Step-by-step guide to writing media pitches to gain press attention, getting your brand noticed, and measure public relations success without the PR flack. Especially relevant for the tech and startup industry, but with key insights for B2B and B2C consumer brands. Includes key steps to writing a great pitch, sample templates and common missteps.
Also features quick tips on how to measure PR success by utilizing coverage tracking, mentions, Google Analytics, and branded searches.
More information at: www.pinegrovepr.com
Want to get in touch?
ashley@pinegrovepr.com
Written and published by Pinegrove Public Relations, Seattle, WA, all content belongs directly to PPR.
Here's the recap of my in-class presentation for the 9th session for the (2009) "Future of Advertising" course at the Minneapolis College of Art and Design (MCAD). On March 23 we got granular and talked about Data, but not just on obsessive detail. Instead, we focused on the idea that data can help propel better stories, more effective media and more useful technology. Many thanks to Patty Henderson from Magnet360 for stopping by to share her perspective; and a big thanks to Chris Wexler and Kristen Findley for sharing their links and resources. Please note the Creative Commons license. Thanks.
So you want to identify the numbers that move your business' bottom line AND the numbers that move your readers. You want to know how, in a sea of data, you can select a few reasonable metrics that really matter today and take action based upon what they tell you. We're here to help. We'll discuss what metrics matter, how they should influence your decision-making, and what metrics tools should look like in five years' time. We'll be sure to share tips and slides so you can put our practical advice to use right away.
Step-by-step guide to writing media pitches to gain press attention, getting your brand noticed, and measure public relations success without the PR flack. Especially relevant for the tech and startup industry, but with key insights for B2B and B2C consumer brands. Includes key steps to writing a great pitch, sample templates and common missteps.
Also features quick tips on how to measure PR success by utilizing coverage tracking, mentions, Google Analytics, and branded searches.
More information at: www.pinegrovepr.com
Want to get in touch?
ashley@pinegrovepr.com
Written and published by Pinegrove Public Relations, Seattle, WA, all content belongs directly to PPR.
Here's the recap of my in-class presentation for the 9th session for the (2009) "Future of Advertising" course at the Minneapolis College of Art and Design (MCAD). On March 23 we got granular and talked about Data, but not just on obsessive detail. Instead, we focused on the idea that data can help propel better stories, more effective media and more useful technology. Many thanks to Patty Henderson from Magnet360 for stopping by to share her perspective; and a big thanks to Chris Wexler and Kristen Findley for sharing their links and resources. Please note the Creative Commons license. Thanks.
So you want to identify the numbers that move your business' bottom line AND the numbers that move your readers. You want to know how, in a sea of data, you can select a few reasonable metrics that really matter today and take action based upon what they tell you. We're here to help. We'll discuss what metrics matter, how they should influence your decision-making, and what metrics tools should look like in five years' time. We'll be sure to share tips and slides so you can put our practical advice to use right away.
business model, business model canvas, mission model, mission model canvas, customer development, hacking for defense, H4D, lean launchpad, lean startup, stanford, startup, steve blank, pete newell, bmnt, entrepreneurship, I-Corps, Deep Fakes
The media industry is well positioned. They could have "better" data (in the meaning of better for placing advertisements) than many social networks. However they need to embrace data and become a data driven organization.
Lutz Finger analyzes in his talk (video: http://youtu.be/mzWBXUc1QWo) the two main worries the media industry has today: Reach / Expertise - in both areas they are faced with competition from new social media channels.
He shows that BigData and Analytics is the only way out of the issue. Media companies could become the leader in Media Data and in Media Analytics.
Amplification is about catching bigger waves
What blogs do you read?
Most corporate blogs are • Boring • Bland • Safe What kind of blogs do you read? • Opinionated • Surprising • Have a point of view • Teach you something
What is Content Marketing?
How important is content marketing? Content wins for most effective strategy; but it’s also the hardest to implement
Amplification = content x channel
What is Amplification?
Example: Catching the wave in 2013 Original post: 2011
What is Amplification?
Section Two: Research
Start with People
People first The first question you need to ask is: “who do I want to reach?” Section Two Every good product, website, campaign, and piece of writing starts with people in mind.
