The Party of the Third Part: Third Party Data Lessons Learned Greg Harper & Andrew Latzman. October 2016

Transcription

1 The Party of the Third Part: Third Party Data Lessons Learned Greg Harper & Andrew Latzman October 2016

2 Growing Role for Agency and Client Agency and client as a systems integrator using stable and proven 3rd party data and tools together to generate a highly evolved optimization and insight system

3 What are we going to Discuss? Pain points Building an integrated system that leverages third party data and tools Joining disparate data using unique keys Challenges in development Benefits of this system What s next?

4 Difficulty in Traditional Reporting Piece together separate sets of data that is keyed on independent pieces of information Significant fidelity loss Gaps in alignment or what should be comparable reports Unable to drill into specific parts of the campaign flexibly without regenerating reports from each data source

5 Partnership Primacy and The Hartford partnered and built tracking, measurement and optimization system joining 3rd party data Allowed for systematized and more comprehensive go to market analytics and optimizations by combining several third party data sources in a single data framework +

6 Building a Framework A lot of ways to skin the cat, go with a process and evolve it Don t get analysis paralysis but the concept should focus on stringing data together that has keys in a relational data structure Just how many ways are there to skin a cat? Unique Placement ID Media Delivery Data Awareness Data Mid Funnel Data Low Funnel Data CRM

7 The Data Generated a constantly updating file that pulled in key dimensions and KPI and was distributed for all parties to interpret in their respective platforms In addition, layered in a time dimension so trending could be constructed to provide additional insights and provide hyper-sensitive optimization capabilities Merging data Data Key across all platforms was the Placement Index which comes from the Campaign Code Generator Location Verification Site Goals Delivery Creative Strategy Hybrids KPI/Dimension Metro Viewability AVOC Player Size High Funnel Mid Funnel Conversions Bounce Sessions Impressions Cost Clicks Size Theme Ad Type Prospecting Data Target Retargeting CPFunnel CPConversion Click/Session Source Exchange inventory Ad Verification Site Analytics Ad Server Creative 3 rd party data targeting 1 st party data DSP/Trading Desk Integrate metrics

8 The Technology DCM Created placements that provided insights across the key variables tracked. GA / Big Query Built Segment Goals in GA based on Campaign Goal of Engagement and Conversion Utilized Big Query to drill into key audience segments to understand how pathing across site side behaviors were different MOAT Used to measure viewability for Display and Pre-Roll placements down to the placement level SAS / Tableau / Domo Outputs from disparate platforms were merged together in SAS and output in raw formats to load into various analytic platforms. This raw data was the fuel to build out reporting from a consistent viewpoint Elbow Grease

9 Building a System URL system to generate all necessary permutations for delivery and measurement Engaged consultancy to build the Campaign Code Generator. Then generator created the Campaign Code tag that was specific for every placement. These tags were embedded in each ad placement and uploaded into GA in the Data Import function. Naming Conventions The names allowed to describe the placement included URL randomization The tool utilized a VB engine to create a random 8 character field as the index. The VB process ensured the use of each index was unique Traffic implementation Primacy created unique placements based on Partner, Media, Creative, Segmentation, Devise and Landing Page QA Each week the SAS process would ensure all the data rules were not broached.

10 Geo Data Ad Server Tracking Weekly Client Reporting Site Analytics Verification Daily Data Join Tracking Table Reporting Weekly Media Optimizations Click Data Raw Output Vendor re-engagement Data Targeting

11 Why do this? Everyone is reading off the same playbook and pulling in the same performance data One set of Data, different ways to analyze / interpret Speed to market, optimizations are generated much faster

12 Challenges Significant upfront investment of time and energy is the juice worth the squeeze? Is the partner disciplined to implement correctly? What do I do now? What is the right next step?

13 Traffic meets site measurement Details DCM data included Impressions, Clicks, Cost, Interaction (rich Media) and Complete Rate for video MOAT data included Viewability for Display and Video in addition to Interaction and AVOC GA Data included Sessions, Users, Bounce, Avg. Duration along with portion of sessions meeting Key Campaign Goals defined within segments Data Joins These data sources were connected via the Campaign Index to provide details on which placements performed best/worst providing clear direction for optimization

14 Did it Work? Was it worth it? Yes Generate Daily File Build Reporting Once up and running, central file was used as reporting seed stock for multiple platforms across teams on client and agency Real-time collaboration Bugs detected and diagnosed in a team effort Run Data

15 A Not So Hypothetical Example Ad server: Information from indicates that Segment 1 is most effective at driving traffic Ad Server Impressions Clicks CTR CPC Media Cost Segment 1 500, % $10.00 $5,000 Segment 2 500, % $11.25 $4,500 Segment 3 500, % $18.75 $3,750 Segment 4 500, % $45.00 $4,500 2,000,000 1, % $14.79 $17,750 Site Analytics: Information from site indicates that Segment 4 is most effective converting once on the site Site Side Engaged Sessions Bounces Engaged Rate Segment % Segment % Segment % Segment % % Data join: tells a third story, one that can nimbly change based on shifting engagement priorities and conversion definitions Ad Server Site Side Impressions Clicks CTR CPC Media Cost Sessions Bounces Engaged Engaged Rate Cost per Engaged Segment 1 500, % $10.00 $5, % $ Segment 2 500, % $11.25 $4, % $60.00 Segment 3 500, % $18.75 $3, % $50.00 Segment 4 500, % $45.00 $4, % $ ,000,000 1, % $14.79 $17, % $76.34

16 Once a central file is generated, analytics is significantly easier and peer reviewed Business communication platform that brings disparate data together into a singular intuitive view.

17 Key Takeaways A lot of work, but worth it Significant upfront investment in time and consideration but saves exponentially more hours or re-running data while dramatically improving insights and speed of optimization Don t get analysis paralysis Figure out the fundamental end goal and build a framework but organic evolution needs to be a part of building the system Create standards KPIs and variables are important to figure out up front and standardization of naming conventions allow for much more efficient campaign and normative learnings

18 Where to go from here, an iterative approach Benchmarks and norms scale-ably storing consistent data in a consistent process is allowing for development of a scaled normative database Modeling regressions, contribution models, segmentation all much more turnkey with a consistent data model Extending the string Conversion/Acquisition data via CRM to align media, site interaction and CRM Optimization Engine Feed findings back into CMS,A/B tests and Trading platforms

19 THANK YOU