This document discusses using metadata about the emotional qualities and experiences of content to improve content recommendations. It argues that targeting based on limited data leads audiences to feel pushed and resent brands. Instead, it proposes focusing on general interest content and using tags to describe the distinctive attitudes, emotions, means of revelation, and organizing ideas of content. This "emotionally intelligent" approach aims to match content based on the experiences audiences enjoyed. It outlines tagging content, setting up a recommendation engine, and monitoring/adjusting to improve over time based on analytics. The goal is to become more audience-centric and attuned to what kinds of experiences they seek.
Using content attractors and metadata to provide emotionally intelligent recommendations
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. 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. I know what you want: targeting
Images: screenshots from NYT
3
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. Targeting is about stopping us
from going where we were heading
Marketing team
Audience
members
Image skeet shooting (modified) from Wikipedia
5
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. 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. Big data targeting is blind to emotion
How can we make
recommendations more
emotionally intelligent?
Image: Anton Croos via wikipedia
8
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. What specifically is distinctive about the
content?
Adjectives =
emotions
Images: screenshot from All Music Guide, photo by Altroscroll via Wikipedia
14
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. Goal: use metadata tags to describe your
content experience
La Bella Principessa,
attributed to Leonardo
da Vinci
Image via Wikipedia
17
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. 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. 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. 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. 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. 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. 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. How many brands tag their content
according to their emotional qualities?
<topic> parenting </topic>
<attractor> funny </attractor>
<attractor> little known fact </attractor>
25
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. 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. 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. 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
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
When the New York Times launched its redesign earlier this year, people noticed the “recommended for you” items, and felt they are wildly wrong. Even the Time’s public editor wrote that based on what she was reading, the Times recommendations suggested she wasn’t a fun person. This sentiment is common reaction to recommendations – they they are emotionally stupid.
Ironically, people pay attention to recommendations mostly when they are comically bad. It’s a widely acknowledged problem, but few solutions have been suggested that don’t involve more detailed targeting: getting even more data about customers to try to make the recommendations seem smarter.
Targeting is a bit like skeet shooting. Those doing the targeting try to predict where the target is going, and interrupt it. The audience can feel like a clay pigeon whose trajectory is calculated and tracked. Targeting is only helpful if when we are interrupted, we no longer need to go where we were heading. That is rarely the case.
Bad recommendations turn out badly. Instead of building a relationship, they can alienate. Most of the time the recommendations are not based much information about me at all. But it is especially annoying when a site knows a lot about me because I use their site often and I provide them with personal information, and they make time-wasting recommendations. For example, LinkedIn knows a lot about me, and they bought a content recommendation service called Pulse, but I have never found these recommendations relevant at all.
Niche content is often “find, get, forget” utilitarian information
Big data isn’t going to help you connect with what matters most to your customers: what they really value.
People often have trouble saying what they like. The key is to understand what content resonates for the audience, and to know how your content is valued. When they find content they enjoy, they identify with it. You want the content you recommend to be aligned with the current frame of mind of the audience. When people discover something they really like, they want more like it. We see this with binge viewing of TV series.
Brands know that general interest content can be popular, but they often fall in the habit of relying on topics to determine what content is relevant to audiences. Topics force us to get specific about our interests. But when when audiences want to relax, feel informed, or be entertained, they often seek general interest content. General interest content is content that audiences might find interesting even if they weren’t searching for it specifically. General interest topics have wide appeal, but need to be distinctive to be interesting. It helps people live richer lives without trying to sell them anything. Done right, it can help build a relationship with the brand.
Making good recommendations can be tricky. Recommendations should focus on what makes content sound unique and distinctive, unlike most of the other content addressing the same broad topic. Distinctive content is both attractive to audiences, and differentiating for brands. You want your recommendations to be emotionally aligned with the audience. So how do publishers do this now?
If you enjoy show or column, you problem like many items in the series because of the distinctive approach they take to a general interest topic. Such author sub-brands work best for loyal viewers or readers, and when there are fixed set of authors and products. It is less adaptable to content written by diverse authors, and it doesn’t help audiences discover content created by other authors.
We need to move away from data literalism. Even wordless songs can be described in words. Some interesting examples of looking at content attractors comes from film and music. Netflix and Pandora both look at the content attractors to help make recommendations to their audiences. This allows them to get more specific than simply relying on genres.
Recall that the New York Times recognized they had trouble with their recommendations. They are looking at how better metadata can improve relevance. One specific new kind of metadata they are considering is the story tone. According to the much-discussed leaked internal innovation report from this spring, The Times is seeking to match the tone of the content with the mood of the audience.
If your content is going to seem different to audiences, you need to know what is different about it. Specifically, you want to think about how different items of content should be different, to appeal to people in unique ways.
When we go to an art museum, we generally aren’t looking for artistic works about specific subjects. We are waiting to discover something with special qualities that produce a reaction in us. When we look at our content, we want to think how we can describe the qualities that make it unique.
If you are unsure what content you offer that’s general interest, check your analytics to see what’s popular and what content gets viewed independent of a direct search. For many brands, general interest content will only be a small subset of all their content. General: It needs to have wide appeal. Interest: it can’t sound like the same stuff everyone is saying. Examples could be travel, culture, careers, parenting, personal finance, or retirement.
Try to identify two or three qualities that make an item of content distinctive. For some content it may seem challenging to figure out what makes it distinctive. If you aren’t sure, you have a great opportunity to do some research with the audiences viewing your content. Ask them what they most like about the content, why they prefer some content to other content on the same topic. These conversations can offer insights into how they value your content, and what they’d like more of.
When you know what your content attractors are, you can tag your content. These tags describe the content experience. The metadata indicating the content attractors will enable you to make better recommendations. Instead, many brands try to push this kind of content through short-lived campaigns, before moving on to another campaign theme.
When you have tagged your content with the key attractors, you can use this information to build a recommendation engine. The essential idea is to suggest other content related to the same broad topic that has the same qualities. You may not know you the person is, or why they came to your site, but you know they have reached the bottom of an article, and presumably liked it enough to read through it. So why not suggest something else that has a similar vibe? A recommendation made immediately after someone has indicated interest can be far more effective than looking at historical behavioral data of a person whose interests may have moved on. It’s a simple heuristic: show people something similar to what they indicated they just liked.
This is important: data *is* valuable provided it captures stuff that matters. You will want to measure the use of this content, and use this information to fine-tune your approach. Perhaps you have popular content, but recommendations don’t seem to increase follow-on views. You may need to re-examine your tags to make sure they capture the spirit of the content accurately. If you do have a flavor of content that is enjoying popularity, perhaps you want to offer more content like that. You can monitor the popularity of different content attractors to guide development of new content.
It important to know not only what the color is, but what shade the color is.