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SES Chicago 2012 | Seth Dotterer: "The Age of Big Data and the Modern Marketer"

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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 …

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

Published in: Business, Technology, Design

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  • 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 oureconomy and our society- data about how much isbeing spent and where,data about how schools,hospitals and police areperforming, data aboutwhere things are and dataabout 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 andcomplex that it becomes difficult toprocess using on-hand databasemanagement tools.
  • 6. Chicago(|(November(12–16,(2012(|(#SESCHI(DefiniDon(of(Big(Data(•  a(@twitterhandleVolume - 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(@dottererAn(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((@twitterhandleUnstructured(!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!(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 leaveit in the ground, it doesn’t have value. Butwhen we find ways to ingest, curate, andanalyze the data in new and different ways,such as in Watson, Big Data becomes veryinteresting.”Stephen Gold, VP ofMarketing 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(((its(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.(((Dont(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