How Publishers Clearing House Leverages Their First Party Data To Acquire, Re-Engage And To Optimize Media Spend
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Publishers Clearing House (PCH) sits on a treasure trove of 1st party data – over 110 million US profiles with hundreds of data points appended to each. Learn how PCH uses this data to Identify high value sources for new acquisition and high value segments for re-engagement. In addition, discover how PCH has shifted toward a data-driven attribution methodology to more accurately attribute credit to media channels driving conversions and revenue.
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How Publishers Clearing House Leverages Their First Party Data To Acquire, Re-Engage And To Optimize Media Spend
- 1. How Publishers Clearing House Leverages First Party Data to Acquire, Engage and Optimize Jason Zeller Director Digital Marketing and Acquisition
- 2. Do People Really Win? Do you sit near Ed McMahon? When are you coming to MY house? LOL… Hehe #ConvCon
- 3. With over $327 million in prizes awarded to date and a prize won every 10 minutes, our members play thousands of casual games daily for chances to win prizes! Take a look at some of our WINNERS! #ConvCon
- 4. How do we make money?? Publishers Clearing House has been a top direct marketer and trusted household brand for over 55 years. PCH members buy products & services via online and offline channels. PCH members are highly engaged on our Web, Mobile Web, and App games and properties #ConvCon
- 5. #ConvCon
- 6. New Customer Acquisition and Customer Re-engagment 1. New Customer Acquisition o Predict LTV of customers acquired by media source o Lookalike Modeling of high value segments 2. Remarketing o Identify which high value PCH customers to remarket to and what to promote 3. Digital and Cross Channel Attribution o Optimize media spend across digital media channels even linear TV networks Today’s Agenda
- 8. Classic Acquisition Marketing 2000-2015 2005-2008 Acquisition Marketing: • Shotgun Approach o Testing Multiple sources and see which performs 2008-2014 Acquisition Marketing: • 3rd party syndicated data segments on Ad Networks. Ex: o Demographic targeting o Interested in Sweepstakes
- 9. In 2017, PCH’s Greatest Asset: It’s Data! We are Extremely Data Driven ALL decisions and strategies are driven by what the data says
- 10. How much to pay per New Customer Conversion? ??? ??? ???
- 11. $.64$.75 $5.68 LTV Model Real-Time Lifetime Value Model
- 12. Real-Time Lifetime Value Model Data Inputs • Form Data and Previous Payment History • Site Engagement • Email Domain • Source of Acquisition • Seasonality Artificial Intelligence • Monitors recent trends to optimize model • Learns new sources and audiences
- 13. Source Evaluation by LTV $5.00 $6.85 $6.25
- 14. • We also use our 1st party data to find Lookalike audiences o What are Lookalikes? • How are we finding Lookalikes: o Facebook and Google can find audiences on their platforms that Look Like our best customers o Our data partners can build models that identify Lookalikes within their own Data Repositories a) Merkle b) Alliant Lookalike Segmentation
- 15. Lookalike Segment Evaluation by LTV $7.00 $9.85 $8.25
- 17. Data Segmentation for Re-Enagement and Remarketing Why do we segment our audiences? 1. Customized Creative o Identifying segments that might respond better to customized creative will improve CTR and/or Conversion Rate CPA is 32% lower vs. controlCPA is 18% lower vs. control
- 18. Data Segmentation for Re-Enagement Why do we segment our audiences? 1. Customized Creative o Identifying segments that might respond better to customized creative will improve CTR and/or Conversion Rate 2. If there are high LTV data sets within a segment o Identifying LTV variations within data sets allow opportunities for greater scale
- 19. Data Segmentation for Re-Enagement Facebook Acquisition – January 2016 Segment LTV CPA Volume PCH Buyers $6.00 $5.00 35,000 Segment LTV CPA Volume VIP Buyers $13.00 $10.80 10,500 Offline Only Buyers $18.00 $7.50 8,000 PCH Buyers $5.95 $4.96 33,200 Total $8.95 $6.54 51,700 Facebook Acquisition – January 2017
- 20. Data Segmentation for Re-Enagement Why do we segment our audiences? 1. Customized Creative o Identifying segments that might respond better to customized creative will improve CTR and/or Conversion Rate 2. If there are high LTV data sets within a segment o Identifying LTV variations within data sets allow opportunities for greater scale 3. Cross Promote other Properties
- 22. Online Merchandise Buyers = 894k Multi-Channel Buyers= 242k Offline Merchandise Buyers= 2.4MM 3X Cross Channel Marketing
- 23. Digital Targeting • We are currently using Liveramp and Merkle to identify any offline buyer who hasn’t engaged online yet • However online Match rates are low so scale will be minimal o 2017 Estimates – 2,000-5,000 conversions Postcard Strategy • Drive offline customers online via a postcard Offline Targeting
- 24. Case Study with Dish • Targeting 6MM Offline Customers on Dish Network via Axciom Data • 10% hold-out group will determine lift • Dish and Axciom will work to match TV viewing data against conversion data Addressable TV
- 26. Last Click Attribution Model Ad Served Ad Clicked Ad Served Conversion 100% Event Publisher Attribution Credit 0%0%0% 10,000000Conversion Credit
- 27. ROI via Last Click Attribution Model
- 28. Ad Served Ad Clicked Ad Served Conversion 25% Event Publisher Attribution Credit 50%0%25% Data Driven Attribution Model This scenario is observed and recorded 1,000s of times 2,5005,00002,500Conversion Credit
- 29. ROI via Data Driven Attribution Model
- 30. ROI via Data Driven Attribution Model
- 32. Multi-Touch Attribution Digital alone is easy o Digital channels leave a footprint at the user level that maps to online conversions. o Most Brands today can follow a user across screens thanks to Authoritative Identity and device mapping. Ex: • Server to Server Conversion tracking • Conversion Pixel What about TV? • Channels like TV and radio on the other hand aren’t as easily mapped • Multi-Touch Attribution partners are needed to make that connection between TV and a Conversion or Purchase o And then model out fractional attribution credit across channels so you can optimize
- 33. • What is my baseline and the true incremental impact of TV? • How does TV influence other channels in my media mix? • How effective is TV at driving business impact? Cross Channel Attribution
- 34. Cross Channel Attribution Spend = $40MM ROAS = 16% Incremental Lift From TV = 20% CPA decreased $.47 Incremental Lift From TV = 32% CPA decreased $.58
- 35. MTA Solutions • Differences in inserting a TV spot view in the path to conversion o Time Series o Deterministic Mapping of TV view to Online and Offline ID • Differences in Models o Some MTA solution has a “secret sauce” that is the best model out there! o Some use IAB definitions of Attribution or allow you to weight accordingly o Any model is better than last click • Differences in Costs and Service o Flexible modeling and platform o Size of the account service team o Costs for the service o Any solution is better than last click!
