INTELLIGENT PAYWALL™ CASE STUDY

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Overview Platform & Case Studies About Us Audience Segments

2 OVERVIEW

Segmenting your audience enables Mather to recommend the best strategy to grow revenue from each customer.

ENGAGEMENT ACQUISITION RETENTION ADVERTISING

5 Listener integrates with your existing fulfillment systems to trigger personalized action at the right time.

6 AVG. PER USER FLYBYS STABLE USERS ENTHUSIASTS FANATICS PER MONTH

ALL USERS 6.9M 595K 340K 167K

PAGEVIEWS 1.4 7.0 14.4 100.7

ARTICLE PAGEVIEWS 0.9 2.6 4.4 27.2

VISITS 1.1 2.6 5.0 26.2

TIME PER VISIT 02:17 06:57 07:40 06:41

SCROLL DEPTH 28% 50% 52% 62%

AD REVENUE $0.01 $0.05 $0.12 $1.08

UNIQUE DAYS 1.1 2.1 3.6 9.5

% KNOWN USERS 0% 2% 5% 38%

CONVERSION RATE 0.00% 0.22% 0.28% 0.73%

7 REPEAT ENGAGED FIRST TIME NEW RETAINED MEDIUM HIGH USER SUBSCRIBER SUBSCRIBER PROPENSITY PROPENSITY

engage, register, acquire via paywall churn prevention, newsletter or email re-engagement, pricing

8 REPEAT ENGAGED FIRST TIME NEW RETAINED MEDIUM HIGH USER SUBSCRIBER SUBSCRIBER PROPENSITY PROPENSITY

On-site On-site Off-site Email Welcome / Timing-Based Welcome Soft Calls to Aggressive Calls Offers Onboarding Brand / Value Messaging Action to Action (known users) Emails Promotion

On-site Behavior-Based Soft Calls to Newsletters Limit Access to Brand / Product Brand / Value Action Content Promotion Promotion

Preference-Based Newsletters Registration for Subscription Education Prior Brand / Value + Page Views Offer to First Renewal Promotion

Lapsed Subscription Subscriber Offer Messaging

9 Propensity Segments

Users Rescaled Conversion Probability The propensity scores and raw output 800,000 1.20% are organized into segments that

700,000 0.97% simplify marketing tactics through 1.00% 600,000 communication channels. 0.80% 500,000

400,000 0.60%

Users 0.47% Though each user is also given a 300,000 0.40% unique probability of conversion. 200,000 0.11% 0.20% 100,000 0.04% - 0.00% Thresholds and audience sizes can be None Low Medium High Propensity Group (based on percentile) adjusted based on goals and risk.

10 CATEGORY FEATURES 50+ Features Tested Visits, Page Views, Article Page Views, Unique Days, Engagement Key inputs include environmental, behavioral, deviation, Unique PV, Time on Site, Scroll Depth, Content Breadth and content features. Location Local, Non-Local

Device Desktop, Mobile, Tablet Takeaways Day-Part / DOW Sunday - Saturday, Morning, Afternoon, Evening Machine learning techniques identify diminishing returns Arts, Opinion, Sports, Business, Guidelive, Life, News, from adding incremental features to predictive power. Content Sportsday, Entertainment

Econometric modeling requires deliberate hypothesis Paywall Interaction Modal Hits, Last Visit Blocked testing and model specification. Deviation Change in Visits, Present in First Period

