Content attractors: Metadata for making more emotionally intelligent recommendations
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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.

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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  • 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. <br />
  • 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. <br />
  • 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. <br />
  • 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. <br />
  • It important to know not only what the color is, but what shade the color is.

Content attractors: Metadata for making more emotionally intelligent recommendations Presentation Transcript

  • 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
  • 9. Triangle of attraction emotionally intelligent content Recommendatio ns offered Content available Audience desires 9
  • 10. Audience desires Focus on emotional intent, not logical intent Image: screenshot from yummly 10
  • 11. Content available Focus on general interest content, not specific “niche” topics Not much can fit here Image by Marc ROUSSEL via 11
  • 12. Recommendations Match distinctive content qualities content experiences enjoyed John Weich Storytelling on Steroids “People read what interests them.” 12
  • 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
  • 15. Insight: better metadata = better recommendations NYT innovation report, March 2014 15
  • 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
  • 30. Thank you! Michael Andrews Blog: storyneedle.com @storyneedle 30
  • 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. 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