Merkle - 2019 Sponsor Luncheon Presentation
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Search and Performance Insider Summit, Deer Valley, UT, Dec. 13, 2019 - 01:00 PM: Moving From Reporting to OptimizationGet more value out of your technology investments. Move your reporting from backward facing to insight generating.Presenter: AdamReitelbach, IndustryExpert,MerkleAdam is a long-time performance marketing veteran. He works with clients to define goals, build world class solutions, and exceed expectations. He has significant experience in the retail, travel, and B2B verticals.
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Merkle - 2019 Sponsor Luncheon Presentation
- 1. © 2018 Merkle. All Rights Reserved. Confidential1 Moving from reporting to optimization Adam Reitelbach December, 2019
- 2. Merkle Who are we? The Ancient Parable of Six Blind Men and the Elephant
- 3. © 2018 Merkle. All Rights Reserved. Confidential3 Merkle is unique among media agencies We are a people-based, performance marketing agency We help the best brands in the world create competitive advantage through people-based marketing. We believe in marketing to people not proxies. We believe the future of marketing is personal, informed by data, powered by technology, and delivered through creativity. STRATEGY AND PLANNING DATA & ANALYTICS AUDIENCE MANAGEMENT Marketing Technology: Database Marketing, Marketing Clouds, Data & Analytics Performance Media and CRM Agency: Creative, Media, and Campaign Management
- 4. How are my marketing dollars working together?
- 5. What combination of channels leads to success?
- 6. How can I get reporting faster?
- 7. Attribution?
- 8. © 2018 Merkle. All Rights Reserved. Confidential8 Why is this? IT Delays Lack of Bandwidth Manual tasks Lack of common data
- 10. © 2018 Merkle. All Rights Reserved. Confidential10 This looks daunting DATA MANAGEMENT INSIGHTS ORCHESTRATION ACTIVATION INTEGRATION OUTBOUND Paid Social Display Search Call Center | CRM Mobile Apps Store | PoS Website Customer Portal Landing Page | Microsite Decision Management APIs Data Messaging IVR Machine Learning | AI Attribution Journey Analytics Predictive Analytics Direct Mail Email SMS/Push eCommerceAddressable TV Data Enhancement Data Sourcing Identity Resolution Consumer Data Store Data Integration Data Distribution Data Visualization Forecasting OBTM Journey Management Tag Management Content ManagementCampaign ManagementAudience Management INBOUND Testing and Optimization
- 11. We work with marketers on GMP Every Day
- 12. © 2018 Merkle. All Rights Reserved. Confidential12 Google Marketing Platform - Marketing Maturity Media Centric Tech Centric Data Centric Data-Driven Marketing Marketing campaigns are executed mainly using external data and direct buys with limited link to sales Some use of owned data in automated buying with single-channel optimisation and testing Data integrated and activated across channels with demonstrated link to ROI or sales proxies Dynamic execution across multiple channels, optimised toward individual customer business outcomes and transactions Nascent Emerging Connected Multi-Moment
- 13. © 2018 Merkle. All Rights Reserved. Confidential13 Implement media & analytics products to unleash out-of-the box capabilities and features. Focus on technically sound implementations to prepare for expanding marketing capabilities over time. Integrate media & analytics products to share audience space, manage campaigns across channels, and take advantage of technology capabilities. Introduce audience fueled website optimization. Connect third party data sources for enhanced data sets for analysis. Experience how more precise media targeting as well as highly customized analytics tracking can enable greater return on investment and reduced waste. Take your capabilities to the next level with Cloud & Marketing. BigQuery Connectors lead to unification of online and offline data, machine learning, automation, scaled activation, and full-funnel attribution. Analytics Campaign Manager Search Ads 360 CRM Display & Video 360 Marketing Automation Optimize 360 Search Ads 360 Marketing Automation CRM Analytics 360 Cloud & Marketing Analytics 360 Campaign Manager Search Ads 360 CRM Display & Video 360 Marketing Automation Analytics 360 Campaign Manager Search Ads 360 CRM Display & Video 360 Marketing Automation Optimize 360 Optimize 360 Siloed Media & Analytics Integrated Media & Analytics Connected Third Party Data Sources Cloud & Marketing Cloud & Marketing Campaign Manager Display & Video 360 Google Marketing Platform - Marketing Maturity Nascent Emerging Connected Multi-Moment New connection for maturity phase Existing connection for previous maturity phase BigQuery connector
