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Maximizing Growth. Transforming Organizations. Unlocking Digital. Product Strategy: Example Deliverable

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Page 1: Product Strategy: Example Deliverablerwadvisors.com/wp-content/uploads/2017/10/2017-10-20-Digital-Pro… · 20/10/2017  · • Stories chosen do not mimic the homepage: “These

Maximizing Growth. Transforming Organizations. Unlocking Digital.

Product Strategy:

Example Deliverable

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Agenda

Product overview

Revenue model

Product case studies

2

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ProductX vanity metrics

1 E-Statement penetration rates for individual clients from XXX data

2 Source: XXX XXX XXX Forecast: 2013-2018, InfXXXends, 2014

WE ARE APPROACHING XXX REGISTERED USERS IN THE COMMUNITY

XXXK

Fans on Facebook

XXK XXK XM

Users in Community

Fans on Instagram Twitter Followers

XX

Countries Represented

XM

Photos Posted To Date

3

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Sections 2 gets more engagement than others but only XX% visit more than once per month

JUST XX% OF USERS VISIT PRODUCTX MORE THAN ONCE A MONTH

User Engagement by section (% of total)

45% 50% 55% 60% 65% 70% 75% 80% 85%

45% 40% 35% 35% 30% 25% 25% 20% 15%

10%10%

10%

Section 8

0%

Section 1

5%

Section 3Section 2 Section 9

0%

Section 4

5%

Section 6Section 5

0%

Section 7

5%

Site Section

4

User type Z User type Y User type X

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Section X gets more engagement than others but only 45% visit more than once per month

USER ENGAGEMENT BY SEGMENT (% OF TOTAL)1

28

60

15

2757

13

SessionsUsers

User type X User type Z User type Y

Note: Engagement numbers adjusted to sum to 100% across categories

Sources: (1) xxxxxxx xxxxx xxxxxx

5

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Very few site visitors register and those that do register rarely return

Monthly unique visitors to Product X site (Ks) Total registered members (Ks)

1(10%)

9(90%)

Active Members Non-Registered Visitors

5(5%)

95(95%)

Active Members Inactive Members

6

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Across key engagement metrics, performance has flatlined or fallen

Source: Data provided by XXX Data Analytics Team

Monthly average uploads (Ks) Monthly average favorites (Ks)

Monthly average ratings (Ks) Monthly average daily dozen votes (Ks)

2017

72

+X%+X%

60 65

2013

56 60

2015 20162014

65 60

20172013

7080

60

2014 20162015

+X%+X%

2013

5060

2015

65

2014

49

70

2016

+X%+X%

2017

+X%

2017

-X%

4540

2014

35

20162015

40

2013

45

7

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ProductX has active users around the world

AVERAGE MONTHLY UPLOADS (KS) BY REGION (% OF TOTAL)

Source: Data provided by XXX Data Analytics Team

Notes: (A) Other Includes Africa, Australia, oceania, south America, Antarctica, countries with coding errors, and not specified.

87

20 (23%)

15 (20%)

2013

26 (34%) 26 (30%)

20 (23%)

15 (20%)

20 (25%)

20 (25%)

20 (25%)

20 (25%)

24 (24%)

30 (30%)

30 (25%)99

2015

30 (25%)

120

80

30 (25%)

2016

23 (23%)

22 (22%)

2017

76

2014

20 (26%) 21 (24%)

30 (25%)

North AmericaAsia Europe OtherA

8

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Nearly XX% of active monthly users are ages XX or older

ACTIVE MONTHLY MEMBERS (KS) BY AGE (% OF TOTAL)

2017

3 (10%)

31

8 (27%)

15 (43%)13 (43%)

2 (6%)

2 (7%)

11 (37%) 12 (39%)

4 (11%)

10 (29%)

2016

30

35

4 (11%)

3 (10%)

3 (11%)

8 (30%)

4 (15%)

10 (37%)

27

5 (17%)

3 (10%)

2 (7%)

9 (30%)

5 (16%)

2013

9 (29%)

2 (6%)

2014

30

2015

4 (13%)

2 (7%)

Under 1860+ 40 to 59 18 to 2930 to 39

Source: Data provided by Data Analytics Team

9

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Agenda

Product overview

Revenue model

Product case studies

10

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Three revenue models were developed for ProductX

