The shift of marketing dollars online and the increasing role of social media have ushered in the era of Big Data - where marketers must effectively mine and analyze massive amounts of data and respond to changing business needs instantly. While most marketers today are well versed in the metrics of their online marketing channels, far fewer are leveraging their 'Big Data' across all digital marketing channels for cross-functional insight into customer behavior, to gain efficiencies across all channels and to grow traffic and revenue. Key takeaways include: - The impact Big Data will have on the modern marketer - forcing professionals to become part marketer, part Chief Information Officer - How to leverage the latest advancements in technology to gather, analyze and react to new consumer behavior uncovered by Big Data with an emphasis on visibility, optimization and automation Download the presentation materials by: http://www.conductor.com/resource-center/presentations/ses-2012-age-big-data-and-modern-marketer

1. Chicago(|(November(12–16(
The(Age(of(Big(Data(
and(the(Modern(
Marketer(
Seth(Do?erer(
Conductor(
VP,(MarkeDng(and(Product(
(speaker(logo)(

2. Chicago(|(November(12–16,(2012(|(#SESCHI(
Link: http://www.youtube.com/watch?v=QV3t-3QIf1E

3. Chicago(|(November(12–16,(2012(|(#SESCHI(
Data underpins our
economy and our society
- data about how much is
being spent and where,
data about how schools,
hospitals and police are
performing, data about
where things are and data
about the weather.
Tim Berners Lee,
Director of W3C.

4. Chicago(|(November(12–16,(2012(|(#SESCHI(
What(is((
Big(Data?(

5. Chicago(|(November(12–16,(2012(|(#SESCHI(
A collection of data sets so large and
complex that it becomes difficult to
process using on-hand database
management tools.

6. Chicago(|(November(12–16,(2012(|(#SESCHI(
DefiniDon(of(Big(Data(
• a(
@twitterhandle
Volume - Variety - Velocity
(

7. Chicago(|(November(12–16,(2012(|(#SESCHI(
Volume(

8. Chicago(|(November(12–16,(2012(|(#SESCHI(

9. Chicago(|(November(12–16,(2012(|(#SESCHI(

10. Chicago(|(November(12–16,(2012(|(#SESCHI(
700(million(
Facebook(
users(
250(million(
TwiHer(users(
156(million(
public(blogs(

11. Chicago(|(November(12–16,(2012(|(#SESCHI(
Volume(
@dotterer
An(average(of(294(
billion(eMmails(are(
sent(every(day.(
(
2.4(Billion(
People(
Online((
(

12. Chicago(|(November(12–16,(2012(|(#SESCHI(
The(machines(also(talk((
to(each(other(

13. Chicago(|(November(12–16,(2012(|(#SESCHI(
Variety(

14. Chicago(|(November(12–16,(2012(|(#SESCHI(
Structured(Data(

15. Chicago(|(November(12–16,(2012(|(#SESCHI(
Variety(
• Structured(Data:(
• Unstructured(Data(
• Even(Structured(Data(has(issues((
@twitterhandle
Unstructured(
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!(mostly)!

16. Chicago(|(November(12–16,(2012(|(#SESCHI(
Velocity(

17. Chicago(|(November(12–16,(2012(|(#SESCHI(
WalLMart(generates(more(than(1M(
transacDons(an(hour(into(databases(
esDmated(at(more(than(2.5(petabytes(

18. Chicago(|(November(12–16,(2012(|(#SESCHI(

19. Chicago(|(November(12–16,(2012(|(#SESCHI(

20. Chicago(|(November(12–16,(2012(|(#SESCHI(
Hadoop(has(its(roots(as(a(search(engine(
• (Apache(Hadoop,(the(leading(open(source(soluXon,(was(created(from(Google’s(MapReduce(
and(the(Google(File(System(but(adopted(by(a(Yahoo(employee.(
@dotterer
• Now,(Hadoop(and(the(resulXng(and(related(technologies(and(toolsets(are(used(to(create(
distributed(processing(across(clusters(of(computers.(((These(scale(up(to(thousands(of(machines(
that(each(can(store(and(process(informaXon,(and(can(pick(up(failures(from(any(of(their(
neighbor(machines.(

21. Chicago(|(November(12–16,(2012(|(#SESCHI(
Big data is the fuel. It is like oil. If you leave
it in the ground, it doesn’t have value. But
when we find ways to ingest, curate, and
analyze the data in new and different ways,
such as in Watson, Big Data becomes very
interesting.”
Stephen Gold, VP of
Marketing for IBM’s Watson