- 14. © 2017 Merkle. All Rights Reserved. Confidential14 INTERNAL ONLY Proprietary + Confidential
- 15. © 2018 Merkle. All Rights Reserved. Confidential15 Agenda 0 0.02 0.04 0.06 0.08 0.1 0.12 Direct Referral Session Volume Event Volume Lifestyle Page Finance Page Locate Dealer Time On Site Morning Visit Model Page view CPC Referral Bounce Number Important Visits Paid Search Referral Evening Visit Google CPC Referral Brochure Referral Recall Page View Configurator Page View Dealer Locator Page View Influence on Vehicle Sales Key indicators of a car buyer include session Volume, Lifestyle, News, Finance, Locate a dealer and time on site According to the data Brochure and Test drive do not feature heavily as indicators of intent to purchase within this dataset Consumer Experience & Information is key
- 16. © 2017 Merkle. All Rights Reserved. Confidential16 MINI Post Purchase Tracking for Closed Loop Online and Offline Measurement Search BrochureOn SiteSearch Audiences Programmatic Display EDM DSP Purchase Post Purchase EDM With implementation of tracking codes on post purchase emails we can now see which website visitors are actually purchasing vehicles
- 17. © 2017 Merkle. All Rights Reserved. Confidential17 How accurately can we predict our car buyers? Normalised Confusion Matrix 70% 30% 90%10% Non-Buyer Car Buyer 100% 0% 25% 50% 75% The confusion Matrix demonstrates the relationship between true and false positives. 90% of the time we can predict that a customer is likely to purchase a vehicle based on their behaviour.
- 18. © 2017 Merkle. All Rights Reserved. Confidential18 Activation Within GMP Visitor ID Probability 945059 69% 194704 86% 724709 86% 361525 66% 626992 97% 753177 74% 641665 86% 193843 98% 141818 100% 586135 78% 145623 85% 754712 98% 849209 98% 865234 91% 103055 99% 40805 98% 176003 97% 816351 92% 273664 91% 461263 79% We now have a probability score against each visitor in Google Analytics 360. Storage Cloud Storage BigQuery Cloud ML App Engine App Engine The propensity modeling score is pushed back into Google Analytics for audience building and activation across Search & Display.
- 19. © 2017 Merkle. All Rights Reserved. Confidential19 Models Produce Greater ROI and Cost Savings High propensity audiences delivered 3.5X more sales than the medium propensity audiences & 8X more sales than low propensity audiences.
- 20. Ok, but how to communicate that value to your business
- 21. Collect Transform Visualize Analyze
- 22. © 2017 Merkle. All Rights Reserved. Confidential
- 23. “What Merkle showed me how to do in the past hour will save me two full days of work each month.”
- 24. © 2017 Merkle. All Rights Reserved. Confidential Archie Collaborative analytics environment Expansion to advanced, unified performance measurement / attribution Rapid campaign reporting powered by data standards01 02 03
- 25. © 2017 Merkle. All Rights Reserved. Confidential 01 02 03 Stand up campaign insights in a matter of weeks, leveraging Merkle’s best practices RAPID REPORTING 01
- 26. © 2017 Merkle. All Rights Reserved. Confidential 01 02 03 Extend to customer journey and advanced attribution analyses for unified performance measurement ADVANCED MEASUREMENT 02
- 27. © 2017 Merkle. All Rights Reserved. Confidential 01 02 03 Extend beyond campaign dashboards for ad hoc investigation and model development COLLABORATIVE ANALYTICS 03
- 28. © 2018 Merkle. All Rights Reserved. Confidential28 What does this enable? Stage and import data to the cloud DATA INGESTION Delivered through our dashboard templates REPORTING Via our analytics resource(s) INSIGHTS CORE SOLUTION EXTENSIONS Structured for reporting & analytics DATA LAYER Digital, TV, site and/or connected (MTA + MMM) attribution UNIFIED MEASUREMENT Environment for ad hoc analysis COLLABORATIVE MODELING Visualize individual and group event streams CUSTOMER JOURNEY INSIGHTS Evaluate options & build forecast SCENARIO PLANNING Added data hygiene / identity services after data ingestion RAPID AUDIENCE LAYER
- 29. © 2018 Merkle. All Rights Reserved. Confidential29 3 takeaways 1. Move past nascent and emerging into connected martech-adtech ecosystem 2. Focus on audience and automation 3. Leverage technology to help your team collect and transform the data, allowing them to add value
- 30. © 2018 Merkle. All Rights Reserved. Confidential30 Want to learn more? Adam Reitelbach jreitelbach@merkleinc.com
Editor's Notes
- Who is Merkle and what are your core services categories? As a data driven CRM agency, we set out to build a digital agency 10 years ago that brought an unmet market need to the agency business – transparency, data driven targeting, and closed loop measurement to create a performance media agency, not just a digital marketing agency.