Model Version Overview

Take no substantial actions to change the Your Shot

product or expand its monetization strategies; continue

similar to current trendsNo Change

Model B

Model C

Invest in improving the BBBB BBBB BBBBB; implement

both BBB BBB BBBB BBBBB BBBB to ensure that the

product tracks with competition

Invest in improving CCCC CCCC CCCC product and

community; develop a set of CCCC CCCCCC CCC

including CCC, CCC and CCC beginning in 2018, and CCC

and CCC beginning in 2020; this model does not include

XXX growth

11

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Model revenue drivers

Model Version Driver 1

No Change

Model B

Model C

Driver 2 Driver 3 Driver 4 Driver 5

Shrinks 5% per year

from ~500K in 2017

to 387K in 2022

Grows at a ~5%

CAGR from ~500K

in 2017 to 643K in

2022

Doubles from ~500K

in 2017 to 1M in 2022

Ad revenue per user

remains constant at

$1.35

Ad revenue per user

declines 1% per year

from $1.35 in 2017 to

$1.28 in 2022

Ad revenue per user

grows 3% per year

from $1.35 in 2017 to

$1.57 in 2022

Remains constant at

$250K per year

Revenue per

campaign declines 1%

per year from $50K in

2017 to $48K in 2022;

number of campaigns

grows from 5 to 10

Revenue per

campaign grows 5%

per year from $50K in

2017 to $64K in 2022;

number of campaigns

grows from 5 to 12

Not implemented

Begins in mid-2018

with a 1.25% user

conversion rate into

the paid option at $96

per year

Not implemented

Not implemented

Begins in 2018 with

0.1 photo prints per

user at $15 and 0.1

non-photo prints per

user at $25; grows 1%

per year

Begins in 2018 with

0.1 photo prints per

user at $15 and 0.1

non-photo prints per

user at $25; grows 5%

per year

12

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Revenue model: No change

DIGITAL REVENUES ($MS)

Source: Digital financial model, XXX financial analysis

Notes: (A) 2017 digital advertising revenue projected based on 2016 financials from Eileen Maroney: Assigned 25% of gallery ad revenue due to sharing with ng.com, 100% of your

shot display ad revenue, and all of photo contest display ads. Excluded photo contest entry fees due to potential double reporting with content verticals. sponsored content

revenue was projected at $250k in 2017 based on 2016 numbers from sales team.

$0.8$0.9

$0.8$0.8

$0.9

2017a 2021

-XX%

20222018 20202019

$0.9

13

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Revenue model: Model B

DIGITAL REVENUES ($MS)

Source: Digital financial model, XXX financial analysis

$0.2 $0.3$0.5

$0.8

$0.6

$0.6$0.7

$0.8

$0.9

$0.6

$0.6

$0.6

$0.6

$0.7

$0.3$0.2$0.2$0.2

20192018

$0.1$0.1

$1.0

$1.4

2020

$1.8

2017a

$1.6

$2.7

$2.1

2021 2022

+XX%

Driver 1 Driver 3 Driver 4Driver 2

14

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Model C

DIGITAL REVENUES ($MS)

$0.2 $0.4$0.6

$1.0

$0.7

$0.7$0.8

$1.0

$1.1

$0.7

$0.7

$0.7

$0.7

$0.8

$0.0$0.4

$0.2$0.2$0.2

$2.2

$0.0

2018

$1.7

$0.1$0.1

2019

$1.9

2017a

$1.2

2021

$3.2

+XX%

2022

$2.5

$0.0 $0.0

2020

$0.0

Driver 5Driver 4Driver 3Driver 1 Driver 2

15

Source: Digital financial model, XXX financial analysis

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Agenda

Product overview

Revenue model

Product case studies

16

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Product case study 1: email newsletters

Product Overview Email Newsletter Overview Key facts

Insights to Consider

• Newsletters contain articles, videos, and photos

sent to users’ email via a newsletter (all link out

to longer pieces or vides on the NYT website)

• Product includes 54+ free individual email

newsletter options for subscription; options vary

in arrival frequency1

• Key topics include: New Email Newsletters;

News, Politics, and Opinions; Business & Tech;

Lifestyle; Special Offers; Dealbook etc.

• Users can preview sample before subscribing

• Product intended to provide personalized,

succinct content overviews that can Segway into

richer, deeper content pieces (written and video)

on the main site

• Weekly newsletters averaged a 50% gross open

rate in 2015; newer niche newsletters topped a

70% gross open rate (38.5% was the industry

average in 2015)2

• Newsletters sometimes include ads, though often

for products / services related to the content

• Arrival tempo varies: Daily, As Needed,

Monday-Friday, Weekends, Weekly, Biweekly,

and Monthly

• Subscription free and can include multiple

options

• 12-person newsletter team as of 20152

• Email subject-lines are 30 characters or less to

drive engagement on mobile3; usually include the

name of the top article in the newsletter

• Newsletters are often sent at a specific time of

day (i.e., Today’s Headlines Asian Morning is

sent when it’s morning in Asia not New York)

• Newsletters include an email address for readers

to respond with feedback

• Users get welcome email to register with NYT

to personalize emails during sign-up

• Newsletters designed to prevent inbox clutter: ““Inbox clutter is something we’re sensitive to,” said Nicole Breskin, a digital product director at the Times.

