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DIGITAL REVENUE OPTIMIZATION USING LISTENER
1215 Hightower Trail
Bld. A, Ste. 100
Atlanta, GA 303501
AGENDA
◎LISTENER
◎APPROACH AND CASE STUDIES
◎FUTURE FOR PUBLISHING
LISTENER3
4
TECHNOLOGY OVERVIEW
5
AUDIENCE GOALS
6
PAID SUBSCRIBER ACQUISITION EXECUTION
2017 MILESTONES
1B PV/MonthSeptember 2017
179% Growthyear-over-year
7
225M events per day
22TB New Data processed each month
PRODUCT ROADMAP
✓ 2017 Q1: Launched Audience Package
✓ 2017 Q2: Development launched for the Content Module
✓ 2017 Q2: Launched Listener Tag Manager
✓ 2017 Q2: Launched metricsXchange digital benchmarking
✓ 2017 Q2: Patent pending
✓ 2017 Q3: Audience Hub and Clustering (add-on to AP)
2017 Q4: Finalize Content Module with Article Lifetime Value
2017 Q4: Productize digital user CLV and acquisition model
8
2017 CASE STUDIES IN PROGRESS
• Intelligent paywall
• Predictive modeling for digital acquisition
• First party data
• Economics of content
• Cart abandonment
• Targeted newsletter signups
• Behavioral clustering 9
APPROACH &CASE STUDIES
10
FLYBYS• 74% of users
• One page view/mo
• Social media
referrers
• Goal: convert to
repeat visitor
11
AUDIENCE SEGMENTATION
STABLE USERS• 10% of users
• 5 page views/mo
• 2-3 article views/mo
• Repeat visitors
• 4:51 minutes on site
• Goal: collect email
via registration
FANATICS• 2% of users
• 30 page views/mo
• 5 content areas
• 20% of Fanatics are
already registered
• Multiple devices, e-
edition, apps,
• Goal: convert AND
retain subscribers
12
USER TARGETING
HighMedium
Know
nA
nonym
ous
• Heavy email
marketing to engage
• Minimal subscription
offers
• Heavy ad targeting
Low
Engagement Level
• Targeted emails based on
preference
• Email subscription offers
• Segments for ad
targeting
• Target content
• Enroll in elite
membership program
• First party ad targeting
• Targeted emails
• Retain/engage
• Engage and grow
through social media,
commenting
• Newsletter signup
• Registration
• Segments for ad
targeting
• Register and convert to
grow known audience
• Forced monetization:
remove ad blocker or pay
for content
Ident
ifie
d U
ser
OVERALL CONVERSION TRENDS
13
• Desktop converts better than mobile• Direct (typed) traffic converts best• Google shows the best social/search conversion• Facebook shows the worst conversion• Local converts better than non-local audience• Content depends on market, property, quality…etc.
DEVICE EXAMPLE – LARGE METRO
14
0.04% of monthly users convert on Desktop vs.
