Big Data and Marketing Technology


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Big Data represent an opportunity for organizations with data analysis needs. Companies need to prepare a number of functions to address the Big Data Challenge.

The following presentation describes the Big Data landscape for marketing technology, introducing several applications, and describing the three key aspects a media agency must focus on when dealing with Big Data analysis applications.

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  • The presentation is structured according to the 3 key aspects that the company must focus on (in our opinion)The first one is technology.In this section I have characterised the data sources and enumerated a set of applications exploiting these data sources.Data sources are characterised by the huge volume of heterogeneous data they contain.Applications are characterised by applying heavyweight data analysis processes to the data extracted from such data sources.
  • Data sources used by marketing applications are diverse.Regarding data structure, such data sources range from structured ones (e.g., off-line advertising) to highly unstructured ones (e.g., social media)Regarding the format of the data they contain, data sources are typically heterogeneous (e.g., Twitter and Facebook data formats)Regarding the granularity of the data, data sources range from those with fine-grained data (e.g., at the level of impression or click in digital display advertising) to data aggregated monthly (e.g., off-line media audience studies)Regarding size, data sources range from Big DATA sources with several TB (e.g., Digital Display Advertising and Social Media) to small ones (e.g., off-line advertising)
  • Data HeterogeneityFrom structured to unstructured formatsFormats non-standardisedDifferent levels of aggregationFrom fine-grained posts and eventsTo time-series aggregated at different time periods (every second, hourly, daily, etc.)Different data sizesBig Data vs. “not so big” Data (both useful)Data Integration is a challengeEven for the same kinds of data (Twitter vs. Facebook, Google vs. Affiliate Networks)Social media content: Twitter vs. FacebookDigital display advertising: Google vs. Affiliate Networks
  • Different kind of applications can be developed by using the aforementioned data sources (individually or combining them)The first kind are Brand Reputation Monitoring applications, which make use of social media data.The second one are Market Research Applications, which typically combine different kinds of data sources, such as social media data for opinion studies and customer data (e.g., sales data)Another kind of applications are Social CRM applications, which consist on managing relationship with customers in social media.The forth group are applications for measuring the performance of communication, which also involve integrating heterogeneous data sources (e.g., online and offline advertising data sources with site analytics)Finally, a promising less-explored group of applications are those that automatize the process of media planning by analysing the insights obtained from the marketing data sources (e.g., placing advertising on TV programs, according to the engagement obtained from Social TV data sources.
  • The second key aspect a company should focus regarding BIG data applications is the alignment between technology specific customers needs.Moving from enumerating facts extracted from data to using such facts for decision support.In our opinion, such needs go beyond basic social media monitoring.
  • Brand Reputation Monitoring Applications are state-of-the-art applications of social media analysisWhich capture a set of basic KPIs and monitor their evolution over timeSuch indicators are typically:Brand popularity (i.e., volume of mentions to a brand in social media)Brand valuation (i.e., sentiment of the opinions mentioning the brand – positive, neutral or negative)Brand attributes (i.e., topics mentioned when talking about the brand)
  • In our opinion, Brand Reputation Monitoring applications are not enough to capture the complexity of the analyses required for social media studiesVolume and sentiment are metrics that cannot explain the status of the market by themselves.In addition, such applications perform a poor segmentation of the consumer (age, gender, incomes, place of residence).As these applications are focused on opinions (not in customers), it is difficult to perform Social CRM by using such applications.Finally, for a marketing company it is difficult to measure business-relevant KPIs, such as the performance of an advertising campaign.Next, we well explain several inovative applications being developed by Havas Media Group, that try to go further by aligning the analyis processes with marketing-specific business cases.