- -spectrum / maturity graph of entry-level single-channel optimization ->> integrated, multi-moment, connected customer journey on the right -everything in between -everyone wants to be on the right -day in, we’re stuck on the left in optimizing channels -Pop quiz – where are you? Show of hands? -How to achieve that?
- In order to advance Marketing maturity and capabilities, Marketer’s must also adopt and integrate products. -Adobe, others could be used (and Merkle does!), but showing Google because of low barriers to entry and everyone buys ads on Google -Left is single channel, right is cross-channel automated -Looking at Google stack, you can visualize the connection points -Every-man’s approach that Google makes readily available -We estimate that >90% are between nascent-emerging (google data) ## you’re not behind ## you have the opportunity to advance -Multi-moment is realtime. ##In-store data syncs with website with email, etc. Customer journey execution is at a 1:1 basis. (Verizon is an example) -Need a vision to get there, with less budget than the big players
- When we ran the model on our dataset we learnt some interesting trends What we discovered was that Brochure, Test Drive were low on the list, but what we did see was things like The Number of Sessions, visits to the Lifestyle, finance and locate a dealer page were all key indicators that someone was likely to purchase a vehicle
- One of our initial challenges was solving the problem of tying online data to vehicles sales data One of the challenges that automotive brands face is that traditionally everything that happened online was very separate to what actually happened in a dealership – there was no way for brands to associate the research took place and how that directly influenced the sale of a vehicle on a 1 to 1 basis. Through online Analytics tools we could analyse a consumers behaviour, we could understand what they do onsite, which channel drove them to the site, when they downloaded a brochure or requested a test drive but this is where it stopped. Whilst onboarding MINI onto GA360, through our discovery phase of MET-A we identified that MINI send consumers a series of emails after they have purchased a vehicle. By appending a few parameters to links in these emails and with some advanced configuration in GTM and GA we are able to identify consumers who have purchased a vehicle. Once we can get the consumer back on site, ID’s are linked and we understand all the research the customer did prior to entering the dealership and we now also now know exactly which vehicle the customer purchased. Using GA 360 and the full configurations all this rich data is streamed into Google BigQuery on a daily basis allowing for deeper analysis and discovery of insights.
- This chart is called a ‘Confusion Matrix’, it tells us how accurate our model is. It’s divided up into 4 quadrants, those that haven’t yet purchased vs those that have purchased and what our model predicted vs what actually happened. What we can see is that of those that we predicted would purchase a vehicle, in 90% of cases we were actually correct. At the time of running the model, we could also see that 70% of the time we were correct in identifying customers who weren’t likely to purchase a car.
- So once we understand the likelihood of a consumer purchasing a vehicle we can use this probability, load directly back into Google Analytics 360 for retargeting. We can build audiences of customers with a high, medium or low probability to purchase. We can define strategies, what do we do if we know someone is likely to buy a car? How do we find more of these people. Do we decrease media spend on the customers that we know have a low probability to purchase? Can we bid more aggressively across generic terms for customers who look like those that are have already purchased?
- 3 months after loading our data into GA we could then analyse the results. What we found was that of the customers that we predicted had a high probability to purchase, these customers actually had a conversion rate 3.5X than those with ‘medium’. Those with ‘medium’ probability purchased at a rate of 8X more than those with low. Seeing the results after a period of time across real data was a way for us to accurately validate that our model was working This presents huge opportunity for us to now embed these learnings and insights into our marketing strategies going forward.