Jason Juma-Ross - Accenture

652 views

Published on

1 Comment
1 Like
Statistics
Notes
  • Thank you, Jason! Great to have the presentation and the notes here. Much appreciated!
       Reply 
    Are you sure you want to  Yes  No
    Your message goes here
No Downloads
Views
Total views
652
On SlideShare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
17
Comments
1
Likes
1
Embeds 0
No embeds

No notes for slide
  • Performance-based shifts in marketing spendExample: deltas for APAC advertisers based on MMM projectsExample: shift in Australia spend from Nielsen data or IAB dataQuestion: how do you do digital well?First, you establish baseline effectiveness…
  • Performance-based shifts in marketing spendExample: deltas for APAC advertisers based on MMM projectsExample: shift in Australia spend from Nielsen data or IAB data
  • Media landscape polarisationThe media landscape is polarising into those that invest and execute well in digital and those that do notA key issue is that digital is a relatively unknown process so effectiveness varies
  • Execution is criticalTremendous variation within channels. How many standard deviations in ANZ Banner CTR dataCTRs vary across media types
  • Integrated: maximise data and reach synergies across media typesIntelligent: optimise full conversion paths for each visitor with full accountabilityIndustrialized: leverage high levels of automation and machine decisioningThe result: de-averaged, scalable digital marketing
  • How to deliver relevance @ scale & speed- Automation platforms- Data-driven decisioning & machine learning- Scale, low cost message & creative developmentDigital & direct processes & mindset
  • Jason Juma-Ross - Accenture