How do you choose your audience?
Technique #2: draw a venn diagram
Our target audience is: __________________
Finding pain
Section Two When you understand your audience’s pain you’ll be able to better define their problem.
Example: pjrvs.com
Focus on their needs
Observe people in their habitat
Where do people hang out?
Research examples: where would you find managers?
What are you looking for? BIG problems trends pain points frustrations
Observing trends
Yesterday I saw this tweet:
He’s tweeting about this site: The Grid
He’s not happy about it
A lot of other designers don’t like it either
I go to Designer News:
Where have I seen this before?
What’s the trend we’re observing? Designers are feeling threatened.
This takes time. Collect and organize your research.
Sample research I’m using an app called keeeb.com to track trends.
Listen
Focus on your audience’s biggest problems
Organize your research • Record your findings • Organize them into groupings • Identify a big pain / issue / topic you could focus on You grow an audience by targeting a topic people care about!
Section Three: Writing & Promotion 3
Write for your audience
Writing viral content is hard.
Great, shareable content…
Why do people share content?
Types of content
Amplify your content
Which train are you going to hitch your content to? Your own small network Someone else’s big network
Effects of amplification
Two types of amplifiers: 1
How do you build relationships with influencers?
How do you earn the right to on online communities? Add value Hacker News Designer News Product Hunt
Target the right networks
Coordinate your efforts
Example 1: Groovehq
Coordinate your launch
The game plan
A note on Hacker News, Designer News, Product Hunt, Reddit…
It typically takes about 20-30 minutes for a story to slip off the "New Submissions" page. That's your window to get enough votes to appear on the front page.
The algorithm on News Sites favours new content
Can you game the system?
Timing is important
We hebben allemaal de kracht van AI gezien in de hele wereld, maar hoe kan het uw KMO helpen groeien of hoe kan je ermee groeien als bedrijfsleider/ #sales en #marketing manager? Sluit u aan bij ons aankomende webinar 'De ultieme gids voor AI voor #KMO's en bedrijfsleider' en ontdek hoe bedrijven net als de uwe hun marketing- en salesinspanningen hebben getransformeerd met AI.
NEW Groundbreaking AI App Curates & Automatically Crafts High-Value Newsletters People Eagerly Read.
Transform Any Website or YouTube Video into a Newsletter Superior to Anything You Could Write Yourself
The Tech Behind the 'Newsletter Linchpin AI' App
Content Aggregation: The AI Curation Matrix scans multiple sources to gather relevant articles, blog posts, and other types of content based on predefined topics or keywords.
Quality Assessment: The algorithm evaluates the quality of the aggregated content based on factors like readability, relevance, and credibility.
Personalization: The matrix tailors the selection of content to match the preferences and behavior of the target audience, ensuring higher engagement.
Contextual Understanding: The AI understands the context in which certain content will be most effective, allowing for more targeted curation.
Duplication Avoidance: The matrix ensures that the same or similar content is not repeated, keeping the newsletter fresh and engaging.
Multi-Source Integration: The AI Curation Matrix can pull content from various platforms, including blogs, and news sites, offering a diverse range of information.
Real-Time Updates: The matrix constantly updates its content pool, ensuring that the newsletter includes the most current and relevant information.
Automated Summarization: The feature can automatically summarize long articles, providing concise yet informative content for the newsletter.
Idea Generation: The Idea Processor starts by generating a list of potential topics or themes based on user input, trending topics, or historical data.
Concept Mapping: The feature organizes the generated ideas into a conceptual map, showing how different ideas are related or can be combined for more comprehensive coverage.
Content Structuring: The Idea Processor helps in outlining the structure of the newsletter, suggesting where each idea or topic would fit best.
Relevance Scoring: Each idea is scored based on its relevance to the target audience, current trends, and the overall theme of the newsletter.
Idea Prioritization: The algorithm prioritizes ideas based on their scores, ensuring that the most impactful topics are covered first.
Content Suggestions: The processor can suggest internally content that would best convey each idea.