- 36. Questions?
Editor's Notes
- Dozens of happy winners each month This one never made it on TV
- So you all have a Frame of reference, let me explain what who we
- Get a different slide.
- Get nice graphic
- Waste a lot of spend in shotgun approach testing multiple sources Then, we got more intelligent by limiting wasted spend by targeting data segments representative of our customers – age targetin, etc…
- Explain how the TOM Model works: The model collects Ad Rev, Ecom Rev, Email Engagement, Web Activity, and leverages 1st and 3rd party data to model out a 1 year LTV. Note this is a 12 month revenue estimate so all revenue after 1 year is gravy! We in-turn take that LTV and use it to set the prices we are willing to pay for Optins by source. The TOM model will tell us varying projected LTVs by source so we can set costs accordingly Have a slide that shows the average LTV with the ecommerce value (you can do it on the previous slide)
- Our customers are all given a unique identifier upon registering for a sweepstakes. All offline and online activity is recorded and appended to that ID for a full view across all channels of that customer. You can’t enter any of our sweepstakes, or collect tokens from any of our properties unless you’re logged in.. 1st party data that impacts LTV scores are geo, age, email domain, credit score Artificial Intelligence adapts to changing trends in our business and adjusts the LTV model in real-time. Our team monitors the changes over time to make sure the model is still accurately predicting LTVs
- Our customers are all given a unique identifier upon registering for a sweepstakes. All offline and online activity is recorded and appended to that ID for a full view across all channels of that customer. You can’t enter any of our sweepstakes, or collect tokens from any of our properties unless you’re logged in.. 1st party data that impacts LTV scores are geo, age, email domain, credit score Artificial Intelligence adapts to changing trends in our business and adjusts the LTV model in real-time. Our team monitors the changes over time to make sure the model is still accurately predicting LTVs
- Here I would explain what a Lookalike is I would elaborate on the two different ways we are identifying Lookalikes: Lookalikes from buying Platforms Like Google and Facebook Lookalikes from our data partnerships – Alliant and Merkle – Both partners are building models to find Lookalikes in their own Data Repository and pushing those segments to our buying platforms (google and FB) for us to target
- Get nice graphic
- Add VIP creative… show higher Conversion rate and lower CPA for VIP creative. Or just say CPA was 20% lower than control creative. LTV was X compared to control Then have VIP Creative and control creative fade in.. Then show chart with CPA difference
- Explain we can that we have high value buyers who stopped engaging with PCH. We use
- Explain we can that we have high value buyers who stopped engaging with PCH. We use
- Show Lotto Creative
- Change the years to 2015 and 2016 Authoritative identity
- Let’s talk a little more about Cross Channel Marketing and the Offline buyer I just referenced. As you saw from the previous slide, we targeting our offline buyers on Facebook. And the LTV of those who converted was $18!
- I’ll describe our activation efforts here. I’ll explain that the digital opportunity is incremental and that it is difficult to scale via this opportunity. These people are obliviously not as engaged online so it’s difficult to target them online with ads. The better opportunity is to find them offline and drive them online via postcards. @Jason, can you send me early results to include in the deck? I’ve asked Linda for a screenshot of the postcard We’ve determined that the match rates are low so it’s digital is not a big opportunity. Reposition that we are going to leverage the postcard strategy.. Match rates are so low so we couldn’t
- Get Dish Logo
- Get nice graphic
- Show chart of ROI by channel in projections document with last click model
- Show same chart with Digital data Driven attribution credit Show that Display ROI is now above goal and there is room to bid higher
- Show new chart with greater meida spend on digital, lower cpa, lower ROI
- Can you get a better TV picture?
- We run a lot of tv media and have subsequent conversions days later. We need a model that doesn’t just look at site traffic directly after a spot airs