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Using content attractors to overcome
indifference
Metadata for making more emotionally intelligent
recommendations
Michael...
You got their attention: now what?
People like your engaging
article. It’s wildly popular,
getting recommended, and
you ar...
I know what you want: targeting
Images: screenshots from NYT
3
Targeting: trying to predict people
“Personalization still isn’t that
good. Consumers still talk
about it mostly when it’s...
Targeting is about stopping us
from going where we were heading
Marketing team
Audience
members
Image skeet shooting (modi...
Audiences have problems with
targeting
The brand presumes to
know I what want based
on limited knowledge of
me
That feels ...
Targeting causes problems for brands
Target
Segment
✓Data
✓Data
✓Data
Pursuing a defined
niche, a narrow
customer segment ...
Big data targeting is blind to emotion
How can we make
recommendations more
emotionally intelligent?
Image: Anton Croos vi...
Triangle of attraction
emotionally
intelligent
content
Recommendatio
ns offered
Content
available
Audience
desires
9
Audience desires
Focus on emotional
intent, not logical intent
Image: screenshot from yummly
10
Content available
Focus on general interest
content, not specific
“niche” topics
Not much
can fit
here
Image by Marc ROUSS...
Recommendations
Match
distinctive content qualities content experiences
enjoyed
John Weich
Storytelling on
Steroids
“Peo...
“Author sub-brand” silos as ways to attract
audiences
Traditional
publishers have
relied on having
audiences follow
distin...
What specifically is distinctive about the
content?
Adjectives =
emotions
Images: screenshot from All Music Guide, photo b...
Insight: better metadata
= better recommendations
NYT innovation report, March
2014
15
Your content should sound
distinctive
Brand Voice Situational Tone
Content attractors
(stuff your audience cares about)
Fo...
Goal: use metadata tags to describe your
content experience
La Bella Principessa,
attributed to Leonardo
da Vinci
Image vi...
Process for better recommendations
① Identify your general interest content
② Identify qualities of your content and tag
y...
Identify general interest content
Content that audiences might find
interesting even if they weren’t searching
for it spec...
Find the nectar:
identify & tag content attractors
What’s most distinctive about your
content?
What do audiences most rela...
Does your content have a distinctive
attitude?
Attitudes
of
content
Authoritative – access the most reliable information
E...
Does your content offer a unique
experience?
Emotions
produced –
experiential
qualities
Empowering - builds confidence
Una...
Does your content show things differently?
Means of
revelation
Visual essay -- Soak up the scenery (image heavy)
Confessio...
Does your content highlight certain aspects
in a special way?
Organizing
idea
Lessons learned
Biographical stories
Situati...
How many brands tag their content
according to their emotional qualities?
<topic> parenting </topic>
<attractor> funny </a...
Set up an emotionally intelligent
recommendation engine
If person views....
General interest topic
(example: careers) with...
Monitor and adjust
AdjustMonitor
Don’t mess with success
Change what’s not working
Use analytics to
measure what
content q...
Distinctive content requires an approach that
values distinctions
Be attuned to what kinds of experiences audiences
seek
B...
Initial steps to a new approach
 Start with a small set of content
 Experiment
 Share what you learn with your team
 C...
Thank you!
Michael Andrews
Blog: storyneedle.com
@storyneedle
30
Slide Credits
5 Wikipedia: http://upload.wikimedia.org/wikipedia/commons/3/36/Skeet.gif
6 K Lorenz (cropped) via Wikipedia...
Additional resources
Content attractors
 “Understanding content attractors to improve content recommendations”
http://csf...
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Content attractors: Metadata for making more emotionally intelligent recommendations

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How to improve content recommendations and relevance to audiences by using content attractors, metadata representing emotional qualities of content. Presentation at CS Forum 2014 Frankfurt.

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Content attractors: Metadata for making more emotionally intelligent recommendations