Gap Analysis: The feature identifies gaps or areas that lack sufficient coverage, prompting the user to explore additional topics or angles (only flows with user feedback).
What is a Creative Date Scientist (and why the $@%! do we need one?)Dave LaFontaine
This presentation was originally delivered to the SoCal UX Camp; it's designed to help "creatives" to get over their numbers-phobia, and instead start engaging with analytics.
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Less than 40% of marketers describe their content marketing programs as effective. To help understand what consumers want from their content, we conducted an in-depth consumer survey and used ScribbleLive Insights to analyze the effectiveness of a number of top brands.
In this presentation, Ural Cebeci and Larry Levy walk you through everything from consumers’ favorite content type (social media), to how much they trust branded content (44% don’t) and which brands are getting it right.
OSF x Highway Africa: New forms of storytelling, distribution and revenueThe Splice Newsroom
I presented this at a workshop at Rhodes University, Grahamstown in South Africa in August 2016. The goal was to help traditional media companies re-think the mass media model and consider new options in the search for sustainable revenue.
Your Customer's Journey in the Social EraTara Hunt
I presented this at the United Benefits Advisors' spring conference in May - to answer the question, "Why would benefits advisors use social media?" I presented it like this:
The customer journey is non-linear and unpredictable. It goes online/offline/and more. You need to be on that path in as many places as possible. Social is a good chunk of that now.
Team Networks - 2022 Technology, Innovation & Great Power CompetitionStanford University
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The media industry is well positioned. They could have "better" data (in the meaning of better for placing advertisements) than many social networks. However they need to embrace data and become a data driven organization.
Lutz Finger analyzes in his talk (video: http://youtu.be/mzWBXUc1QWo) the two main worries the media industry has today: Reach / Expertise - in both areas they are faced with competition from new social media channels.
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Amplification is about catching bigger waves
What blogs do you read?
Most corporate blogs are • Boring • Bland • Safe What kind of blogs do you read? • Opinionated • Surprising • Have a point of view • Teach you something
What is Content Marketing?
How important is content marketing? Content wins for most effective strategy; but it’s also the hardest to implement
Amplification = content x channel
What is Amplification?
Example: Catching the wave in 2013 Original post: 2011
What is Amplification?
Section Two: Research
Start with People
People first The first question you need to ask is: “who do I want to reach?” Section Two Every good product, website, campaign, and piece of writing starts with people in mind.
How do you choose your audience?
Technique #2: draw a venn diagram
Our target audience is: __________________
Finding pain
Section Two When you understand your audience’s pain you’ll be able to better define their problem.
Example: pjrvs.com
Focus on their needs
Observe people in their habitat
Where do people hang out?
Research examples: where would you find managers?
What are you looking for? BIG problems trends pain points frustrations
Observing trends
Yesterday I saw this tweet:
He’s tweeting about this site: The Grid
He’s not happy about it
A lot of other designers don’t like it either
I go to Designer News:
Where have I seen this before?
What’s the trend we’re observing? Designers are feeling threatened.
This takes time. Collect and organize your research.
Sample research I’m using an app called keeeb.com to track trends.
Listen
Focus on your audience’s biggest problems
Organize your research • Record your findings • Organize them into groupings • Identify a big pain / issue / topic you could focus on You grow an audience by targeting a topic people care about!
Section Three: Writing & Promotion 3
Write for your audience
Writing viral content is hard.
Great, shareable content…
Why do people share content?
Types of content
Amplify your content
Which train are you going to hitch your content to? Your own small network Someone else’s big network
Effects of amplification
Two types of amplifiers: 1
How do you build relationships with influencers?
How do you earn the right to on online communities? Add value Hacker News Designer News Product Hunt
Target the right networks
Coordinate your efforts
Example 1: Groovehq
Coordinate your launch
The game plan
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It typically takes about 20-30 minutes for a story to slip off the "New Submissions" page. That's your window to get enough votes to appear on the front page.
The algorithm on News Sites favours new content
Can you game the system?
