O2S Effect: a Reality

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Google vision on On line 2 Store effect based on the latest data collected with the Consumer Barometer

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O2S Effect: a Reality

  1. 1. Google Confidential and ProprietaryGoogle Confidential and Proprietary Think Retail, Paris le 28 juin 2011 Mark Riseley, Market Insights Manager, EMEA The O2S Effect, a quantified reality
  2. 2. Google Confidential and ProprietaryGoogle Confidential and Proprietary Online to Store Matters 7.3% 48% 2 Estimated Online Sales as % of total France retail sales 2011* % of Internet users recently researching a retail product online before buying off- line** *Source: Fevad Juin 2011, **Source: IAB/Google/TNS Consumer Commerce Barometer 2010
  3. 3. Google Confidential and ProprietaryGoogle Confidential and Proprietary Source: Longitudinal Economic Study Series, IRI Attitude Link, n = 1,000+ shoppers. CPG Purchase Decisions. IRI, 2009. In store Before visiting store 3 % of consumers who decide what to buy… Impulse buying is declining 60% 83% 40% 17% 2007 2009
  4. 4. Google Confidential and ProprietaryGoogle Confidential and Proprietary Base : Acheteurs en magasin ayant consulté Internet avant ou après achat Q15 : Pour quelles raisons n’avez-vous pas acheté les produits directement sur le site de l’enseigne ? C'est compliqué de retourner le produit 44% Pour le plaisir de flâner dans les magasins 45% J'avais besoin rapidement des produits 50% Le magasin est à proximité de chez moi 50% Le coût de la livraison est trop élevé 51% Essai du produit / conseil Proximité immédiateté Plaisir Je ne suis pas sur(e) d'avoir les mêmes conseils que si j'achete en magasin 37% Je préfère voir ou essayer les produits avant de les acheter 55% Source : TNSInfratest, Consumer barometer 2010 Why do people still buy in store ?
  5. 5. Google Confidential and ProprietaryGoogle Confidential and Proprietary O2S customers search almost as actively as online buyers Source: GfK MEP, Allemagne - Base: Buyers who have researched online Average number of queries Online buyers O2S buyers 9,2 7,6
  6. 6. Google Confidential and ProprietaryGoogle Confidential and Proprietary Top E-commerce and Top O2S products are different Source : TNSInfratest, Consumer Barometer 2010 Top E-commerce Top O2S 1 Computer Software/Video Games Digital Camera/Camcorder 2 Mobile Phone Visual Devices 3 Audio Devices Audio Devices 4 Digital Camera/Camcorder Home Furnishings/Furniture 5 Computer Hardware Domestic Appliances 6 Computer Peripherals Mobile Phone 7 CDs/DVDs/ Books Computer Hardware 8 Visual Devices DIY, Tools, Garden Equipment 9 Domestic Appliances Computer Peripherals 10 Clothing & Accessories Sport Equipment
  7. 7. Google Confidential and ProprietaryGoogle Confidential and Proprietary How to measure the online to store effect 7 ExperimentsTracking Modelling Attribution of offline sales Prove increment al effect Correlation+ of online ads and offline sales Consumer insights Surveys
  8. 8. Google Confidential and ProprietaryGoogle Confidential and Proprietary incremental spend from in-store buyers who researched online. 33% average €701 RESEARCHED ONLINE DID NOT RESEARCH ONLINE Basket size amount Spent in-store Surveys: Online researchers spend more Source: Online2Store Research - Quantifying the role of the web on in-store TV purchases @ FNAC Spain; Sep 2010 €526
  9. 9. Google Confidential and ProprietaryGoogle Confidential and Proprietary Tracking: Cross-channel measurement embedded in Argos business model 25% 48% multi-channel 10% 9% 2% 2%
