Winning campaign catalysts
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An illogical war against third-party data is brewing; however, it’s selling more than ever before. By leveraging third-party data with the power of first-party data, outcomes are always improved and marketers using that knowledge are creating distinct competitive advantages. They are doing things now that were impossible just two years ago. The Oracle Data Cloud represents as much as two-thirds of the third-party data transacted within the data ecosystem and the volume increases when you include first-party data that runs through our pipes. It’s crystal clear that first- and third-party data are making effective campaigns a recurrent reality. During this session, we’ll get you up to speed on why the only direction is up for this rocket-ship industry.
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Winning campaign catalysts
- 1. #THEDATASUMMIT17 | @ORACLEDATACLOUD Winning Campaign Catalysts Robin Opie VP Data Science Oracle Data Cloud @RobinOpie
- 2. Demographic Data Cookie Mobile ID Individual Household Partner Data 1,500 Web Data Partners 46,000 Pre-Build Audiences 15MM Websites (worldwide) 100B 1st Party Data Points/Month Largest Offline-Online Transaction Data Source Census Level Cross Device Data 7.5Tdata points collected monthly $3Tin consumer spending Copyright © 2017, Oracle and/or its affiliates. All rights reserved. 2
- 3. Copyright © 2017, Oracle and/or its affiliates. All rights reserved. 3 Data Science Value=+ Copyright © 2017, Oracle and/or its affiliates. All rights reserved. 3
- 4. ValueID Graph Measurement Targeting Science+Data Copyright © 2017, Oracle and/or its affiliates. All rights reserved. 4 = = =
- 5. Let’s talk about reach % Total Online Users Reached %CategoryBuyersReached 0 100 0 100 Broad Reach Strategy TARGETING Copyright © 2017, Oracle and/or its affiliates. All rights reserved. 5
- 6. % Total Online Users Reached %CategoryBuyersReached 0 100 0 100 Typical reachof new carbuyers among auto advertisers 85% 5% 10% Broad Reach Strategy 10% 25% 70% TARGETING Copyright © 2017, Oracle and/or its affiliates. All rights reserved. 6
- 7. Demo Segments Building a‘Women’s Apparel Buyers (Category)’ Audience 7 ‘All’ Segments Model Copyright © 2017, Oracle and/or its affiliates. All rights reserved. TARGETING Copyright © 2017, Oracle and/or its affiliates. All rights reserved. 7
- 8. Building ‘Category Buyers:SpecialtyWomen’sApparel’ 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% % of Total Households Reached 50 100 150 200 PerformanceIndex (vs.NationalAverageBuyRate) 0 0% TARGETING Copyright © 2017, Oracle and/or its affiliates. All rights reserved. 8 Validated Demo (21-29) -- 2.6% of HH’s @ 133 Index Validated Demo (45-49) -- 1.8% of HH’s @ 127 Index Reach more than 20% of the US with Segments that all index > 100 Demo Segment
- 9. Building ‘Category Buyers:SpecialtyWomen’sApparel’ 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% % of Total Households Reached 0% TARGETING Copyright © 2017, Oracle and/or its affiliates. All rights reserved. 9 Senior Fashions -- 3.2% of HH’s @ 178 Index The North Face -- 5.6% of HH’s @ 162 Index Reach more than 40% of the US with Segments that all index > 100 How many category buyers did I fail to reach? 50 100 150 200 0 PerformanceIndex (vs.NationalAverageBuyRate) All Segment
- 10. 50 100 150 200 0 PerformanceIndex (vs.NationalAverageBuyRate) Building ‘Category Buyers:SpecialtyWomen’sApparel’ 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% % of Total Households Reached 20% 40% 60% 80% 100% %ofTotalBuyersReached 0% 0% Demo Segment Approach TARGETING Copyright © 2017, Oracle and/or its affiliates. All rights reserved. 10
- 11. Building ‘Category Buyers:SpecialtyWomen’sApparel’ 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% % of Total Households Reached 20% 40% 60% 80% 100% %ofTotalBuyersReached 0% 0% Demo Segment Approach TARGETING All Segment Approach Copyright © 2017, Oracle and/or its affiliates. All rights reserved. 11
- 12. Building ‘Category Buyers:SpecialtyWomen’sApparel’ 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% % of Total Households Reached 20% 40% 60% 80% 100% %ofTotalBuyersReached 0% 0% Demo Segment Approach TARGETING All Segment Approach Model (All Data) Approach Copyright © 2017, Oracle and/or its affiliates. All rights reserved. 12
- 13. Building‘CategoryBuyers:SpecialtyWomen’sApparel’ TARGETING Copyright © 2017, Oracle and/or its affiliates. All rights reserved. 13 …The New Possible: Same Volume. 3X The Buyers
- 14. CPG: Choose the right audience todrive morelift TARGETING Copyright © 2017, Oracle and/or its affiliates. All rights reserved. 14 …The New Possible: Same Campaign. 80% More Lift
- 15. 32%Lower CPC 8%Lower CPA TARGETING ODC Data 10XSize Edmunds® Copyright © 2017, Oracle and/or its affiliates. All rights reserved. 15
- 16. ValueID Graph Measurement Targeting Science+ =Data Copyright © 2017, Oracle and/or its affiliates. All rights reserved. 16 = =
- 17. ID Graph Measurement Targeting Lower media cost Credible measurement Oracle Data Cloud capabilities work together to compound value Copyright © 2017, Oracle and/or its affiliates. All rights reserved. 17 More touchpoints More connections More data for every model Questionable ID Graph Questionable measurement Credible ID Graph