Timing is important
We hebben allemaal de kracht van AI gezien in de hele wereld, maar hoe kan het uw KMO helpen groeien of hoe kan je ermee groeien als bedrijfsleider/ #sales en #marketing manager? Sluit u aan bij ons aankomende webinar 'De ultieme gids voor AI voor #KMO's en bedrijfsleider' en ontdek hoe bedrijven net als de uwe hun marketing- en salesinspanningen hebben getransformeerd met AI.
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Quality Assessment: The algorithm evaluates the quality of the aggregated content based on factors like readability, relevance, and credibility.
Personalization: The matrix tailors the selection of content to match the preferences and behavior of the target audience, ensuring higher engagement.
Contextual Understanding: The AI understands the context in which certain content will be most effective, allowing for more targeted curation.
Duplication Avoidance: The matrix ensures that the same or similar content is not repeated, keeping the newsletter fresh and engaging.
Multi-Source Integration: The AI Curation Matrix can pull content from various platforms, including blogs, and news sites, offering a diverse range of information.
Real-Time Updates: The matrix constantly updates its content pool, ensuring that the newsletter includes the most current and relevant information.
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Idea Generation: The Idea Processor starts by generating a list of potential topics or themes based on user input, trending topics, or historical data.
Concept Mapping: The feature organizes the generated ideas into a conceptual map, showing how different ideas are related or can be combined for more comprehensive coverage.
Content Structuring: The Idea Processor helps in outlining the structure of the newsletter, suggesting where each idea or topic would fit best.
Relevance Scoring: Each idea is scored based on its relevance to the target audience, current trends, and the overall theme of the newsletter.
Idea Prioritization: The algorithm prioritizes ideas based on their scores, ensuring that the most impactful topics are covered first.
Content Suggestions: The processor can suggest internally content that would best convey each idea.
Gap Analysis: The feature identifies gaps or areas that lack sufficient coverage, prompting the user to explore additional topics or angles (only flows with user feedback).
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I presented this at the United Benefits Advisors' spring conference in May - to answer the question, "Why would benefits advisors use social media?" I presented it like this:
The customer journey is non-linear and unpredictable. It goes online/offline/and more. You need to be on that path in as many places as possible. Social is a good chunk of that now.
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Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
2. Kaveh
PhD in Econ
Janel
B.S. Computer
Science
Michael
Creative Dir. at SF
Business Times
Titus
Investigative
journalist
Michelle
B.S. Computer
Science
The Team
3. News organizations haven’t been doing so well.
Advertising & subscription revenue
for U.S. newspapers (in billions)
60
50
40
30
20
10
4. Advertising & subscription revenue
for U.S. newspapers (in billions)
We wanted to do something about this.
CS 206: Computational
Journalism
60
50
40
30
20
10
6. News organizations
(e.g., NYT, WaPo)
Software
development
Reader-side platform
User data
Writer-side platform
Help news
organizations
attract new readers
and better engage
existing ones
Make the news more
informative and
enjoyable for
readers
KEEP: Customer
support
GET: Free testing
Direct / Sales Team
Software development Customer acquisition
Customer support
Annual licensing fee
Tech companies
with large amounts
of demographic data
(e.g., facebook)
Customer support
Help readers
understand different
perspectives
Direct / Internet
GROW: Upsell to
higher-end license
Original Business Model Canvas
News organizations
Readers
7. News organizations
(e.g., McClatchy,
NYT, WaPo)
UX design, learning
archetype engagement
User data collection
Writer-side platform
KEEP: Customer
support
Direct / Sales Team
News organizations
Annual licensing
fee structure
News agencies
Integrating with existing
news CMS
Direct / Internet
GROW: Upsell to
higher-end license
Predict archetypes
(branches) who
should get a different
version of the story
Provide framework
and structure to make
archetyping easy for
every story
Identify archetypes by
leveraging/clustering
user data already
collected by news orgs
Advertising, PR, and
marketing agencies
Parsing data, collecting
engagement stats
Analytics platforms
Ad server platforms
Publisher onboarding
Suite:
Premium features
Versioning for
other industries
Advertisers, PR, and
Marketing agencies
143 Interviews Later... Current Business Model Canvas
Software development Customer acquisition
Customer support
14. Experiment:
Do readers enjoy personalized news texts? Nope.