  1. 1. Using content attractors to overcome indifference Metadata for making more emotionally intelligent recommendations Michael Andrews, Content Strategist Story Needle | Rome, Italy @storyneedle 1
  2. 2. You got their attention: now what? People like your engaging article. It’s wildly popular, getting recommended, and you are attracting many first-time visitors. What do you do next? A: Randomly show another article B: Try to “convert” them C: Show them something else they will be interested in. 2
  3. 3. I know what you want: targeting Images: screenshots from NYT 3
  4. 4. Targeting: trying to predict people “Personalization still isn’t that good. Consumers still talk about it mostly when it’s laughably bad.” Image: screenshot from HBR 4
  5. 5. Targeting is about stopping us from going where we were heading Marketing team Audience members Image skeet shooting (modified) from Wikipedia 5
  6. 6. Audiences have problems with targeting The brand presumes to know I what want based on limited knowledge of me That feels pushy, so I ignore the recommendations If they keep getting it wrong, I resent brand Image K Lorenz (cropped) via 6
  7. 7. Targeting causes problems for brands Target Segment ✓Data ✓Data ✓Data Pursuing a defined niche, a narrow customer segment or specific topical niche Doesn’t help people discover content they might want but don’t know about Not useful for general interest content ✗ ✗ 7
  8. 8. Big data targeting is blind to emotion How can we make recommendations more emotionally intelligent? Image: Anton Croos via wikipedia 8
  9. 9. Triangle of attraction emotionally intelligent content Recommendatio ns offered Content available Audience desires 9
  10. 10. Audience desires Focus on emotional intent, not logical intent Image: screenshot from yummly 10
  11. 11. Content available Focus on general interest content, not specific “niche” topics Not much can fit here Image by Marc ROUSSEL via 11
  12. 12. Recommendations Match distinctive content qualities content experiences enjoyed John Weich Storytelling on Steroids “People read what interests them.” 12
  13. 13. “Author sub-brand” silos as ways to attract audiences Traditional publishers have relied on having audiences follow distinct programs or columnists Example: CNN Images: screenshots of CNN.com 13
  14. 14. What specifically is distinctive about the content? Adjectives = emotions Images: screenshot from All Music Guide, photo by Altroscroll via Wikipedia 14
  15. 15. Insight: better metadata = better recommendations NYT innovation report, March 2014 15
  16. 16. Your content should sound distinctive Brand Voice Situational Tone Content attractors (stuff your audience cares about) Foundation: Styles of talking (consistent and generic to all content) Differentiation: How you connect with audiences (variable and specific to particular content) Attitudes of the content Emotional experience of the content The organizing idea How the story is revealed 16 Your creativity Your style guide
  17. 17. Goal: use metadata tags to describe your content experience La Bella Principessa, attributed to Leonardo da Vinci Image via Wikipedia 17
  18. 18. Process for better recommendations ① Identify your general interest content ② Identify qualities of your content and tag your content ③ Set up your recommendation engine ④ Monitor and adjust 18
  19. 19. Identify general interest content Content that audiences might find interesting even if they weren’t searching for it specifically. Image: Alistair Young via Flickr 19
  20. 20. Find the nectar: identify & tag content attractors What’s most distinctive about your content? What do audiences most relate to? Image ForestWander via Wikipedia 20
  21. 21. Does your content have a distinctive attitude? Attitudes of content Authoritative – access the most reliable information Exclusive – preview privileged info Trust our picks – we've found the best for you Contrarian – don't rely on conventional wisdom We make the difficult approachable Visionary - show how future will be different Championing, crusading – acts as an ombudsman Practical – you get only stuff you can use Thought leading –the best thinking of best experts 21
  22. 22. Does your content offer a unique experience? Emotions produced – experiential qualities Empowering - builds confidence Unafraid of controversy Clarifying – the bare truth exposed Aspirational – what you want Funny Celebratory – something to appreciate Surprising – discover something unexpected Emotionally inspiring – uplifting Motivating – seems possible, tempted to try Challenging – see things in a new light Calming - made worrying topic less anxious 22
  23. 23. Does your content show things differently? Means of revelation Visual essay -- Soak up the scenery (image heavy) Confessional - what I learned from my mistakes Guided tour by celebrity or expert host Behind the scenes at someplace familiar On location somewhere unfamiliar - you are there Spotlight on -- bring attention to something generally in background Finding the perfect combinations – these things belong together Interview - in their own words Myth-busting - The Reality of ________ Imaginative : What would it be like if... Intimacy: True stories of people who _______ 23
  24. 24. Does your content highlight certain aspects in a special way? Organizing idea Lessons learned Biographical stories Situational anecdotes Little know facts Explanatory – why things are Break free from the ordinary Weird but true stories or fact Understand through analogies Critical moments: turning point events Then and now (continuity and change) Below the surface – what you don't see Wise advice – how to live well 24
  25. 25. How many brands tag their content according to their emotional qualities? <topic> parenting </topic> <attractor> funny </attractor> <attractor> little known fact </attractor> 25
  26. 26. Set up an emotionally intelligent recommendation engine If person views.... General interest topic (example: careers) with Attractor A: aspirational Attractor B: biographical Then recommend... Other content on same topic (careers) with Attractor A: aspirational Attractor B: biographical 26
  27. 27. Monitor and adjust AdjustMonitor Don’t mess with success Change what’s not working Use analytics to measure what content qualities are in demand, and what recommendations are effective Analyze and adjust the tags, the recommendation matching, and even the general interest content itself based on these insights 27
  28. 28. Distinctive content requires an approach that values distinctions Be attuned to what kinds of experiences audiences seek Become more audience-centric on a given topic by knowing what audiences like and don’t like Know your content better, and improve what you offer 28
  29. 29. Initial steps to a new approach  Start with a small set of content  Experiment  Share what you learn with your team  Collaborate with colleagues in the CS community 29
  30. 30. Thank you! Michael Andrews Blog: storyneedle.com @storyneedle 30
  31. 31. Slide Credits 5 Wikipedia: http://upload.wikimedia.org/wikipedia/commons/3/36/Skeet.gif 6 K Lorenz (cropped) via Wikipedia http://commons.wikimedia.org/wiki/File:Lorenz_emotions.png 8 “Mother’s Love” by Anton Croos via Wikipedia http://upload.wikimedia.org/wikipedia/commons/8/85/Mother's_love.jpg 11 Marc ROUSSEL via Wikipeida http://commons.wikimedia.org/wiki/File:Amiens_niche_de_mitoyenneté_1.jpg 14 Altroscroll via Wikipedia: http://commons.wikimedia.org/wiki/File:Technics_SL-1600_turntable.JPG 17 Wikipedia: http://upload.wikimedia.org/wikipedia/commons/f/f9/Profile_of_a_Young_Fiancee_-_da_Vinci.jpg 19 Alistair Young via Flickr: http://www.flickr.com/photos/ajy/3979940998/ 20 ForsterWander [www.ForestWander.com] via Wikipedia: http://commons.wikimedia.org/wiki/File:Bee- gathering_pollen_yellow-flower-macro.jpg Image Credits 31
  32. 32. Additional resources Content attractors  “Understanding content attractors to improve content recommendations” http://csforum.eu/articles/content-attractors-to-improve-content- recommendations  “Improving content discovery through typologies” http://storyneedle.com/improving-content-discovery-typologies-2/ Netflix content classification approach  “How Netflix Reverse Engineered Hollywood” http://www.theatlantic.com/technology/archive/2014/01/how-netflix-reverse- engineered-hollywood/282679/ 32

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