- 18. Copyright © 2017, Oracle and/or its affiliates. All rights reserved. 18 ID GRAPH Cookie #1 Individual IP Address Cookie #2 LocationEmail OneOracleIDGraph. Multiple‘ConnectionLenses’. Household Mobile ID #1
- 19. Copyright © 2017, Oracle and/or its affiliates. All rights reserved. 19 ID GRAPH Cookie #1 Individual IP Address Cookie #2 LocationEmail OneOracleIDGraph. Multiple‘ConnectionLenses’. HouseholdHousehold Mobile ID #1 Mobile ID #1
- 20. Copyright © 2017, Oracle and/or its affiliates. All rights reserved. 20 ID GRAPH Household Mobile ID #1 OneOracleIDGraph. Multiple‘ConnectionLenses’. The New Possible: Know the ‘Quality’ of Each and Every Connection
- 21. ValueID Graph Measurement Targeting Science+ =Data Copyright © 2017, Oracle and/or its affiliates. All rights reserved. 21 = =
- 22. ID Graph Measurement Targeting Oracle Data Cloud capabilities work together to compound value • Through measurement, we discovered that the industry’s ID Graph was broken (& started fixing it) years before others even knew there was a problem. • We measure what is most important to our clients so that we can improve what is most important to our clients. Copyright © 2017, Oracle and/or its affiliates. All rights reserved. 22
Editor's Notes
- Slide setup: First, for any marketing campaign to be effective, you need to reach the right audience. The right offer and the right creative simply can’t overcome the wrong audience. For example, my kids are X and Y, you simply can’t sell me diapers anymore, no matter what the discount is or how cute the baby and surrounding puppies. A mechanism we use here at ODC to evaluate “the right audience” is this graph, demonstrating the trade-off of total online users reached (left to right) with the percentage of total relevant buyers reach (up and down). We typically define “relevant buyers” as purchasing a certain type of clothing across retailers, shopping for a certain segment of vehicles, or a category at a grocery store. As a start, I would like to point out that reaching all of the online users does not reach all of the category buyers for most of the brands we measure. Joke: The good news is … even though we can’t reach everyone that matters online - 100% of publishers believe it is the right strategy to try. Seriously though, we’ve seen some publishers recently claim that the best strategy to reach relevant consumers is to hit most of the US. Its pretty easy to see through that.
- Let’s take a look at Automotive. Point 1: Many of the pubs we work with have told us that advertisers need to target massive audiences (50MM, 75MM, even 100MM HH’s) to reach their campaign goals. I want to take a moment to dispel this ridiculous myth: In automotive, for example, fewer than 9% of campaigns reach more than 30% (No longer made up) of the HH’s in the US. As much as the pubs would like it – the typical campaign doesn’t have enough budget to target the entire United State. Now, to the graph at hand, this represents about 150+ campaigns from our auto advertisers. You can see the automotive manufactures, through the use of Endemic publishers, audience targeting, etc tend to do somewhat better than random at reaching new car buyers. But on average… its not that much better. And put quite frankly, that’s unacceptable given what’s possible with data & analytics.
- CPG has been the most successful vertical for Datalogix and the Oracle Data Cloud, and there is a very good reason for this. When you know what CPG products someone has purchased in the past, you can do a much better job of getting the right offers in front of them. And the result is that ads work almost twice as well. So if data adds less than 80% to the cost of the media, this is a great ROI – and you should use more data!
- EDMUNDS TARGET EXAMPLE: The combination is powerful … assuming the product meets a market need & the sales team is effective (I love being on this team) – our experiencTargeting is the Lifeblood of our business… Most in industry start with an interesting data set, build modeling software to use it, and then sell audiences Experian is a good example … they have demos and use them to model pretty much everything. Old Datalogix was similar: We used offline purchase data to model all sales. We realized we needed to change … added demos Access to BK Segments & AT data (by far the most formidable online data sets available) made us rethink M360 is born Here is an example of how it worked … Edmunds e has been that the company with the best data & science will win the day.
- Our 3 Primary Product areas are built to help each other MEASUREMENT: - helps targeting - was the reason we got to work on ID Graph ID GRAPH: - drives targeting revenue (Imagine creating and audience and – instead of connecting it to 1 browser per HH, connecting it to all of the browsers that are connected to that HH. - Was necessary for Open Web Measurement to work. Our clients want us to compare across Digital Providers … even across channels into areas like TV … Simply not possible without a good ID graph.