“That detail doesn’t
belong in this
article.”
“All this one
has is more
numbers.”
“Meh. This one
doesn’t flow well.”
“That statistic
doesn’t fit in.”
18. Did we
personalize the
wrong way?
Is personalized
text the wrong
solution?
Let’s understand one
customer segment really
well and personalize the
right way this time.
19. Who would be our early evangelists?
People who are...
1. Hungry for news
1. Dissatisfied with mainstream news
1. Making the extra effort to fill appetite
20. Who would be our early evangelists?
People who are...
1. Hungry for news
1. Dissatisfied with mainstream news
1. Making the extra effort to fill appetite
31. We went back to our vision, but with a twist.
This was from our Week 1 Presentation.
32. Then we took our MVP to news agencies, who said...
33. Irene Chang
Product Manager,
Text & Multimedia
It’s not a super huge need.
Maybe it’ll be more useful to
publishers?
Then we took our MVP to news agencies, who said...
“Meh.”
34. A solution for news agencies is a small market.
$600 millionSubscriptions cost
$10k to $1-2 million
We estimated our annual revenue to be $15 million.
Revenue of Top 3 in U.S.
35. With nothing to lose, we asked news executives
for thoughts on all our previous MVP iterations.
VS
MVP 1:
Personalize news texts
MVP 3:
Augment wire text
VS
MVP 2:
Automate style & standards
36. MVP 1:
Personalize news texts
…and they jumped out of their seats
for our very first iteration.
37. …and they jumped out of their seats
for our very first iteration.
Wait, what?
MVP 1:
Personalize news texts
38. Localization is really valuable, but at
the community level.
Daniel Schaub
Corporate Director of
Audience Development
We want our journalists to write
with a specific person in mind.
But there’s no structure in place
to suggest
that right now.
Tim Grieve
VP of News
What got them so excited?
39. Journalists & editors felt the same way.
Andre Taylor
Desk Editor
“If a story gets clicks or blows up, we
want to know how to do that again.
When something spikes,
it’s like Christmas.”
“People care about things you may not
suspect they’d click on.
Most of the time, the editor or reporter
thinks they know what people like.”
Arlene Washington
Digital Editor
40. Key Insight:
There’s a gap between the way journalists write
and the way publishers wish they would write.
To journalists and publishers, our original MVP
looked like a bridge between this gap.
41. Back to our Origins,
But Wiser this time
116
interviews
42. A Hunch
The original MVP tweaked sentences.
What really moves people is stories.
We need to inform writers about readers earlier
in the story writing process.
43. We changed how we pictured our readers
Demographic data → Archetypes, Occupations, Interests
(Consider the reader as a whole)
We changed how we personalized to our readers
Statistics & Detail → Tone, Structure, Anecdotes
(Consider the narrative as a whole)
47. Experiment: Do readers segmented by archetype enjoy
personalized news narratives? YES! “It helped me imagine the
actual product and made it
sound cool.”
(Tech)
“I like that it sounds like it is written
in a viewpoint of not needing to
advertise but to just write the facts
about the researchers' work.”
(Biology)
“I liked that it gave some
background on the condition
and some facts/figures...this
could very well be an issue of
interest though.”
(Biology) “It provided me the
opportunity to learn
more.”
(Tech)
“I liked it more because it had a
quote from Apple.”
(Tech)
50. Our idea:
We help journalists find
product-market fit for their stories.
51. Our idea:
We help journalists find
product-market fit for their stories.
How?
A writing tool that informs the writer
about their target audience during the
writing process.
52.
53. Advertising & subscription revenue
for U.S. newspapers (in billions)
We wanted to do something about this.
60
50
40
30
20
10
54. Advertising & subscription revenue
for U.S. newspapers (in billions)
We will do something about this.
60
50
40
30
20
10
55. Three of us will be continuing!
Janel
B.S. Computer
Science
Michael
Creative Dir. at SF
Business Times
Michelle
B.S. Computer
Science
59. Now our customers are news organizations.
$28 billionannual revenue (ads + subscription)
Pains: Subscriber model in a digital age.
Gains: Segment readers to drive subs and better target ads.