Ratios Pageview per visits, Duration per page

11 ACTUAL ACTUAL NO YES Predicted Results Baseline Model - Logit PREDICTED 513,227 1 Due to an imbalanced dataset, two different distributions Sensitivity: 79% NO are applied to the modeling and implementation. Precision: 92% PREDICTED 213 24 YES Trained with 70% of cleansed data and tested with the rest 30% (subset of 30% shown here). ACTUAL ACTUAL NO YES Takeaways Downsample Unconverted PREDICTED 478,142 20 Sensitivity: 86% NO Under-sampling helps to normalize the distribution and Precision: 4% PREDICTED 35,078 223 improve sensitivity though there is a tradeoff in precision. YES The size of the training dataset is reduced, and Stepwise ML technique is used to select variables. ACTUAL ACTUAL NO YES Out-of-sample tests PREDICTED 1,317,067 85 NO P-values <0.001; pseudo R² 0.68 Sensitivity: 83% Precision: 16% PREDICTED 91,021 725 YES

12 Frequency is often cited as the strongest predictor to subscribe.

However, certain markets show that consuming different types of content has the greatest impact on propensity.

Content categories Page views and Visits Days since and authors time onsite (per week) last visit (last 28 days) (per day/page)

13 Actual Conversions by Segment Low Medium High 4% 84% of the actual conversions are categorized correctly into 12% the high propensity group.

12% of actual conversions are categorized into the medium propensity group.

84%

14 PLATFORM AND AUDIENCE SEGMENTS SEGMENTATION TYPES

Segments can be nearly anything based on the profile of each user.

Examples include: engagement levels, reading behavior, content preference, propensity to pay, advertising value, price elasticity, paywall interaction, etc.

Mather’s team of data scientists and consultants work with clients to customize the analytics and user journeys. Listener™ collects data from unique users who visit a website. These users are then segmented based on environmental, content, and user behaviors. APPROX. In addition to the recommended SEGMENT NAME DEFINITION POOL segments, publishers can use any MATHER_C2_SYRACUSENCAA_3_20180105 3+ article pageviews in Syracuse basketball over a 30-day period 50,000 Listener™ segment and create custom segments as needed to further refine MATHER_C2_POLITICS_3_20180105 3+ articles in Politics section over a 30-day period 175,000 business rules, targeting and MATHER_C1_SPORTS_5_20181010 5+ articles in any sports content over a 30-day period 250,000

experiences. MATHER_E1_ENTHUSIAST_20171215 Users who fall into the second highest engagement level 300,000

MATHER_E1_STABLEUSER_20171215 Users who fall into the third most engagement level 500,000 Other environmental factors and business rules can be configured within MATHER_E2_ACTIVEUSERS_20171215 A combined segment using OR logic. Any fanatic, enthusiast, or stable user 950,000 the paywall based on general MATHER_U1_KNOWNUSER_20180201 Known users (logged in, or email newsletter click) 20,000 recommendations, advertising demand, MATHER_P3_HIGHPROPENSITY_20180301 Users that Mather considers to have a high likelihood of conversion 15,000

and content. MATHER_G1_LOCAL_20180205 Users within the local market area as defined per market (zip code) 195,000

MATHER_C2_SYRACUSENCAA MATHER_C2_POLITICS MATHER_E2_KNOWNUSER MATHER_P3_HIGHPROPENSITY MATHER_E1_ENTHUSIAST 3_20180105 3_20171205 _20180201 _20180301 _20171214 Users are assigned segment names that are stored in the Mather cookie which can be read by a paywall system (or ad server, DMP…etc.). The segments can be used directly within the paywall system to enable an intelligent paywall and dynamic meter.

Price: m_P10_D10 = $10, 10% discount Price: m_P15_D20 = $15, 20% discount Message: m_CB1 = Journalism Value Message: m_CD3 = March Madness Product: m_OSUG = Sun+Dig Product: m_ODIG = Dig-Only Meter: m_M08 = 8 articles Meter: m_M03 = 3 articles

There is some initial setup within the paywall system to build creatives, prices, products, and messaging.

Once that has been set up, experiences can be created for any combination of these “levers” for targeting.

Assigning a combination of Mather segments associated with each unique experience enables the intelligent paywall. In real time, the segments are read by the paywall system and evaluated per user.

