Analysing Your Marketing Mix Through Actionable Metrics Within A Fair Attribution Model
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Analysing Your Marketing Mix Through Actionable Metrics Within A Fair Attribution Model

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The presentation is from a keynote speech at the Online Retail Summit in May 2013 combining lean analytics with google analytics attribution to better understand the online marketing mix and to learn ...

The presentation is from a keynote speech at the Online Retail Summit in May 2013 combining lean analytics with google analytics attribution to better understand the online marketing mix and to learn from a scientific approach. The presentation plus further readings and excel examples can also be downloaded from www.mobile-future.com.au. Bjorn Behrendt is a serial entrepreneur and co-founder of hiogi, StyleTread and ParcelPush. He lives with his family in Sydney, Australia.

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Analysing Your Marketing Mix Through Actionable Metrics Within A Fair Attribution Model Analysing Your Marketing Mix Through Actionable Metrics Within A Fair Attribution Model Presentation Transcript

  • BJORN BEHRENDT, EXAMPLES & RESEARCH FILES ON MOBILE-FUTURE.COM.AUPAGE 1ANALYSING YOUR MARKETING MIXTHROUGH ACTIONABLE METRICSWITHIN A FAIR ATTRIBUTION MODELONLINE RETAIL SUMMIT 2013, HUNTER VALLEY
  • PAGE 2© MOBILE-FUTURE.COM.AUAGENDABACKGROUNDBASICSACTIONABLE METRICSLEAN ATTRIBUTION MODELCOOKBOOKGOOGLE ANALYTICS
  • PAGE 3© MOBILE-FUTURE.COM.AUMYBACKGROUNDKPMGEBAY4SXSHIOGISTYLETREADPARCELPUSH• STYLETREAD LAUNCHED 10/12MODELLED AROUND ZAPPOS• $19M IN FUNDING (NINEENTERTAINMENT, STARFISH,LAKESTAR…)• 750K-1M MONTHLY VISITORS• 500K+ DATABASE• TRADE SALE FEB 2013• LAUNCH OF KILLCOM.COM 3/13• INSERT MARKETING NETWORKFOR ONLINE RETAILERS• LAUNCHED 01/13• 250K MONTHLY E-COMMERCEPARCEL INSERTS• 2M MONTHLY CAPACITY
  • PAGE 4© MOBILE-FUTURE.COM.AUTHREEDIFFERENTTYPESACROSSMULTIPLESCREENSACROSS DIGITALCHANNELSATTRIBUTIONMODELSONLINE TOSTORE
  • PAGE 5© MOBILE-FUTURE.COM.AUDEFINITIONSMARKETING ATTRIBUTION IS THE PRACTICE OFDETERMINING THE ROLE THAT CHANNELS PLAYIN INFLUENCING THE CUSTOMER JOURNEYTOWARDS CONVERSION GOALS.ATTRIBUTION MODELS AIMS TO GIVE EACHCHANNEL A FAIR ROI VALUE.ATTRIBUTION OFFERS THE ABILITY TOUNDERSTAND HOW MARKETING IMPACTS THEENTIRE SALES CYCLE AND TO OPTIMIZEACROSS IT.*PARTLY FROM 2012 ECONSULTANCY“MARKETING ATTRIBUTIONVALUING THE CUSTOMER JOURNEY”
  • PAGE 6© MOBILE-FUTURE.COM.AUSURVEYRESULTSBudgetincrease of21%in ALLchannels54% ofbusinesses carryout some form ofattributionBut of thosewho do,89%say attributionhad a positiveimpact on theirbusinessof whom28%use lastclickWWW.GOOGLE.COM/THINK/WEBINARS/BUILDING-BLOCKS-DIGITAL-ATTRIBUTION.HTMLADOBE ATTRIBUTIONINFOGRAPHIC
  • PAGE 7© MOBILE-FUTURE.COM.AUBENEFITS OFATTRIBUTION**2012 STUDY BY ECONSULTANCY“MARKETING ATTRIBUTIONVALUING THE CUSTOMER JOURNEY”
  • PAGE 8© MOBILE-FUTURE.COM.AUBARRIERS TOATTRIBUTION /KEYS TOSUCCESS*2012 STUDY BY ECONSULTANCY“MARKETING ATTRIBUTIONVALUING THE CUSTOMER JOURNEY”
  • PAGE 9© MOBILE-FUTURE.COM.AUCHANGES TOCHANNELINVESTMENTRESULTINGFROMATTRIBUTION*2012 STUDY BY ECONSULTANCY“MARKETING ATTRIBUTIONVALUING THE CUSTOMER JOURNEY”
  • PAGE 10© MOBILE-FUTURE.COM.AUAGENDABACKGROUNDBASICSACTIONABLE METRICSLEAN ATTRIBUTION MODELCOOKBOOKGOOGLE ANALYTICS
  • PAGE 11© MOBILE-FUTURE.COM.AUATTRIBUTIONMODELS AREMORE THAN…SPEND PER CHANNEL/ CONVERSIONS PER CHANNEL= CPO PER CHANNEL…AIDA MODEL,ENGAGEMENT,LOYALTYCROSS-CHANNEL IMPACT EXAMPLES:• „DISPLAY‟ AND „TV‟ CREATE A NEW BUYINGINTENT AND A DESIRE WHILE INCREASINGBRAND AWARENESS.• „SEM‟ AND „SEO‟ ALSO CREATE BRANDAWARENESS, BUT MAINLY REACT TO ANEXISTING INTENT/DESIRE (SEARCH QUERY)OFTEN RESULTING IN AN ACTION.• „DIRECT‟ IS A RESULT OF ALL OF THE ABOVE.• WHICH CHANNEL MOVES CUSTOMERS MOREEFFICIENTLY FROM BEING INTRODUCERS TOINFLUENCERS TO CLOSERS…?!