If we can address even 3% of this pool, that’s a $1B market.
60. Lesson 1: Good startups run like efficient code
while (product_market_fit==0) {
# define experiment;
# define threshold for
success;
if (result < threshold) {
next_step =
next_step_fail;
}
else {
next_step = next_step_pass;
}
}
Week Experiment Result
1 The Value of
Personalized News
Fail (18%)
3 Is There Unmet Need
for Crypto News?
Fail (67%)
4 The Value of
Personalized Crypto
News
Fail (44%)
... ... …
8 The Value of
Personalized News,
Part II
PASS!
(80%)
KD
61. Lesson 2: Like journalists, startups need key sources
Michelle Park
Interview Request from Stanford University Team
To: Schaub, Daniel (McClatchy)
-----------------------------------------------------------------
Hi Mr. Schaub,
I’m an engineering student at Stanford
working on a class project. I was hoping to
talk with you about how news agencies
could better serve readers.
...
Titus Plattner
Interview Request from Tamedia News
To: Johansen, Ed (MU)
-----------------------------------------------------------------
Hi Mr. Johansen,
I’m a journalist at Tamedia writing a story
on offshore tax havens. I would like to
talk with you about your company based in
the Cayman Islands.
...
KD
63. Tone: Excitement about new product
Structure: Surface tech-related information to the top
Anecdote: Incorporate quotes from leading technologists
Experiment: Do readers segmented by archetype enjoy
personalized news narratives?
64. Lesson 3: Passion is necessary (but not sufficient)
Crypto News
(MVP 2)
Social News
Personalized News Texts
(MVP 1)
KD
67. Kaveh Danesh Jihyeon Janel Lee Michael Grant Titus Plattner Michelle Park
MG
Econ PhD,
former Duke
Trustee, writer for
Obama, RA for Raj
Chetty, NCAA
Division I soccer
coach
CS Major,
Section Leader,
KPCB Engineering
Fellow, RA at
Stanford AI Lab,
Theater gal who
likes corgis
Knight Fellow,
Creative director
at the SF Business
Times, Lead SF
Chronicle, digital
incubator training
ground
Knight Fellow,
Investigative
reporter, created a
tool to share
terabytes of data
in newsrooms,
Swiss
CS Major,
CS Course Asst,
created platform
to crowdsource
education content,
intern at Apple,
Google, and NASA
68. Small market:
The three big wire agencies:
$600M per year for text in the US
(Subscriptions based on feeds + total readers, news orgs pay
btw. a few 10k to a 1-2 M. per year)
Content Cube max. revenue: 15M per year
=> “This is a hobby” said Steve W.
69. Interesting market:
Revenue of US newspapers ($28B in 2017)
If we even can address 3% of this market by driving more
subscriptions or better targeting ads, we would have a
$1B market.
1. For news orgs, getting more online subscriptions is key.
2. Better targeting online ads would allow news orgs to
compete again with the internet giants.
70. TAM:
Revenue of US newspapers in 2017:
Ads: $18B
Subscriptions: $10B
Total: $28B
SAM:
Bigger US newspapers revenue in 2017: $14B
Target:
Address about one fifth of this market: >$2B
Content Cube
helps on both
markets
}
Editor's Notes
add a hero’s journey slide
Once upon a time...
And every day...
Until one day...
Because of that...
Because of that...
Because of that...
Until, finally...
And, ever since then…
Put learnings on a timeline
What parts should be well illustrated
Overlay the storytelling
The plummeting curve
Journalism class
Naive but hopeful
Carried out with us a shining MVP
The plummeting curve
Journalism class
Naive but hopeful
Carried out with us a shining MVP
We created a writing tool that allows journalists to personalize sentences to different readers based on their demographics.
key learnings included x, y, and z
added a lot more yellow boxes
-completely revised value prop, customer segment and customer relationship
-detail
-clarity of what you’ve learned
-we started by not knowing much, now we know a lot
-put green around stuff you were right about, yellow around stuff that changed after 143 interviews.
key learnings included x, y, and z
added a lot more yellow boxes
-completely revised value prop, customer segment and customer relationship
-detail
-clarity of what you’ve learned
-we started by not knowing much, now we know a lot
-put green around stuff you were right about, yellow around stuff that changed after 143 interviews.