22. Chicago(|(November(12–16,(2012(|(#SESCHI(

23. Chicago(|(November(12–16,(2012(|(#SESCHI(

24. Chicago(|(November(12–16,(2012(|(#SESCHI(

25. Chicago(|(November(12–16,(2012(|(#SESCHI(

26. Chicago(|(November(12–16,(2012(|(#SESCHI(

27. Chicago(|(November(12–16,(2012(|(#SESCHI(

28. Chicago(|(November(12–16,(2012(|(#SESCHI(

29. Chicago(|(November(12–16,(2012(|(#SESCHI(
How(Search(Marketers(can(use(big(data(
• Think(Flow,(not(Batch.(
• PPC(Vendors(like(Marin,(Kenshoo,(Adobe(all(have(big(data(opXmizaXon((
• Leverage(crossMdiscipline((SEO(posiXon(data(to(drive(Bids,(and(viceMversa)(
• Mining(and(teasing(out(the(tacXcs(and(strategies(that(are(working,(vs.(what(is(noise(
• Opportunity(forecasXng(
@dotterer

30. Chicago(|(November(12–16,(2012(|(#SESCHI(
How(Cross(Channel(MarkeDng(is(Changing(how(independent(silos(
are(working(Big(Data(Means….(
No(More(Silos(

31. Chicago(|(November(12–16,(2012(|(#SESCHI(

32. Chicago(|(November(12–16,(2012(|(#SESCHI(

33. Chicago(|(November(12–16,(2012(|(#SESCHI(

34. Chicago(|(November(12–16,(2012(|(#SESCHI(
How(Cross(Channel(MarkeDng(is(changing(how(we(keep(score(
(
@dotterer

35. Chicago(|(November(12–16,(2012(|(#SESCHI(

36. Chicago(|(November(12–16,(2012(|(#SESCHI(
• MCAMADS(M(MulXMChannel(AHribuXon,(Across(Digital(Channels:(
• MCAMO2S(M(MulXMChannel(AHribuXon,(Online(to(Store(
• MCAMAMS(M(MulXMChannel(AHribuXon,(Across(MulXple(Screens(
Are(you(referring(to(MCALO2S,(MCALAMS(or(MCALADC?(
http://www.kaushik.net/avinash/multi-channel-attribution-definitions-models/

37. Chicago(|(November(12–16,(2012(|(#SESCHI(
“The(ability(to(
take(data(L(to(be(
able(to(
understand(it,(to(
process(it,(to(
extract(value(
from(it,(to(
visualize(it,(to(
communicate(
(
(it's(going(to(be(a(
hugely(important(
skill(in(the(next(
decades”(
Hal(Varian((
L(Google’s(Chief(Economist(

38. Chicago(|(November(12–16,(2012(|(#SESCHI(

39. Chicago(|(November(12–16,(2012(|(#SESCHI(

40. Chicago(|(November(12–16,(2012(|(#SESCHI(

41. Chicago(|(November(12–16,(2012(|(#SESCHI(
Don’t(forget(the((
((((((((((clean(up(

42. Chicago(|(November(12–16,(2012(|(#SESCHI(
Takeaways(
• Big(Data(is(what(YOU(say(it(is,(so(that(you(can(solve(your(problems.(((
Don't(lose(sight(of(that,(or(blindly('trust(the(data'.(
• The(more(frequently((or(faster)(you(analyze(your(data,(the(more(likely(it(is(to(be(valuable.((
Think(less(about(‘batch’(and(more(about(flow.(
• Find(new(sources(of(data(–(don’t(just(dumping(more(of(the(same(at(it.(((
• Keep(data(as(long(as(you(can(
• Become((or(hire)(a(staXsXcian(M(Big(Data(and(advanced(analyXcs(have(emerged,(with(programs(
sprouXng(up(at(USC,(N.C.(State,(NYU(and(elsewhere.(
• Figuring(out(what(the(data(tells(you(is(hard.((
• How(to(convince(others(what(the(data(tells(you(is(harder.((VisualizaXon(helps.(
@dotterer

43. Chicago(|(November(12–16,(2012(|(#SESCHI(
Thanks!(
(
(
sdo?erer@conductor.com(
@do?erer(

44. Chicago(|(November(12–16,(2012(|(#SESCHI(
Photo(Credits(
• hHp://www.flickr.com/photos/29691859@N03/2873017925/sizes/l/(
• hHp://www.flickr.com/photos/baqueroguapo/5567237076/sizes/l/in/photostream/(
• hHp://www.flickr.com/photos/markusperl/7689989242/sizes/l/in/photostream/(
• hHp://www.flickr.com/photos/ragfield/6728728687/lightbox/(
• hHp://www.flickr.com/photos/ppix/2305078608/sizes/l/in/photostream/(
• hHp://www.flickr.com/photos/matneym/4145370899/sizes/l/in/photostream/(
@dotterer