Based on the unique combination of segments, the right experience is selected to be presented to each user. COLUMN SAMPLE userid 0598927e55d52fe3707bad4ba user type print+digital, active online user account_num 42390090 email [email protected] engagement group fanatics primary content news For known users, behavioral data is primary device desktop primary author Patrick Smith made actionable by loading primary geo local secondary content sports directly into email or CRM system. secondary device secondary author Briana Garcia engagement change reengaged Key engagement metrics and page views 162 article page views 80 behavioral segments are combined cookies 5 with subscriber data, demographic, unique days 20 ad impressions 648 and other offline sources. ad revenue $1.35 time per visit 6:15 last login 2/15/2018 Columns and format is customized newsletter flag 1 status active for every integration. expire date 12/15/2018 price $20.00 service 7-day churn group low CLV group high BRIEF TECHNOLOGY SUMMARY

Patent-pending technology turns any paywall into an intelligent paywall. Mather Economics uses DynamoDB, an AWS service to store and deliver audience segments in real- time.

Any website or app with the Listener™ script is able to integrate with paywall or other onsite messaging systems .

23 Mather Economics has developed integrations with most major paywall vendors, ad servers, and email systems.

Many publishers use homegrown systems which can also integrate with Listener™.

24 An array of audience segments are listed in the browser stored in the user cookie. In this example, the user has been identified as a Fanatic and will be treated differently than Nearly any paywall can be lesser engaged users. configured to deliver personalized user journeys based on a Listener segment.

25 We have taken the following steps to ensure Listener is GDPR compliant: 1. Replace the cookie with a random value 2. Remove the known user ID along with any related behavior metrics 3. Remove fingerprint data 4. Remove the IP address and geolocation data 5. Set a flag that indicates that the above was applied

The above will be enforced if: a) The user has not consented to the GDPR standards; and/or b) Mather ascertains that the user is from one of the countries that enforces the GDPR policy CASE STUDIES:

INTELLIGENT PAYWALL™ A large U.S. newspaper publisher wanted to transition from an ad-supported digital business model to one driven by both consumer revenue and advertising revenue. Retaining advertising revenue was a requirement in order to justify the investment.

Using the Listener™ data platform, Mather completed a comprehensive strategic assessment outlining the risks and opportunities with a consumer revenue business model. The addressable market was measured and propensity modeling was used to identify specific users who were likely to pay for a subscription product. Additional insight was also provided on user behavior, content preferences, and multiple scenario forecasts to guide the client along their digital transformation.

Mather partnered with the client to assist in decisions with the tech stack, user journeys, marketing communication, and audience lifecycle management.

In 2018, the client launched an intelligent paywall on a subset of the high propensity audience with great success. After less than one month of testing, the paywall was applied to all high propensity users. After three months of further testing, it was determined no advertising revenue was lost during the launch period, which gave the client confidence to expand the intelligent paywall to their other brands. The client is now actively testing user journeys for mid-propensity and low-propensity audiences and evolving personalization of the user experience.

The Intelligent Paywall™ was also launched on native apps (Android and iOS) which is the first case study of its kind in the market.

28 Net Revenue Consumer Revenue Ad Revenue at Risk $1,000

$800 Thousands $600 $378 $400 $232 $200

$0 Month Month Revenues - 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

12 -$200 -$146 -$400

-$600 Static Meter Setting (free articles per 30 days per user)

29 The Intelligent Paywall returns a stronger yield vs. comparable STATIC CONTENT INTELLIGENT scenarios. METER PREMIUM BALANCED Page Views at Risk 1,171,534 3,499,579 1,122,239 A static metered model shows the worst Inc. performance across all metrics. Monthly Paywall Hits 50,084 1,116,050 48,228 Conversion Rate 0.37% 0.04% 0.36% A premium content model may generate Total Conversions 3,468 7,582 3,486 the greatest consumer revenue though Total Sub Revenue $378,064 $829,161 $378,654 advertising revenue is also significantly at Ad Revenue at Risk -$145,703 -$343,380 -$139,572 Inc. risk. Net Revenue $232,361 $485,781 $239,082 12-Months Opportunity Cost from Ad Lite -$5,492 -$12,046 -$5,501 An intelligent paywall targeting users Dollar Tradeoff $2.59 $2.41 $2.71 based on propensity to subscribe Net Revenue per Sub $66.99 $64.07 $68.57 generates the strongest yield.

