – And lots of room for continued innovation ahead • Making it more important than ever before to develop skills in – Data modeling – Query formulation – Data analytics – increasingly “democratized” • Overall: a paradox of abundance in related products and services, but only helpful if used effectively 29
  • 30. Agenda • Context setting • A historical recap of big data in marketing automation • Today’s state-of-the-art • Some projections • Discussion 30
  • 31. Discussion • This presentation can be downloaded from the conference Web site 31

Editor's Notes

  1. From session description at http://gilbaneconference.com/2014/program.aspx
  2. Source: http://www.moneymisfit.com/getting-paid-watch-tv-nielsen-company/
  3. Source: http://faculty.wiu.edu/E-Solymossy/Presentations/MGT%20481/Lotus%20MarketPlace.pdf Harvard Business Review case study on Lotus Marketplace: Households
  4. Tech snapshot: site introduced during 2013, after rapid expansion of data aggregators/brokers https://aboutthedata.com/ Captured 20141102
  5. Facebook details: http://newsroom.fb.com/news/2014/11/news-feed-fyi-reducing-overly-promotional-page-posts-in-news-feed/
  6. NYT source: http://www.nytimes.com/2014/11/13/upshot/americans-say-they-want-privacy-but-act-as-if-they-dont.html
  7. Source http://www.pewinternet.org/2014/11/12/public-confidence-in-the-security-of-core-communications-channels-is-low/ captured 20141114
  8. William Gibson quote: http://en.wikipedia.org/wiki/William_Gibson
  9. Source: http://www.realstorygroup.com/vendormap/ Captured 20141119
  10. Source: http://chiefmartec.com/2014/01/marketing-technology-landscape-supergraphic-2014/ captured 2014114 (Repeats graphic from previous slide, for viewing clarity)
  11. Source: http://www.lumapartners.com/lumascapes/display-ad-tech-lumascape/ captured 2014114
  12. Source: http://blogs.the451group.com/information_management/2014/11/18/updated-data-platforms-landscape-map/ Captured 20141119