  • The first example consist in monitoring the status of a market by obtaining the state of decision of consumers regarding the acquisition of a productSpecfifically analyse social media opinions about brands and align social media users with states of the Consumer Decision JourneySuch states are the following:AWARENESS:Theconsumer considers an initial set of brands, based on brand perceptions and exposure to recent touch pointsEVALUATION: Consumers add of subtract brands as they evaluate what they wantPURCHASE: Ultimately, the consumer selects a brand at the moment of purchasePOST-PURCHASE: After purchasing a product or service, the consumer build expectations based on experience to inform the next decision journeyWe combine the detection of the stage in the Consumer Decision Journey with a socio-demographic segmentation of social media users
  • This slide presents example visualisations of the application being developed.The picture on the left reflects the volume of users that are on a particular stage segmented by place of residence.The picture on the upper right corner shows the distributions of consumers by several dimensions (e.g., age, gender, sentiment, location, …).The picture on the lower right corner shows the evolution of the distribution of consumers by stage in the consumer decision journey.
  • Anotherexampleapplicationis Social CRM.ChallengeHandling thousands of brand consumers in social mediaCommunity managers cannot handle every social media userTimelines are hugeApproachDealing with communities instead of dealing with consumersDetecting clusters of consumersFocusing communication on relevant consumersDetecting user roles (opinion leaders, influencers, …)Discovering information propagation pathsBig graph analysis techniquesThousands of brand consumers (nodes)Millions of relationships (friend of, follows, …)
  • The last application we want to show is one used for measuring the performance of communiction.ChallengeMeasure how marketing campaigns (paid media) influence in word of mouth (earned media)ApproachGenerate time series for buzz and advertising pressureApply time series data mining techniquesDetecting events on time seriesFind explanations to the events detected with trending topicsCorrelate buzz and advertising time series (thousands of tests)
  • The third key aspect a company should focus are human resources and methodologyIn our opinion, preparing for BIG data applications is not only about technologyThe apropriate skills must be found, and the approprite workflow must be implmemented
  • Big Data and Marketing Technology

    1. 1. #amecsummitWiFi AccessUser name:Password:#amecsummitWiFi AccessUser name:Password:
    2. 2. #amecsummitWiFi AccessUser name:Password:#amecsummitWiFi AccessUser name:Password:Óscar Muñoz-GarcíaProject Manager, Havas Media Group
    3. 3. #amecsummitWiFi AccessUser name:Password:#amecsummitWiFi AccessUser name:Password:Big Data and Marketing TechnologyChallenges and Case Studies for Media Agencies
    4. 4. #amecsummitWiFi AccessUser name:Password:What are the 3 things that a company needs to focus on?1. TechnologyHuge volume of heterogeneous dataHeavyweight data analysis proceses
    5. 5. #amecsummitWiFi AccessUser name:Password:Data SourcesSocial MediaHighly unstructured / HeterogeneousFine grained (seconds, posts/events)~ 1,200 M posts/year (1.56 TB)~ 0.5 M social ad events/year (250 GB)Digital Display AdvertisingStructured / HeterogeneousFine grained (seconds, events)~ 145,000 M ad serving events (11 TB)Search Engine MarketingStructured / HeterogeneousFine grained (seconds, events)~ 191 M search events (20 GB)Site AnalyticsStructured / HeterogeneousData aggregated (daily, weekly, monthly)~ 51 M records (11 GB)Off-line AdvertisingStructured / Highly HeterogeneousData aggregated (minutes, hourly, daily,weekly, monthly, …)Not So Big Data(but require to be integrated with the restof data sources)Customer data(CRM, Sales, Visits to Store, …)Structured? / Highly HeterogeneousFine grained and aggregatedRequires to be integrated with the rest ofdata sources)
    6. 6. #amecsummitWiFi AccessUser name:Password:Data Sources Data Heterogeneity From structured to unstructured formats Formats non-standardised Different levels of aggregation From fine-grained posts and events To time-series aggregated at different time periods (every second, hourly, daily, etc.) Different data sizes Big Data vs. “not so big” Data (both useful) Data Integration is a challenge Even for the same kinds of data (Twitter vs. Facebook, Google vs. Affiliate Networks)
    7. 7. #amecsummitWiFi AccessUser name:Password:Example Applications Brand Reputation Monitoring Market Research Social CRM Measuring the Performance of Communication Supporting Digital Media Planning and Buying
    8. 8. #amecsummitWiFi AccessUser name:Password:What are the 3 things that a company needs to focus on?2. Alignment between technology and specificcustomer needsFrom data observations to actionable knowledgeDeliverables must go beyond basic metrics extracted fromsocial media monitoring
    9. 9. #amecsummitWiFi AccessUser name:Password:SoA: Brand Reputation Monitoring Obtain brand reputation KPIs and monitor its evolution over time Brand Popularity Volume of mentions to a brand in social media Brand Valuation Sentiment of the opinions mentioning the brand Brand Attributes Topics mentioned when talking about the brand
    10. 10. #amecsummitWiFi AccessUser name:Password:Brand Reputation Monitoring (data processing workflow)Content SearchAuthorReputationAnalysisDataWarehousing &QueryContentExtractionUnshortenLinksLanguageDetectionSentimentAnalysisOpinionClippingTopicIdentificationSpamDetectionPOS-Tagging
    11. 11. #amecsummitWiFi AccessUser name:Password:SoA: Brand Reputation MonitoringSo what?Are these basic insights enough?Which is the status of my market by segments ofconsumers?How can I focus CRM in social media on the appropriatetargets?Which is the performance of my advertising campaignson paid media?