30 A large U.S. newspaper publisher wanted to test the effectiveness of personalization within the high propensity audience segment to grow conversions.

Using the Listener™ data platform, Mather Economics built a propensity model and partnered with the client to integrate segments into their homegrown paywall. After thorough validation by the client, a testing plan was launched to refine the user journeys.

Additional customized segments were created to support these journeys such as content preference, frequent paywall interactions without successful conversion (high modal segment), and segments for various known and anonymous user types. Testing is ongoing throughout the year.

Early testing indicates a 0.12% growth in the conversion rate from personalization for certain segments.

31 Personalized call to action with reference to popular sports writer shows a 0.12% lift in conversion rate from initial testing.

A.

0.26% CONVERSION RATE

B. 0.38% CONVERSION RATE

32 A large U.S. newspaper publisher wanted to test the effectiveness of various high-modal offers and user experiences to see what would incentivize difficult users to subscribe.

Using the Listener™ data platform, Mather Economics identified key thresholds where users kept encountering the paywall, but continued to abandon their checkout process. In collaboration with the client and the paywall vendor, Mather Economics helped to set up A/B testing to measure the effectiveness of various offers and touchpoints. Testing is ongoing throughout the year though early trends suggest takeaways for future testing.

The client has created some unique user experiences on the site such as a “sticky” cart abandoner message to remind users to finish their order.

33 Conversions Cumulative Pct. On average, users hit the paywall 120 100% almost seven times prior to converting.

82% 90% 100 80% Over 80% of conversions occur by 70% th 80 the 10 paywall hit. 60% 60 50% Setting the high modal segment at 10 40% Conversions paywall hits ensures the majority of

40 Cumulative Pct. 30% users subscribe under normal 20% 20 conditions while trying to incentivize 10% the users likely ignoring the same call 0 0% to action with a different offer. 1 3 5 7 9 11 13 15 17 19 21 Paywall Hits

34 HIGH PROPENSITY CART ABANDONMENT

TOTAL A B A B

Cookies 11,028 4,022 3,647 2,237 2,242

Modals 18,600 5,865 5,354 3,712 3,669

Conversions 23 10 10 3 0

Conversion Rate 0.19% 0.25% 0.27% 0.13% 0.0%

Visits per Cookie 2 2 2 2 2

Time per Visit 02:16 02:27 02:36 01:54 01:57

Ad Revenue $826 $280 $257 $152 $139

35 A large U.S. newspaper publisher transitioned from a premium site with a hard wall to a metered paywall to benefit from the Intelligent Paywall™. The client wanted to develop personalized user journeys for high/medium/low propensity audiences to maximize the value of each group.

Using the Listener™ data platform, Mather Economics developed a propensity model and integrated the resulting segments into the client’s paywall. Significant A/B testing continues to yield nuances and insights to further evolve the right messaging, meter thresholds, email newsletter calls to action, educational messaging, and other touchpoints.

Early findings suggest each propensity segment has an “optimal” meter setting based on conversion rate and email capture rate.

36 HIGH PROPENSITY MEDIUM PROPENSITY LOW PROPENSITY

A B A B

1. Welcome Whisper 1. Welcome Whisper 1. Welcome Whisper 1. Welcome Whisper 1. Content Suggestion

2. Soft Newsletter CTA 2. 2. Content Suggestion 2. EDU 2. Soft Email

3. Hard Wall 3. Soft Newsletter CTA 3. 3. 3.

4. 4. Soft Newsletter CTA 4. Soft Newsletter CTA 4. Soft Email

5. 5. Hard Wall 5. 5.

