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Mobile attribution modeling - open analytics nyc
 

Mobile attribution modeling - open analytics nyc

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  • What has this problem led to …?
  • Move fast away from traditional marketing to deliver customized, targeted offers to customers based on their real-time needsThe new customer-centric environment requires personalized products and services Organizations need to move past an intuitive-driven approach to one focused on using data-driven analytics to drive recommends and inform strategy
  • What has this problem led to …?
  • Formalize your presence – I am not an actor playing a chain smoking sex addictIntroduce Yourself – MD of Analytics At Euro WWReiterate Credibility – 10 years of Marketing exp working with numerous S&P100 brands to some of the world’s most creativeState Purpose – here to talk to you briefly about the importance of measurement and analytics and what it can bring to Don JulioTout Guideposts – I have 9 slides and a short video that I plan to take you through
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Mobile attribution modeling - open analytics nyc Mobile attribution modeling - open analytics nyc Presentation Transcript

  • The Channel-lessConsumerTALK ABOUT US USING#FUTUREMIdentifying Attribution within Mobile
  • Michael KaushanskyAnalytics and Insights, Havas Media,michael.kaushansky@havasmedia.com@kaushansky2New Approach to ConsumerCentric Modeling
  • • Deterioration in key performance metrics (leads &sales)• We looked for answers: why wasn’t our existingmedia mix working? Why wasn’t TV, search anddisplay across screens generating the samevolume of leads?• Simulations of existing media mix models pointedto a shift to digital though unclear where. Currentmodels were not reflective of emerging trends inmedia.Our global Auto client had experienced arecent deteriorating trend3
  • • Click to edit Master text styles– Second level• Third level– Fourth level» Fifth levelThe Role of the Consumer Consumer ExpectationsWhat we experienced was a shift in marketingdynamics
  • Adding to the shift - the emergence of themulti-channel marketplace 2 explosive and complementary markets Local Advertising: $35B in 2015 Mobile Commerce: $39B in 2016 Location, Location, Location 70% of mobile revenue tied to location by 2015 40% of mobile search has local intent1) BIA/Kelsey, U.S. Local Media Forecast, 03/20122) eMarketer, US Mobile Commerce Forecast: Capitalizing On Consumers’ Urgent Needs, 01/20123) Forrester Report, US Online Sales Forecast, 20094) BIA/Kelsey, U.S. Local Media Annual Forecast (2010-2015), 06/2011
  • Cross Channel Shopping will be 6X of OnlineRetail aloneSource: Paypal Media Network
  • Multi-channel brings a new complexity to thepurchase funnelSource: Paypal Media NetworkNearly 1/3 of retailers credited smartphoneswith driving traffic to physical stores in 2011,up from 1/5 in 2010-Retail Systems Research, ‘The 21st Century Store: The Search for Relevance”, 6/201115% of US online shoppers made a holidaypurchase via their mobile, growing 3X overHoliday 2010- Baynote, ‘2nd Annual Baynote Holiday Shopping Survey’, 1/2012- IBM, ‘Benchmark December holiday report’, 1/2012
  • The purchase funnel becomes… the purchasepretzelSource: Paypal Media Network
  • Existing model failed to identify the shifttowards digitalInvestmentSalesBlackBoxConsumer journey is uniqueJourney metrics areessential to understand theimpact of media
  • Our objective was clear…update the model! Enhance our existing model to address theunexpected digital shift Approach – divided the journey into stages, definedsuccess metrics across each stage & linked the metricsto show progression throughout the journey Methodology – developed 4 models which explainedkey drivers at each stage, i.e. what drove search, sitetraffic, leads and sales
  • Of Course itwill work
  • We modeled the consumer journey across the5 stagesStage Success MetricAwareness Organic Branded SearchEngagement Page ViewsConsideration Digital LeadsShopping Showroom TrafficPurchase Unit Sales12345
  • Consumer StagesAwarenessEngagementConsiderationShoppingPurchaseHierarchical Modeling explained impact ateach stage4 IndependentECONOMETRICModelsKnown StrongExisting CorrelationMedia MixTVPrintOOHDisplaySearchMobileOnline videoPaid socialSocial
  • We identified 12+ data sources across 52 weeksStructuredEquationSource Data Begins Timing Data Descriptions Metrics AvailableDigital Media (DFA) 2009 daily actual media spend by channel (OLA, SEM) impressions, clicks, hvtsKantar Evaliant 2010 weekly competitor web impressions and spend impressions, spendKantar Stradegy 2011 weekly offline GRP & spend GRP, spendSysomos 2009 weekly social media buzz data buzz volume, sentimentHitwise/Compete last year daily site visitation and duration visitation, durationGoogle Insights 2004 daily total search trends (paid & organic) search trend indexAutodata/Wards 2008 monthly monthly sales data by brand/model/category sales figuresGoogle Analytics/ Omniture 2009 daily Website/model/configurator/RFQ pageviews visits, configs, RFQ, Model PVHarte-Hanks 2001 weekly owners, prospects, email/mail campaign, etc.Urban Science 2009 weekly leads by channel by week leadsManufacturer 2009 weekly dealer visits dealer visitsThird Party Sites 2009 monthly Comp model PV, day/week/month model pageviewsBrand Tracker 2010 Semi attitudinal data by month consideration, intentCensus 2000 monthly economic data by Geo economic dataYt= α + β1Mt + β2Et + β3St + β4Tt + εt, Y (represents the dependent variable, e.g.,sales)M (media), E (engagement), S (searches), T (store traffic), etc.
  • Results As suspected… results pointed mediaconsumption varying at each stage We identified 5 key insights
  • 1. Brand equity dominates & TV is king of paid(36% contribution from TV)StageAwarenessEngagementConsiderationShoppingPurchase
  • 2. More channels at play in driving site traffic,digital media is the workhorse (33%contribution)StageAwarenessEngagementConsiderationShoppingPurchase
  • 3. Paid Search was essential in driving leadsinfluenced by DRTV (paid/Mobile search +DRTV @ 46%)StageAwarenessEngagementConsiderationShoppingPurchase
  • 4. Leads were the most significant driver ofshowroom traffic (40% contribution)StageAwarenessEngagementConsiderationShoppingPurchase
  • 5. Showroom traffic was the best proxy ofunits salesStageAwarenessEngagementConsiderationShoppingPurchase350040004500500055006000650070007500800024002600280030003200340036003800SalesUnitsShow-rooming was responsible for 82% of salesUnitSalesShowroomTraffic
  • Moving the consumer further down journeyincreasingly relies on paid media0%20%40%60%80%100%MediaBase1 2 3 4 5 sales
  • The new models made our simulations moretrue-to-life… (what-if scenarios) identifieddimensioning returns & ROI
  • What we’ve learned…1. Consistently / frequently assess the journey of your consumers2. Digital media is significant in the middle of the journey3. Mobile’s role was stronger than previously thought4. Promotional messaging is effective if consistent at each stage5. Exogenous data (weather & holidays) have minimal impact6. Going dark beyond 5-weeks would be detrimental7. Optimizing media at each stage points to a 12% improvement
  • Ramifications and things you should considerDo YourHomeworkDetermineconsumerbehavior bystage/funnelWeight socialinfluencealong journeySet up formeasurementsuccessEstablish KPIsalongpurchase pathCRM listsPaid, ownedand earnedmediaUnify 3screensCreative +MediaMerge screenidentifiers andinteractionsCrunch dataWhat does ittell you?WeightactivityagainstconversioncontributionOptimizeaccordinglyFutureConsiderationWeight socialinfluencealong thejourneyInvest inapproach &tech to unifyscreens