    12. 12. #amecsummitWiFi AccessUser name:Password:From brand reputation to market monitoringMcKinsey (2009). The Consumer Decision Journey The Consumer Decision Journey + Socio-demographic Segmentation80% 20%75% 25%GenderAgeLocationPurchase Intention75% 25%
    13. 13. #amecsummitWiFi AccessUser name:Password: Data intensive analysis processes of social media content and metadatacombined with site analytics
    14. 14. #amecsummitWiFi AccessUser name:Password:Social CRM Challenge Handling thousands of brand consumers in social media Community managers cannot handle every social mediauser Approach Dealing with communities Focusing communication on relevant consumers Discovering information propagation paths Big graph analysis techniques Thousands of brand consumers (nodes) Millions of relationships (friend of, follows, …)
    15. 15. #amecsummitWiFi AccessUser name:Password:Measuring the Performance of Communication Challenge Measure the influence of paid mediaover earned media Approach Generate time series for buzz andadvertising pressure Detect events on time series Find explanations to the events Correlate buzz with advertisementpressureBrand popularityCampaign GRPs
    16. 16. #amecsummitWiFi AccessUser name:Password:What are the 3 things that a company needs to focus on?3. Human Resources & MethodologyIt is only technology?Skills & Workflow
    17. 17. #amecsummitWiFi AccessUser name:Password:Cannot be automatizedBusiness objectives andapplication success criteriamust be defined by theOrganization Strategist andCustomersBIG Data applicationrequirementsCross Industry Standard Process for Data MiningBusiness Understanding
    18. 18. #amecsummitWiFi AccessUser name:Password:Data UnderstandingCannot be automatizedDomain Experts and DataAnalystsTo become familiar with the dataTo identify data quality problemsand solutionsTo asses that the data is valid forachieving business objectivesCross Industry Standard Process for Data Mining
    19. 19. #amecsummitWiFi AccessUser name:Password:Data preparationCan be automatizedExamples: Remove spam from content gathered Filter content according to itslanguage Filter content according to is context,etc.Application Developers BIG Data Warehousing skills (e.g.,HIVE, PIG, …) AI skills (e.g., NLP)
    20. 20. #amecsummitWiFi AccessUser name:Password:Modeling Can be automatized Application Developers with expertise inData Mining and AI Data mining frameworks Machine learning classifiers Clustering techniques Statistical analysis tools …
    21. 21. #amecsummitWiFi AccessUser name:Password:Evaluation Cannot be automatized Data Scientists are required to validatethe models Assessing the correctness of the model Validating that a correlation found implycausality Assessing that the sample used isrepresentative …
    22. 22. #amecsummitWiFi AccessUser name:Password:Deployment Cannot be automatized Knowledge obtained must be translatedinto a final report aligned with businessfor consumers Data insights enhanced by Consultantswork From observations to decision support From KPIs to recommendations