“Because it’s so saturated, it’s important we produce something of value.”4 Efforts include combining multiple newsletters into one email if they will arrive on the

same day, keeping subject lines brief, and allowing for personalization of email notifications on mobile if users register their subscription

• Stories chosen do not mimic the homepage: “These are not news summaries. We’re conscious of short paragraphs and sentences, of what’s pleasant to read on

a phone screen,” said Clifford Levy, a Times assistant masthead editor5. Newsletters include a plethora of photos and only brief descriptions of articles.

• NYT has emphasized “radical flexibility” in testing formats and content: “We see newsletters as a very cool data testing ground for new products, ideas, and

experiments with tone and voice,” said Nicole Breskin, product director in 20156. Newsletters vary in design, length, and opening letter inclusion based on

readership, time of day and theme. Likewise, “most (newsletters) start out on a weekly basis to prevent … turning off readers before a list gains momentum”.

• Newsletter subscribers frequently become NYT subscribers: “Times users are twice as likely to become paid subscribers if they signed up for a newsletter

first.”6 This probably reflects the fact that someone reading more than 10 articles a month needs a paid subscription.

• NYT uses social media to promote newsletters: NYT uses Facebook and Twitter to gauge interest in new newsletters and to promote current ones6

Sources: (1) New York Times Newsletters (2) Digiday, August 2015, “New York Times Gets Seventy Percent Open Rate” (3) Contently, September 2015, “How the New York Times

Gets 70% Email Open Rates” (4) Digiday, August 2015, “New York Times Gets Seventy Percent Open Rate” (5) Nieman Lab, February 2016, “The New York Times Has a

New Email Newsletter Aimed At College Students” (6) Digiday, August 2015, “New York Times Gets Seventy Percent Open Rate”17

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Product case study 1

Sources: (1) NYT, June 2014, “For Email Newsletters, A Death Greatly Exaggerated” (2) Photos from January 6, 2017 Science Times newsletter (3) January 24, 2017 Science Times

newsletter (4) NYT email newsletters

INSIGHT: NEWSLETTERS DESIGNED TO PREVENT INBOX CLUTTER

The brief subject line of the Science Times email

was “Gene-Modified Ants Shed Light on How

Societies are Organized, which was the featured

article of that newsletter

When the Book Review Newsletter and Science

Times Newsletter appeared on the same day at the

same time in an email inbox, the two newsletter

were joined together into one email thread.

As said by David Carr, a writer for the NYT, “At a time when lots of news and information is whizzing by online, email newsletters

…help us figure out what’s worth paying attention to.”1 With this focus in mind, NYT newsletters have 20-30 featured articles in one

email and brief subject lines (30 characters or less) that feature the main article of the newsletter. They also append newsletters to one

another when two or more are sent at the same time to one subscriber.

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Product case study 1

Sources: (1) Nieman Lab, February 2016, “The New York Times Has a New Email Newsletter Aimed at College Students” (2) Photos from January 24, 2017 Science Times newsletter (3)

NYT email newsletters

INSIGHT: STORIES CHOSEN DO NOT MIMIC THE HOMEPAGE

Each article gets a few brief words about its topic,

which readers can click on to go to the article on

the NYT if they want to read more.

In the Science Newsletter, articles refer to all the

science articles in the NYT currently, not just ones

on the homepage

Stories in each newsletter are organized according to theme, not what is popular or prominent on the homepage. Likewise, articles are

frequently just given a brief sentence or two for introduction – “If you want more information, you click on the links”, particularly in

newsletters like the Morning Briefings.1

19

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Product case study 1

Sources: (1) NYT email newsletters (2) Photos from January 27, 2017 Book Review newsletter (3) February 1, 2017 Sciences Times newsletter

INSIGHT: NYT HAS EMPHASIZED ‘RADICAL FLEXIBILITY’ IN TESTING FORMATS AND CONTENT

“Sciences Times” newsletter: begins with a large

photo from an article, then short descriptions of

articles following“What We Are Reading” newsletter: begins with a

lengthy letter directly to the reader

In trying to avoid the “one size fits all” model, newsletters vary within and between each other: starting the newsletter with a letter vs.

a photo, quantity of articles, tone, time of day sent, etc. This “radical flexibility” allows the newsletter team gauge what works with

different audiences

20

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Product case study 1

Sources: (1) Digiday, August 2015, “New York Times Gets Seventy Percent Open Rate” (2) Photos from Pinterest, “What Are We Reading” (3) NYT email newsletter

INSIGHT: NYT USES SOCIAL MEDIA TO PROMOTE NEWSLETTERS

Videos on the NYT YouTube, such

as this one about Fukushima, suggest

a newsletter to follow.