  • PAGE 12© MOBILE-FUTURE.COM.AUCUSTOMERJOURNEY:ATTRIBUTINGBASED ON LASTCLICK ISSURELYNOT FAIRWEBSITE$1 $2V1,OS,DMMYYYYTTSS,CPC$ | V2,SEM,DDMMYYYYTTSS,CPC$ |V3,DP,DDMMYYYY,CPC$ | V4,SEM,DDMMYYYYTTSS,CPC$ |CUSTOMER_ID | GENDER | MOBILE_APP_DOWNLOAD | APP_EVENT |ORDER_ID, DDMMYYYYTTSS, $XXX.XX , PRODUCT_ID|V5,EM,DMMYYYYTTSS | V6,SEM,DDMMYYYYTTSS,CPC$ |V7,DI,DDMMYYYYTTSS | TV_AD, CHANNEL |ORDER_ID, DDMMYYYYTTSS, $XXX.XX, PRODUCT_ID |
  • PAGE 13© MOBILE-FUTURE.COM.AUAGENDABACKGROUNDBASICSACTIONABLE METRICSLEAN ATTRIBUTION MODELCOOKBOOKGOOGLE ANALYTICS
  • PAGE 14ACTIONABLE , ACCESSIBLE AND AUDITABLETHE THREE A‟S OFMETRICS
  • PAGE 15© MOBILE-FUTURE.COM.AUCONVERSIONFUNNEL:ACTIONABLEPERCENTAGESMATTERE.G. HYPOTHESIS:ACTIVATION RATEFROM 1ST TIMEVISITORS TO APAYING CUSTOMER ISHIGHER IF SOMEONEWATCHED A PRODUCTVIDEO (EVENT)ACQUISITIONACTIVATIONRETENTIONREFERRALREVENUE%%%%% Event% Event% Event% Event% Event % Event% Event% Event
  • PAGE 16…IT IS MOST LIKELY A VANITY METRICIF “A CURRENT AFFAIR”INCREASES THEMETRIC…
  • PAGE 17© MOBILE-FUTURE.COM.AUACTIONABLECONVERSIONMETRICSMetric Customer Behaviour ConversionRate (% ofuniquevisits)EstimatedValueAcquisition Rate 1 Spends x time on site and yviews onsite60% $0.10Activation Rate 2 Relevant 1st visit (x clicks, yviews, z time on site)25% $0.40Activation Rate 1 Email Signup 4% $2.50Activation Rate 2 Account Signup 2% $3.00Retention Rate 1 Email opened 4% $0.75Retention Rate 2 Repeat visit (x visits in ydays)3% $2.00Referral Rate 1 Referred a user who visitedsite4% $6.00Referral Rate 2 Referred a user whoactivated3% $9.00Revenue Rate 1 Customer orders 4% $12.00Revenue Rate 2 Customer orders repeatedlyreaching relevant CLTV2% $28.00E.G. HYPOTHESIS:THE CLTV AND REFERRALRATE OF A CUSTOMERWHO HAS SEEN THE TVC ISX TIMES HIGHER THAN THEAVERAGE
  • PAGE 18© MOBILE-FUTURE.COM.AUIDEAS FORGOALS ANDEVENTS• CONVERSION / PURCHASE• NEWSLETTER SUBSCRIPTIONS• FREE TRIAL SUBSCRIPTIONS• UPGRADES• QUOTE REQUESTS• DOWNLOADS• WATCHING A VIDEO ABOUT A SERVICE/PRODUCT• VIEWING 5+ PAGES ON THE WEBSITE• VIEWING 10+ PAGES ON THE WEBSITE• FACEBOOK LIKES / GOOGLE+• JOB APPLICATION FORMS• CONTACT FORMS• IN-APP PURCHASES
  • PAGE 19© MOBILE-FUTURE.COM.AUCOHORTSANDFUNNELANALYSISSOURCE:BOOK BY ERIC RIESTHE LEAN STARTUP
  • PAGE 20© MOBILE-FUTURE.COM.AUAGENDABACKGROUNDBASICSACTIONABLE METRICSLEAN ATTRIBUTION MODELCOOKBOOKGOOGLE ANALYTICS