6. Hard Wall 6. Content Suggestion 6. Soft Email

7. 7.

8. Content Suggestion 8. Soft Email

9. 9.

10. Soft Newsletter CTA 10.

11. Hard Wall 11. Hard Wall 37 CURRENT SETTING

$7.49 Per month 3 5 6 7 8 9 10 15 20 ATB Meter ATB Meter ATB Meter ATB Meter ATB Meter ATB Meter ATB Meter ATB Meter ATB Meter

Page Views 1,441,776 1,043,335 917,577 816,055 732,336 659,429 597,905 368,578 229,756 at Risk

MONTHLY Paywall Hits 202,928 82,678 61,116 47,491 38,094 31,328 26,102 12,244 6,338

Conversion 0.70% 0.78% 0.82% 0.86% 0.89% 0.93% 0.97% 1.17% 1.36% Rate Total 16,299 8,668 7,332 6,517 5,983 5,629 5,379 4,268 3,702 Conversions Total Sub $924,120 $485,121 $407,854 $360,369 $328,869 $307,772 $292,612 $228,716 $196,054 Revenue Ad Revenue at -$179,178 -$128,259 -$112,388 -$99,953 -$89,699 -$80,769 -$73,233 -$45,145 -$28,141 Risk 12 MONTHS Net Revenue $744,942 $356,862 $295,467 $260,416 $239,170 $227,003 $219,379 $183,571 $167,912

Sub Revenue per Ad Dollars $5.16 $3.78 $3.63 $3.61 $3.67 $3.81 $4.00 $5.07 $6.97 Risked Net Revenue $46 $41 $40 $40 $40 $40 $41 $43 $45 per Subscriber

38 The ATB meter is expected to Net Revenue Total Sub Revenue Ad Revenue at Risk generate positive net revenue at $700,000 lower meter settings. $600,000

$500,000

Incremental subscriber revenue $400,000 $300,000 greater than lost advertising $192,509 $200,000 revenue. $137,483

$100,000

$0 3 5 6 7 8 9 10 15 20 25 -$100,000 -$55,026 -$200,000

39 CURRENT SETTING

$7.49 Per month 3 4 5 6 7 8 9 10 ATB Meter ATB Meter ATB Meter ATB Meter ATB Meter ATB Meter ATB Meter ATB Meter

Page Views 765,903 759,495 740,753 715,481 687,577 657,753 626,749 597,905 at Risk

MONTHLY Paywall Hits 138,133 86,325 62,004 48,337 39,696 33,729 29,427 26,102

Conversion 1.28% 1.24% 1.19% 1.15% 1.10% 1.06% 1.02% 0.97% Rate Total 19,817 12,859 9,649 7,900 6,838 6,149 5,695 5,379 Conversions Total Sub $1,138,477 $732,816 $545,285 $442,736 $380,141 $339,170 $311,922 $292,612 Revenue Ad Revenue at -$95,676 -$94,341 -$91,135 -$87,634 -$84,217 -$80,564 -$76,766 -$73,233 Risk 12 MONTHS Net Revenue $1,042,801 $638,476 $454,150 $355,101 $295,924 $258,606 $235,156 $219,379 Sub Revenue per Ad Dollars $11.90 $7.77 $5.98 $5.05 $4.51 $4.21 $4.06 $4.00 Risked Net Revenue $53 $50 $47 $45 $43 $42 $41 $41 per Subscriber

40 Net Revenue Total Sub Revenue Ad Revenue at Risk

$900,000

Targeting the most engaged group of $800,000 users (Fanatics) will generate greater net $700,000 revenue at lower meter settings due to: $600,000

$500,000 • Higher conversion probability $400,000 $252,672 • Managed advertising risk $300,000