And so our journey began. The first thing we did was test our assumptions about our original idea. Would readers even enjoy personalized news texts?
So we ran an experiment. We took the breast cancer article shown in our tool and personalized it to two different demographics.
YAF and OWM. We asked them to read both the standard and personalized versions.
key learnings included x, y, and z
added a lot more yellow boxes
-completely revised value prop, customer segment and customer relationship
-detail
-clarity of what you’ve learned
-we started by not knowing much, now we know a lot
-put green around stuff you were right about, yellow around stuff that changed after 143 interviews.
key learnings included x, y, and z
added a lot more yellow boxes
-completely revised value prop, customer segment and customer relationship
-detail
-clarity of what you’ve learned
-we started by not knowing much, now we know a lot
-put green around stuff you were right about, yellow around stuff that changed after 143 interviews.
Not only did they not like it, they really hated it. They did
key learnings included x, y, and z
added a lot more yellow boxes
-completely revised value prop, customer segment and customer relationship
-detail
-clarity of what you’ve learned
-we started by not knowing much, now we know a lot
-put green around stuff you were right about, yellow around stuff that changed after 143 interviews.
This is what we asked ourselves. Saying yes to the question on the right broke our hearts, so we decided to take a guess at the question on the left. Maybe we didn’t know how people wanted their articles personalized.
This is what we asked ourselves. Saying yes to the question on the right broke our hearts, so we decided to take a guess at the question on the left. Maybe we didn’t know how people wanted their articles personalized.
This is what we asked ourselves. Saying yes to the question on the right broke our hearts, so we decided to take a guess at the question on the left. Maybe we didn’t know how people wanted their articles personalized.
People who have a high information diet but aren’t getting satisfied from mainstream news.
This is what we asked ourselves. Saying yes to the question on the right broke our hearts, so we decided to take a guess at the question on the left. Maybe we didn’t know how people wanted their articles personalized.
This time, we wanted to really understand our customers. We talked to many cryptocurrency investors and found a natural grouping amongst them
We had the coin founders and the early investors
Then we had the experienced investors, who had hopped over from the finance space.
And finally, the finance and crypto novices who started investing because of the hype.
They were having a really hard time finding credible news sources, and told us the learning curve for crypto was uncomfortably high.
We spent a lot of time listening to their pain points and started mocking up solutions for the biggest pains.
But in the midst of this, something felt wrong.
This unease came together during one of our weekly presentations.
Steve Blank asked us how much we would charge for our product, and we nervously said “ummm, $20 a month?”
He immediately asked us: “Do you even believe in your product?” “Do any of you care enough about this space to buy a coin?”
And he was completely right. We sat down afterwards and realized that none of us were passionate about crypto, and it was eating us alive.
After all, as our instructors say, “If we aren’t excited about our idea, how will we convince other people to love it?”
And thus ended our brilliant but short-lived dive into cryptocurrency.
key learnings included x, y, and z
added a lot more yellow boxes
-completely revised value prop, customer segment and customer relationship
-detail
-clarity of what you’ve learned
-we started by not knowing much, now we know a lot
-put green around stuff you were right about, yellow around stuff that changed after 143 interviews.
It takes a long time for publishers to take content from news agencies and customize it to their own style and standards. After talking to a lot of wire editors, we found a lot of this could be automated, and it would enable news agencies to offer more value to their clients.
Because of that, thought deeply about it
Because of that, made pivot
Because of that, thought deeply about it
Because of that, made pivot
Because of that, thought deeply about it
Because of that, made pivot
Because of that, thought deeply about it
Because of that, made pivot
We were confused because we thought readers hated it. But somehow, news organizations seemed to love it.