  10. 10. Google Confidential and ProprietaryGoogle Confidential and Proprietary Experiments: Vodafone example 1 Objective: Create a measurable difference in Adwords spending between test and control regions and determine the impact on in-store sales * Test ran for 8 weeks, beginning 1 April 2010
  11. 11. Google Confidential and ProprietaryGoogle Confidential and Proprietary Experiments: Vodafone example 1 « Vodafone’s additionnal investment in generic and upper funnel keywords was an effective way to drive incremental in-store sales. We now have a better understanding of the impact of online research on retail purchase behaviour and we’ll have to rethink the way our current attribution model works. » -Mike Durbridge, Head of Direct Sales, Vodafone UK
  12. 12. Google Confidential and ProprietaryGoogle Confidential and Proprietary Halo effect Impact of online advertising on offline sales in non-tested categories Up to 3.5% Offline sales lift Increase in offline sales revenues in tested categories 1.5% – 4.4% 12 Experiments: Online advertising drives offline sales Source: Google client studies in Europe & USA
  13. 13. Google Confidential and ProprietaryGoogle Confidential and Proprietary Mobiles & Contracts 4:1 ROAS Apparel 3:1 ROAS Source: Google client studies in Europe & USA Consumer Electronics 12:1 ROAS Home & Garden 6:1 ROAS 13 Experiments:…delivering strong ROI across different categories
  14. 14. Google Confidential and ProprietaryGoogle Confidential and Proprietary Modelling: explaining sales with equations system 1 4 0 1000 2000 3000 4000 5000 6000 20-May-07 20-Jun-07 20-Jul-07 20-Aug-07 20-Sep-07 20-Oct-07 20-Nov-07 20-Dec-07 20-Jan-08 20-Feb-08 20-Mar-08 20-Apr-08 20-May-08 20-Jun-08 20-Jul-08 20-Aug-08 20-Sep-08 20-Oct-08 20-Nov-08 20-Dec-08 20-Jan-09 20-Feb-09 20-Mar-09 20-Apr-09 20-May-09 20-Jun-09 20-Jul-09 20-Aug-09 20-Sep-09 20-Oct-09 20-Nov-09 20-Dec-09 20-Jan-10 20-Feb-10 LaptopSales(Unit) Modeled Actual 98%Le modèle explique des variations dans les ventes
  15. 15. Google Confidential and ProprietaryGoogle Confidential and Proprietary Modelling: PC City ecosystem 1 5 Offline Sales Online Sales Rainfall Holiday Sporting Events Store Refurbishment Competitive Media Traditional Media Web traffic Online Display SEM (Paid Search) Email Online Search Online world   Offline world   Business Outcome + Driver - Driver Legend 15 Price, Base Sales & Seasonality Short term Promo days Width of line indicates size of driver
  16. 16. Google Confidential and ProprietaryGoogle Confidential and Proprietary Modelling: Understand in-store sales drivers 1 6 Base Sales Seasonality Regular price Price promotio n TV Advertising Catalogues Press Radio & OOH Other Web traffic to pccity.es 42% 12% 8% 2% 5% 15% 11% 3% 1% 1% 62% of PC City sales are non marketing drivers 38% of PcCity sales are influenced by Marketing Catalogs and website traffic are the two key in- store sales drivers PCcity.es explains 11% of offline sales – which means nearly 1/3 of all driven by marketing offline sales
  17. 17. Google Confidential and ProprietaryGoogle Confidential and Proprietary 4,100 4,200 4,300 4,400 4,500 4,600 4,700 4,800 - 50,000 100,000 150,000 200,000 250,000 300,000 350,000 WeeklySales(Units) Weekly Media Spend € Increase spend in 4,300 4,400 4,500 4,600 4,700 4,800 - 10,000 20,000 30,000 40,000 50,000 60,000 70,000 WeeklySales(Units) Weekly Media Spend € Modelling: Understand Marketing investments efficiency 1 7 4,100 4,200 4,300 4,400 4,500 4,600 4,700 4,800 - 100,000 200,000 300,000 400,000 500,000 600,000 WeeklySales(Units) Weekly Media Spend € Historical spend Optimal spend Historical spend Optimal spend Optimal spend Historical spend 4,400 4,500 4,600 4,700 4,800 - 10,000 20,000 30,000 40,000 50,000 WeeklySales(Units) Weekly Media Spend € Historical spend Optimal spend An optimal mix of PC City would induce a reduction in TV Investment and an increase in other media catalogues TV Paid search Press Increase spend in Increase spend inDecrease spend in