0.01% on Mobile
0.34% users who hit the paywall convert on Desktop
vs. 0.15% on Mobile
REFERRER EXAMPLE – LARGE METRO
15
Of the users who hit the paywall, the
conversion rate by referrer is:
0.47% Direct 0.16% Google
0.07% Facebook
16
0.015%
0.029%0.065%
0.189%
0.009%
0.035%
0.143%
0.000%
0.020%
0.040%
0.060%
0.080%
0.100%
0.120%
0.140%
0.160%
0.180%
0.200%
Un
iqu
e U
sers
Conversion Rate
AUDIENCE EXAMPLE – LARGE HYPERLOCAL
ANONYMOUS USER PROPENSITY
Acceleration in page views and unique days for local users are best predictors
RECOMMENDATION: apply user targeting to high propensity users with a custom subscription offer without the traditional checkout process
0.00%
0.05%
0.10%
0.15%
0.20%
0.25%
0.30%
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
90,000
5 11 13 18 20 22 30 32 36 38 48 50 52 61 64 67 69 71 73 75 77 80 84 86 88 90 95 98
Co
nve
rsio
n P
rob
abili
ty
An
on
ymo
us
Use
rs
User Conversion Rank
Anonymous User Conversion Estimate
Users Seen an Offer Selected an Offer Reached Payment Probability
17
INTELLIGENT PAYWALL FORECASTING18
$83,429$130,497
$200,657
$324,996
-$8,669 -$23,211 -$50,267 -$105,6280
500
1,000
1,500
2,000
2,500
3,000
3,500
-$300,000
-$200,000
-$100,000
$0
$100,000
$200,000
$300,000
$400,000
$500,000
$600,000
$700,000
29 28 27 26 25 24 23 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5
Incr
em
en
tal S
ub
scri
be
rs
Incr
em
en
tal R
eve
nu
e
Meter Setting (number of free articles per user)
Incremental Revenue Estimate - Conversion Impact
12-month Sub Revenue 12-month Advertising Risk 12-month Net Subscribers
Incremental
subscription
revenue at
different meter
settings
Incremental
advertising
revenue risk at
different meter
settings
Over 3X conversion for users identified as “high propensity”
• Users were targeted based on acquisition propensity via email
• Volumes are low due to small list of engaged registered non-paid users –testing confirmed and refined the model
• Now launching campaign for newsletter opt-ins who are not registered and for anonymous users through paywall
19
EMAIL TARGETING TEST
List Name Emails sent Open rate Click rate Total conversions Conversion rate
Rank 3 - Mather Ranking of most likely to convert 2,368 17.91% 1.20% 8 0.34%
Rank 2 6,994 22.75% 1.83% 11 0.16%
Rank 1 612 16.23% 0.76% 0 0.00%
Rank 0 (random registration list that ST picked) 7,110 10.28% 0.51% 7 0.10%
AD SERVER TARGETING USING HOUSE ADS
20
2X conversions targeting engaged users vs. control
FIRST PARTY DATA
The Columbus Dispatch 221,247
Columbus Blue
Jackets
116,402
Matched21,416
242,663
137,818
9% of entities matched from current and former Dispatch subscribers16% of entities matched from all Blue Jackets ticket buyers
2,400 entities (11% of matches) accessed content online
LOOKALIKE AUDIENCES
Statistical analysis completed on subscribers in the local DMA 210,238 subscriber entities given a ticket score
22
Columbus Dispatch current and previous subscribers ranked from 0 to 100 on probability of buying Blue Jackets tickets
Columbus Dispatch anonymous web visitors ranked from 0 to 100 on probability of buying Blue Jackets tickets
Statistical analysis completed on anonymous users in the local DMA 625,986 entities given a ticket score
FIRST PARTY DATA TAKEAWAYS
• 15-20% lift in engagement vs. comparable ad sizes for advertiser
• 9:1 ROI for SEO
• 18:1 ROI for display
• Known users are key to growing value of advertising
• Improves yield for local direct advertising sales
• Can be connected to programmatic exchanges, retargeting tools, and DMPs
See the article in our new monthly newsletter!=http://www.mathereconomics.com/wp-content/uploads/2017/09/Mather-Insights_TargetingCustomAudience_SEP17.pdf 23
CONTENT MODULE
24
DRILL DOWN TO ARTICLE LEVEL
25
KEY METRICS BY ARTICLE AND AUTHOR
Internal, direct, fanatics, local, desktop rank high for conversion
26
BREAKDOWN CONTENT/ARTICLE
27
0%
10%
20%
30%
40%
50%
0%5%
10%15%20%25%30%35%
Rat
io
Shar
e
Fanatics Audience Size by Content TypeShare Ratio
EACH ARTICLE AND AUTHOR ARE SCORED
28
FUTURE29
2018 REVENUE GENERATION
• Subscriber revenue growth – Digital & Print
• Nurturing users with targeted messaging
• Registration for incremental access to content
• Growing known users onsite
• Intelligent paywall + dynamic metering
• Article lifetime value
• First party data for advertising yield
30