    1. 1. Agile, intelligent marketingJason Juma-Ross@ideasocAdTech Sydney, 2013Copyright © 2013 Accenture All Rights Reserved. Accenture, its logo, and High Performance Delivered are trademarks of Accenture.
    2. 2. 2013: another digital media watershed$000s 2012 Australian Media Spend & YOY Δ ($24m)4,000,000 $516m3,000,000 ($476m)2,000,0001,000,000 ($18m) $21m $11m $8m ($41m) 0 Television Press Magazines Radio Cinema Out of Home Direct Mail DigitalROIBasis > > Historical Marginal OptimalSource: AQX Monthly, Jan 2011 – Jan 2013, IAB Online Advertising Expenditure Report, Dec 2012Copyright © 2013 Accenture All Rights Reserved. 2
    3. 3. 2013: another digital media watershed$000s 2012 Australian Media Spend & YOY Δ ($24m)4,000,000 $516m3,000,000 ($476m)2,000,0001,000,000 ($18m) $21m $11m $8m ($41m) 0 Television Press Magazines Radio Cinema Out of Home Direct Mail DigitalROIBasis 25% 8%(example) (2%) (28%) (60%) (10%)Source: AQX Monthly, Jan 2011 – Jan 2013, IAB Online Advertising Expenditure Report, Dec 2012, Accenture analysis: APAC region.Copyright © 2013 Accenture All Rights Reserved. 3
    4. 4. Two types of organisations; two types ofmarketing processes 20,000 ANZ Bank 18,000 16,000 Toyota 14,000Interactive (Online & Direct) Spend ($ 000s) AMEX Relatively NAB unknown: 12,000 iterative Origin Telstra 10,000 Westpac Optus MEAN: 9% 8,000 VW CBA Hyundai 0 20,000 40,000 6,000 60,000 80,000 100,000 120,000 Holden MEDIAN: Coles 4% 4,000 Relatively known: stable process Woolworths 2,000 McDonalds Target 0 Reckitt B. Broadcast (non-interactive) Spend ($ 000s) Harvey NormanSource: AQX Monthly, Feb 2012 – Jan 2013 Top 10 Traditional Top 10 Digital & InteractiveCopyright © 2013 Accenture All Rights Reserved. 4
    5. 5. Marketing performance varies enormouslyacross digital campaigns • Return (campaign performance) varies enormously – CTR median of 0.0005, range of 0.05 (100 times the median) – Skewed distribution. Only a Campaign CTR few, very high CTRs in the >1% range – Base scenarios becoming more costly (having lower ROI) • Cost (CPX) is even more varied How can we consistently improve marketing performance in the ‘relatively unknown’ territory of Media CPX digital?Source: Accenture analysis, example digital campaigns aggregate 12m dataCopyright © 2013 Accenture All Rights Reserved. 5
    6. 6. Use ‘fast transients’ to deliver relevance atscale & speed Search Display Media KWG 1 KWG 2 KWG N Seg 1 Seg 2 Seg N de-averaging Dynamic Landing Pages Product/Detail Pages • Integrated data • Intelligent conversion paths Checkout / Conversion • Industrialised automation & decisioningCopyright © 2013 Accenture All Rights Reserved. 6
    7. 7. Delivering relevance has a high cost ofcomplexityCustomers demand a more granular and continuous content and functionalitydevelopment cycle than is possible in the current paradigm New Paradigm Complexity Dimensions Platform based, componentised dev., 5 Cust. Segments 12,150 Treatments flexible architecture with analytics linking 2,430 content, usage, and 3 Channels value + Relevance 6 Regions 810 Current Paradigm 135 Monolithic web 27 Brands development & digital Current supply chain. Analytics 5 Treatments used primarily for 5 Product categories reporting purposes 1 Treatment Unit Delivery Cost +Copyright © 2013 Accenture All Rights Reserved. 7
    8. 8. Known user profiling to drive contenttargeting at the last millisecond Profile Data Context Cloud Digital Data Warehouse Repository Onsite Behaviours Demographic Data Social Profiles Custom Data StoresNB. Example ‘Context Cloud’ from Adobe CQ5Copyright © 2013 Accenture All Rights Reserved. 8
    9. 9. Intent can be estimated through combiningunknown user context data Environmental Variables • IP address Referrer Variables • Country of origin • Referring domain Site Behaviour Variables • Time zone • Campaign ID • Customer/prospect • Operating system • Affiliate • New/return visitor • Browser type • PPC • Previous visit patterns • Screen resolution • Natural search • Previous Product • Direct/bookmark interests – top level • Previous Product interests – low level Temporal Variables • Searches • Time of day • Previous online • Day of week purchases • Recency • Previous Campaign exposure • Frequency • Previous Campaign responses Highly Predictive Anonymous Profile For Testing Offline VariablesCopyright © 2013 Accenture All Rights Reserved. 9
    10. 10. Delivering individual relevance: no averageuser experienceCopyright © 2013 Accenture All Rights Reserved. 10
    11. 11. Copyright © 2013 Accenture All Rights Reserved. 11
    12. 12. Lagging Leading Emerging STATIC ‘ONE-SIZE FITS PERIODIC, EMPIRICALLY- AGILE, INTELLIGENT ALL’ WEB SITES DRIVEN ITERATION DELIVERY Search Social Display Personalisation (HTML) Web Skin Core Systems Componentised (Aligned) (HTML Layer) Architecture Search Social Display Foundational Components Intelligence Driven (Analytics) Intelligent Adaptation Analytics Applications, (Reporting) transactional, and service platforms Customer Data CloudCopyright © 2013 Accenture All Rights Reserved. 12
    13. 13. Relevance = business de-averaged 1960 1980 2000 2020 Broadcast Paradigm Intent Paradigm Campaigns Demand Campaign Demand Profile Profile Population Population Demog. Simple Single Uniform Intent Multiple Fragmented Campaign Segment Offer Channel Campaign Segments Offers Channels Relevance Local Mobile Social Bundle Search Web eDM/DM IPTV, etc Hindsight based business Relevance, scale, & speedCopyright © 2012 Accenture All Rights Reserved. 13
    14. 14. Thank youJason Juma-RossRegional Managing DirectorAccenture Interactivejason.juma-ross@accenture.com@ideasocCopyright © 2012 Accenture All Rights Reserved. 14

    ×