  • PAGE 21THE LEAN STARTUP PROCESS - DIAGRAM
  • PAGE 22THE MINIMUM VIABLE CAMPAIGN IS THAT VERSION OF A NEWMARKETING CAMPAIGN WHICH ALLOWS A TEAM TO COLLECT THEMAXIMUM AMOUNT OF STATISTICAL RELEVANT RESULTS WITH THELEAST EFFORT, LEAST TIME AND THE LEAST BUDGET POSSIBLE.MVC
  • PAGE 23AND AVOID THE LARGE BATCH DEATH-SPIRALWORK IN SMALLBATCHES
  • PAGE 24© MOBILE-FUTURE.COM.AUITERATE,ITERATE,ITERATE,THEN PIVOTCUSTOMER SEGMENT PIVOT: HYPOTHESIS MIGHT BECONFIRMED FOR THE CHANNEL, BUT AN UNEXPECTED USERGROUP REACT => MIGHT LEAD TO CHANGES IN LANGUAGE,GRAPHICS, CLTV, BUDGET…ZOOM-IN PIVOT: WHAT WAS CONSIDERED TO BE ANASSISTING OR SUPPORTING CAMPAIGN BECOMES THE MAINCAMPAIGN.ZOOM-OUT PIVOT: TEST AROUND A CERTAIN MEDIUM /CHANNEL MIGHT NOT HAVE BEEN ENOUGH TO VALIDATE AHYPOTHESIS AND MORE CHANNELS MIGHT NEED TO BEADDED.PLATFORM PIVOT: A TECHNOLOGY MIGHT PROOF TODELIVER USELESS RESULTS.METHODOLOGY PIVOT: A CERTAIN STATISTICAL APPROACHMIGHT PROOF TO BE IRRELEVANT OR USELESS.MAKE AS MANYMISTAKES ASQUICKLY ASPOSSIBLE TO LEARNFROM THEM
  • PAGE 251. RATE OF NEW CUSTOMER ACQUISITIONS NEEDS TOEXCEED THE CHURN RATE.2. INCREASE REVENUE FROM EACH CUSTOMER.3. IMPROVE RETENTION AND VIRAL COEFFICIENT (VC>1).4. REDUCE ACQUISITION COSTS FOR NEW CUSTOMERS.RULES FOR THESTICKY ENGINE OFGROWTH
  • PAGE 26USE THE5 WHY‟STO GET TO THE BOTTOM OF THINGS…
  • PAGE 27© MOBILE-FUTURE.COM.AUAGENDABACKGROUNDBASICSACTIONABLE METRICSLEAN ATTRIBUTION MODELCOOKBOOKGOOGLE ANALYTICS
  • PAGE 28© MOBILE-FUTURE.COM.AUCOOKBOOKPART I:THE SETUP1. ENSURE YOUR PROMOTIONS ARE MEASURABLE(CAMPAIGN LINKS UTM TAGGED / URL BUILDER).2. ENSURE GOOGLE ANALYTICS IS INSTALLED ONYOUR LANDING PAGES (TAGGING PAGES).3. DEFINE AND SETUP CONVERSION GOALS ANDEVENTS FOR YOUR SITE AND APP (ENGAGEMENTFLOWS, FUNNELS)4. TEST YOUR SETUP5. INSTALLADWORDS CONVERSION TRACKING6. LINK ADWORDS ACCOUNTS7. ENABLE ADWORDS AUTO-TAGGING8. ENABLE TRACKING FOR ALL PAGES ANDSUBDOMAINS (EXCLUDE OFFICE IP)9. COLLECT ORDER DATA (SKU, PRICE)
  • PAGE 29© MOBILE-FUTURE.COM.AUCOOKBOOKPART II:TIPS FOR THEFIRSTITERATION1. DO A CHANNEL BY CHANNEL AUDIT FIRST2. THEN START WITH TWO CHANNELS3. USE ONE TRACKING SYSTEM4. SET COOKIE DURATION5. REDUCE DATA / COMPLEXITY E.G. BY JUSTLOOKING AT 2-3 TOUCH POINTS6. START WITH STANDARD CONVERSIONGOALS7. RUN SPLIT-TESTS AND REGRESSION TESTSTO TEST CORRELATIONS8. ENSURE RESULTS ARE STATISTICALRELEVANT(SEE CALCULATOR MOBILE-FUTURE.COM.AU)