“What We’re Reading” is a page on

the NY Times Pinterest, as well as

one of their newsletters

The NYT uses social media (such as Pinterest) to promote newsletters (such as What We’re Reading) and to gauge interest in potential

new newsletters. “. In the case of the Kristof letter, it got 50,000 signups after promoting it on social media and on site for six

months.”1

“The African-American Experience” on

the NYT Pinterest references articles in

the Race/ Related newsletter

21

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Product case study 1

Sources: New York Times Newsletters

THE NEWSLETTERS

New, politics and opinions Lifestyle New email newsletters

• Briefing: Europe Edition

• Morning Briefing: Asia and Australia Edition

• The Edit

• Nicholas Kristof

• Evening Briefing

• Morning Briefing: Americas

• What We’re Reading

• The Upshot

• Today’s Headlines

• Breaking New Alerts

• New York Today

• First Draft

• Opinion Today

• Today’s Headlines European Morning

• Today’s Headlines Asian Morning

• Booming

• @Times

• Running

• Science Times

• The New York Times Magazine

• NYT Living

• T Magazine

• Real Estate

• Cooking

• Book Review

• Big City Book Club

• Movie Updates

• Theater Update

• Travel Dispatch

• Times Video

• Wheels

• Well

• The Learning Network

• Well Family

• Sports

• The Interpreter

• Canada Today

• California Today

• Louder

• Op-Docs

• Watching

• Race/ Related

Business and tech

• Dealbook

• Bits

• Your Money

• Personal Tech

• Entrepreneurship

Special offers

• Sophisticated Shopper

• Great Getaways

• Travel Deals

• TicketWatch

• The New York Times Store

• Times Journeys

• Updates and

Special Offers

22

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Product case study 2: Website

Product Overview Product overview3 Key facts

Insights to Consider

• ESPN’s main website, www.espn.com

• Central online hub for sports news, scores,

leagues, merchandise, and tickets from ESPN

• Homepage: Acts as a one-stop destination for

sports, teams, Fantasy, news, scores, and other

websites

• Personalization: Users can log-in and

personalize which teams/ sports are emphasized

on the homepage

• “Now Feed”: live Twitter-like feed of short-

form content on the side of the homepage

• League homepages: every major league (NFL,

NCAA) and most major sports (Baseball,

Basketball, Hockey) link from the main

homepage to their own homepage

• Offshoots: WatchESPN, ESPN Radio, and

Fantasy

• Ranking: #1 sports news website, #29 website

in the US, and #99 website worldwide based on

traffic4

• Engagement: 366M visits in Jan 2017, 10:52

average duration and avg 4 pages per visit4

• Traffic sources: 44% direct, 22% referrals, 17%

search, 10% social media, 6% email, <1% ads4

• Top referring sites: cnn.com, espn.go.com,

bleacherreport.com, amazon.com4

• Social media traffic: 42% FB, 21% Reddit4

• ESPN did a massive web overhaul in 2015 focused on mobile, personalization, fresh content and user journeys: The company did a full site revamp focused

on mobile (“Internally we think that browsing on your phone is the most beautiful, clean experience”), personalization (“I’m a Duke fan …. It’s a pretty profound

statement that Duke football sites on top of ESPN.com”), fresh content (Top Headlines constantly refreshing) and user journeys (“Each article bleeds into the next

one. If you’re in a clubhouse for the Cavaliers, the next one will come into a Cavaliers article too”)5

• Personalization playing an increasingly critical role: Recommended teams and leagues reflect a user’s location (without log-in); users can log-in and pick sports

and teams to emphasize on the homepage feed; preferences affect scoreboard, news and recommended videos

• Focus on making the website a stand-alone “destination”: “We are a destination network. People tune in to ESPN without even knowing what’s on,” notes

ESPN President John Skipper6; the website has been designed to serve users who want to browse the world of sports with no particular goal

