how publishers clearing house leverages their first party data to acquire, re-engage and to optimize...

How Publishers Clearing House Leverages First Party Datato Acquire, Engage and Optimize

Jason ZellerDirector Digital Marketing and Acquisition

Do People Really Win?

Do you sit near Ed McMahon?

When are you coming to MY house? LOL… Hehe

#ConvCon 

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 

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 

#ConvCon 

New Customer Acquisition and Customer Re-engagment

1. New Customer Acquisitiono Predict LTV of customers acquired by media sourceo Lookalike Modeling of high value segments

2. Remarketingo Identify which high value PCH customers to remarket to and what to promote

3. Digital and Cross Channel Attributiono Optimize media spend across digital media channels even linear TV networks

Today’s Agenda

New Customer Acquisition

Classic Acquisition Marketing 2000-2015

2005-2008 Acquisition Marketing:•Shotgun Approacho Testing Multiple sources and see which performs

2008-2014 Acquisition Marketing:•3rd party syndicated data segments on Ad Networks.

Ex:o Demographic targetingo Interested in Sweepstakes

In 2017, PCH’s Greatest Asset: It’s Data!

We are Extremely Data DrivenALL decisions and strategies are driven by what the data says

How much to pay per New Customer Conversion?

???

???

???

$.64$.75

$5.68

LTV Model

Real-Time Lifetime Value Model

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

Source Evaluation by LTV

$5.00

$6.85

$6.25

• We also use our 1st party data to find Lookalike audienceso 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) Merkleb) Alliant

Lookalike Segmentation

Lookalike Segment Evaluation by LTV

$7.00

$9.85

$8.25

Remarketing and Re-Engagement

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

Data Segmentation for Re-Enagement

Why do we segment our audiences?

1. Customized Creativeo 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 segmento Identifying LTV variations within data sets allow opportunities for greater scale

Data Segmentation for Re-Enagement

Facebook Acquisition – January 2016Segment LTV CPA Volume

PCH Buyers $6.00 $5.00 35,000

Segment LTV CPA VolumeVIP Buyers $13.00 $10.80 10,500

Offline Only Buyers $18.00 $7.50 8,000PCH Buyers $5.95 $4.96 33,200

Total $8.95 $6.54 51,700

Facebook Acquisition – January 2017

Data Segmentation for Re-Enagement

Why do we segment our audiences?

1. Customized Creativeo 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 segmento Identifying LTV variations within data sets allow opportunities for greater scale

3. Cross Promote other Properties

Online Merchandise Buyers = 894k

Multi-Channel Buyers= 242k

Offline Merchandise Buyers= 2.4MM

3X

Cross Channel Marketing

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 minimalo 2017 Estimates – 2,000-5,000 conversions

Postcard Strategy• Drive offline customers online via a postcard

Offline Targeting

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

Digital and Cross-Channel Attribution

Last Click Attribution Model

Ad Served Ad Clicked Ad Served Conversion

100%

Event

Publisher

Attribution Credit 0%0%0% 10,000000Conversion

Credit

ROI via Last Click Attribution Model

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

ROI via Data Driven Attribution Model

ROI via Data Driven Attribution Model

Cross Channel Attribution

Multi-Touch AttributionDigital alone is easyo 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

• 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

Cross Channel Attribution

Spend = $40MMROAS = 16%

Incremental Lift From TV = 20%

CPA decreased $.47

Incremental Lift From TV = 32%

CPA decreased $.58

MTA Solutions• Differences in inserting a TV spot view in the path to conversion

o Time Serieso Deterministic Mapping of TV view to Online and Offline ID

• Differences in Modelso 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 accordinglyo Any model is better than last click

• Differences in Costs and Serviceo Flexible modeling and platformo Size of the account service teamo Costs for the serviceo Any solution is better than last click!

Questions?