$234,771 $200,000

$100,000

$0 3 4 5 6 7 8 9 10

-$100,000 -$17,901

41 ABOUT US Matt Lindsay is the President of Mather Economics, a business consulting firm based in Atlanta. Matt has over 25 years of experience in helping businesses increase operating margins and grow revenue through economic modeling and analytics. In a consulting role over the past 19 years, he has shared this expertise and developed pricing strategies and predictive analytics models for clients including the Intercontinental Exchange, Gannett, The Home Depot, NRG Energy, Tribune, IHG, McClatchy, the Walton Foundation, Coca Cola, UPS, Dow Jones, Chick fil-A, Clorox, Scientific Games, The Georgia Lottery and The New York Times.

Matt began his career with the corporate Economics Group of United Parcel Service measuring price elasticity and marginal network costs to improve profitability by customer. Prior to founding Mather Economics, Matt worked with Arthur Andersen in the firm’s Atlanta strategy practice. His extensive experience in marketing spend effectiveness optimization, customer retention, analysis and predictive models have been used to support strategic pricing decisions, marketing initiatives and customer acquisition tactics, ultimately generating millions of dollars in incremental profits for his clients. President Mather Economics, one of Inc. Magazine’s 5000 fastest growing US companies for the past 3 years, [email protected] works with hundreds of clients to strengthen business performance through customer analytics. In the 678-585-4101 highly disrupted publishing sector, Mather manages over $4 billion in client revenue and receives data on over 30 million households each week. Mather has recently launched a digital data capture tool called ListenerTM that combines hardware, software, and analytics to provide actionable recommendations at a customer level.

Matt has authored a book, The Relationship Economy, which became available in March of 2017.

Matt holds a Doctorate in Economics from the University of Georgia, a Master of Applied Economics from Clemson University and an undergraduate degree in Economics from the University of Georgia.

43 Learn how to make data work for you, grow valuable relationships, and empathize with your customers.

By Matt Lindsay, President

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44 President Executive Vice President Sr. Director Product Development

Director Manager Data Engineering Data Science Services

45 THE WALL STREET JOURNAL ADVANCE LOCAL New York, NY New York, NY

SMITHSONIAN INSTITUTION GANNETT Washington, D.C. McLean, VA

NBA GATEHOUSE MEDIA New York, NY Fairport, NY

THE HOME DEPOT NY POST Atlanta, GA New York, NY

HEARST CORPRATION TRIBUNE MEDIA New York, NY Chicago, IL

DALLAS MORNING NEWS NEWSDAY Dallas, TX Melville, NY

SCIENTIFIC GAMES MCCLATCHY FLORIDA LOTTERY GOOGLE Las Vegas, NV Sacramento, CA Tallahassee, FL Mountain View, CA INTERCONTINTAL COX ENTERPRISES PHILADELPHIA INQUIRER EXCHANGE Atlanta, GA Philadelphia, PA Atlanta, GA *Select clients

46 BORSEN SCHIBSTED Copenhagen, Denmark Oslo, Norway BONNIER Stockholm, Sweden NRC Amsterdam, Netherlands PROJECT SYNDICATE POST MEDIA Prague, Czechia Vancouver, Canada TORSTAR Toronto, Canada eJOBS Bucharest, Romania THE GLOBE & MAIL NZZ JOONGANG ILBO Toronto, Canada MEDIAHUIS Zürich, Switzerland Seoul, South Korea Antwerp, Belgium

GRUPO NACION San Jose, Costa Rica SINGAPORE PRESS HOLDINGS (SPH) Singapore CINEPLANET Lima, Peru NINE NETWORK Willoughby, Australia

FAIRFAX MEDIA Sydney, Australia News Corp THE AGE Sydney, Australia Melbourne, Australia NZME Auckland, New Zealand *Select clients

47 Mather Economics 1215 Hightower Trail Building A, Suite 100 Atlanta, GA 30350

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