• ESPN tailors content and tone to platform and audience: “Rather than force a unified ESPN style onto every social-media platform, the team takes care to learn

the local language of every territory of the Internet-experimenting with live feeds on its homepage, studying which stories fly furthest on Facebook, and practicing the

goofball patois of Snapchat”6. Different platforms vary in content length, format (e.g. ,photo, video, article), and frequency of new content

Sources: (1) ESPN Media Zone, “ESPN, Inc. Fact Sheet”(2) Fast Company, March 2015, “ESPN Just Redesigned Its Site For The First Time Since 2009—Here Are 4 Takeaways” (3)

www.espn.com (4) Similar Web, February 2017, “ESPN.com Analytics” (5) Fast Company, March 2015, “ESPN Just Redesigned Its Site For The First Time Since 2009—Here

Are 4 Takeaways” (6) The Atlantic, July 2015, “ESPN’s Plan to Dominate the Post-TV World”

Timeline

• 1995: ESPN launched www.espn.com1

• 2015: ESPN redesigned www.espn.com to

include team/sport personalization, mobile

compatibility, white space, live-stream updating,

and “infinite scrolling”2

23

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Product case study 2

Sources: (1) The Atlantic, July 2015, “ESPN’s Plan to Dominate the Post-TV World” (2) Similar Web, February 2017, “www.espn.com Analytics” (3) ESPN.com (4) Photos from

espn.com pages (4) ESPN mobile application, February 2017

INSIGHT: ESPN DID A MASSIVE WEB OVERHAUL IN 2015 FOCUSED ON MOBILE, PERSONALIZATION,

FRESH CONTENT AND USER JOURNEYS

Articles and videos lead into directly related content:

“Each article bleeds into the next one. If you’re in a

clubhouse for the Cavaliers, the next one will come

into a Cavaliers article too”

Old site incorporated many

similar elements but in a

way better suited to a

generic, desktop experience

Site as a “mobile first” feel

with one article, large text

and use of icons as links to

other parts of the site

The site varies significantly for

logged-in users based on

preferences: scores, news

items, articles highlighted, etc.

Scores, news and videos

refresh constantly (e.g.,

scores update without

refreshing the page)

24

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Product case study 2

Sources: Photo from espn.com

INSIGHT: PERSONALIZATION PLAYING AN INCREASINGLY CRITICAL ROLE

Easy to select favorite teams

to get more content on

those choices

Large and visible log-in

button

Sign-up button given

prominent with both

placement and color choice

(blue on white background)

Any user can select any

league or event they prefer

to see scores for that choice

(without logging in)

Users can login and decide which sports and teams are emphasized on their homepage feed (with suggestions based on geolocation).

Accessing ESPN.com from Los Angeles brings suggestions of adding the Lakers, Dodgers, and Angels as a favorite automatically on

the homepage while someone in Dallas will see content related to UT and the Dallas Cowboys.

Top headlines will vary

based on location (e.g.,

Dallas headline for

someone in Texas per

below)

25

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Product case study 2

Sources: (1) The Atlantic, July 2015, “ESPN’s Plan to Dominate the Post-TV World” (2) Similar Web, February 2017, “www.espn.com Analytics” (3) The Atlantic, July 2015, “ESPN’s

Plan to Dominate the Post-TV World” (4) Photos from espn.com (5) Photos from Similar Web, February 2017 (6) “www.espn.com Analytics”

INSIGHT: FOCUS ON MAKING THE WEBSITE A STAND-ALONE ‘DESTINATION’

Direct traffic accounts for

almost 50% of all traffic with

Referrals (22%) and Search

(17%) together accounting for

80%+ of the traffic

In 2013, ESPN President Skipper said, “We’re not looking for niche audiences… Instead, ESPN sought to maximize the odds that

whenever an American guy tuned into its flagship channel, he would see either a major sport, or coverage of a major story line on one of

ESPN’s rapidly multiplying talk shows”1 The website supports this with live scoreboards, the Now feed on the homepage, major sports

on the homepage (with niche sports accessible in dropdown menus), and strong keyword visits (“espn nba”, “espn nfl”)2

“We are a destination network. People tune in to ESPN without even knowing what’s on” according to ESPN President John Skipper3

Top keywords #4 and #5

(“espn nba” and “espn nfl”)

suggest ESPN has succeeded

in becoming a destination

site in the minds of its

potential audience

To p E S P N s e a r c h k e y w o r d s 2 :

1 ESPN

2 WATCHESPN

3 WATCH ESPN

4 ESPN NBA

5 ESPN NFL

Mail

6% Social

10%

Search17%

Referrals

22%

Direct 45%

26