  • PAGE 30© MOBILE-FUTURE.COM.AUCOOKBOOKPART II:USEATTRIBUTIONRATIOS TOCOMPARECAMPAIGNSPLIT-TESTRESULTSREVENUE FROM FIRST TOUCH / REVENUE FROM LAST TOUCH= APPROPRIATE ATTRIBUTION RATIO• CLOSER TO 0.00 = CONVERSION CAMPAIGN• CLOSER TO 1.00 = PERSUASION CAMPAIGN• CLOSER TO 10.00 = ACQUISITION CAMPAIGNAPPROPRIATE ATTRIBUTION NET RATIO = (REVENUE FROMFIRST TOUCH - CAMPAIGN MARKETING COST) / (REVENUEFROM LAST TOUCH – CAMPAIGN MARKETING COST)APPROPRIATE ATTRIBUTION RATIO MARGIN = (REVENUEFROM FIRST TOUCH – CAMPAIGN COGS/ REVENUE FROMLAST TOUCH - CAMPAIGN COGS)SOURCE: ACQUISIO / SEARCH MARKETING NOW“BUILDING AN ATTRIBUTION MODEL THAT WORKS”
  • PAGE 31© MOBILE-FUTURE.COM.AUCOOKBOOKPART III:CALCULATEEFFICIENCYFACTORS PERCHANNEL ANDUSE SIMPLESCORING TODETERMINEFAIRER CPOS(SEE EXCEL EXAMPLE MOBILE-FUTURE.COM.AU)INSPIRED BY QUISMA “INDIVIDUAL CONVERSION ATTRIBUTION”
  • PAGE 32© MOBILE-FUTURE.COM.AUCOOKBOOKPART IV:STARTCRUNCHINGBIGGER DATA1. WORK SCIENTIFICALLY2. HIRE SOME REALLY BRIGHT TALENT (FINANCE ORACCOUNTING BACKGROUND, EXCELLENT EXCELSKILLS)3. UPGRADE YOUR ANALYTICS SOFTWARE OR STARTCOMBINING MORE DATA SOURCES IN ONE BISYSTEM4. INVEST INTO AN AD SERVER5. ADD BEHAVIOURAL PROFILING TO THE MIX6. INTEGRATE DIRECT MARKETING TOOLS ANDSTART AUTOMATING PERSONALISED MESSAGES(EMAIL, ABANDONED CART, RETARGETING…)
  • PAGE 33© MOBILE-FUTURE.COM.AUAGENDABACKGROUNDBASICSACTIONABLE METRICSLEAN ATTRIBUTION MODELCOOKBOOKGOOGLE ANALYTICS
  • PAGE 34© MOBILE-FUTURE.COM.AUVENN DIAGRAMGOOGLE ANALYTICS
  • PAGE 35© MOBILE-FUTURE.COM.AUVENN DIAGRAMWITH SOCIALMEDIAGOOGLE ANALYTICS
  • PAGE 36© MOBILE-FUTURE.COM.AUTOPCONVERSIONPATHSGOOGLE ANALYTICS
  • PAGE 37© MOBILE-FUTURE.COM.AUASSISTEDCONVERSIONSANALYSIS“SUCCESSFULSALE”GOOGLE ANALYTICS
  • PAGE 38© MOBILE-FUTURE.COM.AUPATH LENGTH ININTERACTIONSGOOGLE ANALYTICS
  • PAGE 39© MOBILE-FUTURE.COM.AUTIME LAG INDAYSGOOGLE ANALYTICS
  • PAGE 40© MOBILE-FUTURE.COM.AUGOOGLEANALYTICSATTRIBUTIONMODELINGTOOL**GOOGLE ANALYTICS PLAYBOOK
  • PAGE 41© MOBILE-FUTURE.COM.AUGOOGLEANALYTICSATTRIBUTIONMODELINGTOOL**GOOGLE ANALYTICS PLAYBOOK
  • PAGE 42© MOBILE-FUTURE.COM.AUGOOGLEANALYTICSCUSTOMMODELSGOOGLE ANALYTICS
  • PAGE 43
  • PAGE 44BJORN@MOBILE-FUTURE.COM.AUTWITTER.COM/BBWEB0423 509 161SOURCES & EXAMPLES AS ZIP ONWWW.MOBILE-FUTURE.COM.AU