For example, executives at McClatchy were already thinking about personalizationBut it wasn’t tweaking statistics. It was about creating stories that resonate with specific people.Tax plan change, you would tell the entire narrative differently to blah
So we talked to journalists and editors too, and they also wanted to know more about who they should write too. But like Tim said, they were only taking a guess at
key learnings included x, y, and z
added a lot more yellow boxes
-completely revised value prop, customer segment and customer relationship
-detail
-clarity of what you’ve learned
-we started by not knowing much, now we know a lot
-put green around stuff you were right about, yellow around stuff that changed after 143 interviews.
key learnings included x, y, and z
added a lot more yellow boxes
-completely revised value prop, customer segment and customer relationship
-detail
-clarity of what you’ve learned
-we started by not knowing much, now we know a lot
-put green around stuff you were right about, yellow around stuff that changed after 143 interviews.
key learnings included x, y, and z
added a lot more yellow boxes
-completely revised value prop, customer segment and customer relationship
-detail
-clarity of what you’ve learned
-we started by not knowing much, now we know a lot
-put green around stuff you were right about, yellow around stuff that changed after 143 interviews.
key learnings included x, y, and z
added a lot more yellow boxes
-completely revised value prop, customer segment and customer relationship
-detail
-clarity of what you’ve learned
-we started by not knowing much, now we know a lot
-put green around stuff you were right about, yellow around stuff that changed after 143 interviews.
tried occupation because thought of biggest things that shape people as a whole
We took an article about a collaboration between Apple watch and Stanford medicine
& personalized it two ways.
And again 3 people liked it
This time, they were in the majority.
They mentioned they strongly preferred the personalized version because of tone and anecdotes, confirming our hunch.
We also learned not to make broad assumptions about our experiment results.
Because of that, thought deeply about it
Because of that, made pivot
Because of that, thought deeply about it
Because of that, made pivot
Because of that, thought deeply about it
Because of that, made pivot
The plummeting curve
Journalism class
Naive but hopeful
Carried out with us a shining MVP
The plummeting curve
Journalism class
Naive but hopeful
Carried out with us a shining MVP
Because of that, thought deeply about it
Because of that, made pivot
add a hero’s journey slide
This is what we asked ourselves. Saying yes to the question on the right broke our hearts, so we decided to take a guess at the question on the left. Maybe we didn’t know how people wanted their articles personalized.
One key lesson from the course has been the importance of a scientific mindset to finding product-market fit. It took us many tries to pass an experiment, which was difficult and frustrating. But we stuck to the script and continued working in an almost code-like way: define the experiment that tests the key assumption, define the threshold for success, define next steps conditional on success and failure. And eventually we got results that did not reject our hypotheses.
Another key lesson was the importance of finding good sources. In the beginning of the semester, we were mostly interviewing friends or friends of friends, but by the end we were sending cold emails or LinkedIn messages to people we never would have thought to contact. What’s funny is that this is exactly what journalists do. They don’t settle for interviews with people who don’t understand the subject of the story. They make bold calls and send bold emails in order to dig up the truth. And that is what we started learning to do over the course of the semester.
This is what we asked ourselves. Saying yes to the question on the right broke our hearts, so we decided to take a guess at the question on the left. Maybe we didn’t know how people wanted their articles personalized.
key learnings included x, y, and z
added a lot more yellow boxes
-completely revised value prop, customer segment and customer relationship
-detail
-clarity of what you’ve learned
-we started by not knowing much, now we know a lot
-put green around stuff you were right about, yellow around stuff that changed after 143 interviews.
Finally, we learned that while passion, ability to contribute, and economic viability are all important components for a successful startup, none of them is sufficient on its own. We started the semester with a tool that personalized news texts, and we were pretty confident that we execute that as well as anyone. But when we heard that people didn’t seem to want that, we thought about making news more social—something we were passionate about—before pivoting to where we thought the money was (and you all know how that went). We feel our current idea strikes a nice balance between all three elements, in a way that sets us up to take this curve -- *next slide* -- and try to flip it upside-down -- *next slide.*
Once upon a time...
Every day...
But, one day...
Because of that...
Because of that...
Because of that...
Until, finally...
And, ever since then...
Once upon a time...
Every day...
But, one day...
Because of that...
Because of that...
Because of that...
Until, finally...
And, ever since then...
http://www.journalism.org/fact-sheet/newspapers/
Ads in 2017: 18B + circulation 10B