  18. 18. Google Confidential and ProprietaryGoogle Confidential and Proprietary €1.40 Modelling: Apply the optimal media mix 1 8 Paid Search 9% Catalogues 42% Outdoor 3% Press 3% TV 43% Paid Search 1% Catalogue 32% Outdoor 1% Press 2% TV 64% Last year 23 Aug – 11 Oct 2009 (8 weeks) Optimal Mix for 24th Aug – 12 Oct 2010 (8 weeks) Aggregate Marketing ROI Aggregate Marketing ROI€1.10 With the same global budget, an optimal mix would bring 6% of additionnal revenue Note : Understanding of ROI of display within the media mix was compromised by a lack of data relating to Display investments over the period analysed.O
  19. 19. Google Confidential and ProprietaryGoogle Confidential and Proprietary Campaign impact on store sales: Instead of this…. The Dutch brand Aviko did this
  20. 20. Google Confidential and ProprietaryGoogle Confidential and Proprietary 50% of Aviko TV budget into pre-roll on YouTube 2
  21. 21. Google Confidential and ProprietaryGoogle Confidential and Proprietary Aviko (NL): Cost GRP Pre-roll targeted usage 2 1,0 0,2 1,0 0,5 1,0 0,8 1,0 1,4 1,0 3,2 0 1 2 3 4 5 6 7 8 9 10 TV YT TV YT TV YT TV YT TV YT Net Cost/GRP (indexed against TV) Male  20-­‐30   20-­‐34   20-­‐49   Female  20-­‐49   13+   ~ 1:16 Source: Gfk Media Efficiency Panel + Carat Media facts booklet (TV net cost based on 70% average market discount)
  22. 22. Google Confidential and ProprietaryGoogle Confidential and Proprietary Aviko (NL): Pre-roll targeted usage (size within inventory) Efficiency in driving store purchase 2 1,0 0,6 1,0 1,4 1,0 2,5 1,0 3,7 1,0 8,5 0 1 2 3 4 5 6 7 8 9 10 TV YT TV YT TV YT TV YT TV YT Source: Gfk Media Efficiency Panel + Carat Media facts booklet (TV net cost based on 70% average market discount) Efficiency  in  driving  sales   (indexed  against  TV)   13+   20-­‐49   Female  20-­‐49   20-­‐34   Male  20-­‐34  
  23. 23. Google Confidential and ProprietaryGoogle Confidential and Proprietary 40+ Studies to quantify the O2S Effect §  40+ studies in Europe and in the United States §  Methodologies : econometrics, geo-testing, coupons, tracking, single source panels §  Instore sales driven by the retailer website traffic : 10%-13% §  Case studies : PC City, Auchan, Vodafone, Cadbury, Aviko 2
  24. 24. Google Confidential and ProprietaryGoogle Confidential and Proprietary O2S effect parameters 2 1 Product categories affinity with O2S effect 2 Store network coverage 3 O2S functions on your websites (ex. Store locators, in-store stock availability etc.) 4 Conversion efficiency on your website 5 Your customers’ increasing comfort with e-commerce O2S is amplified by: O2S is minimised by :
  25. 25. Google Confidential and ProprietaryGoogle Confidential and Proprietary Conclusions •  Online to store is much bigger than e-commerce •  O2S shoppers are high value •  Economic benefit is measurable with several methods •  Online ads can deliver 1.5-4.4% offline sales lift •  Online to offline ROAS has been measured from 3:1 to 20:1+ •  We can help you with measurement challenges 2

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