Understanding content attractors to improve content recommendations
It’s possible to improve content recommendations without using conventional targeting approaches. Identifying the essential emotional attractors in your general interest content can improve how audiences relate to your recommendations. This article explains what a content attractor is, and how to use them to make more effective content recommendations.
Content publishers want their content to reach a broad audience and be used, and often recommend content to customers. They rely on targeting to match the correct content with the exact audience segment that is looking for it. Sadly, targeting often fails. Audiences don’t consider the recommendations relevant.
Limitations of targeting
Publishers utilise two common types of targeting. When targeting audiences, the publisher tries to determine precisely who is viewing its content — or who they want to be viewing it. They create content that addresses the presumed interests of a segment, or else adapt content to match the preferences of the segment, for example, by changing the messaging of an offer. Audience targeting requires collecting details about the audience, and making assumptions about their interests and motivations based on known information about them.
Another form of targeting is keyword targeting, which examines what keywords audiences are using to look for content. The publisher creates content that talks about specific keyword search terms that the audience uses to find content. Common search keywords are very popular and will be pursued by other competitors, so publishers often rely on combinations of keywords that people use that aren’t as common.
Whether targeting focuses on audience characteristics, or the keywords associated with their searches, it involves pursuing a defined niche — either an audience segment niche, or a specific topic niche. Matching niche content with a niche audience segment can work well, but the tactic also has limitations. When content is focused on a narrow, specific topic, it only attracts a limited audience. Topics with broad interest will generally attract bigger audiences, provided the content is distinctive and unique.
Audiences may find targeting pushy. Targeting assumes that people who match certain criteria will be interested in particular content, but such assumptions can be questionable. Even if the publisher knows the search terms that brought someone to its site, it doesn’t necessarily know that person’s true intent or needs — why they came. Audiences may be baffled why they get certain recommendations. Audiences either ignore pushy targeting, or worse, resent it.
Targeting won’t help people discover things they might be interested in but don’t know about. In many cases people aren’t sure what specifically they want or are interested in at a given point in time. They only recognise what they want after they have seen something they like. This is a known difficulty for presenting content: the discovery problem.
Targeting doesn’t work well for general interest content. Just because lots of people are interested in a broad topic such as gardening, that doesn’t mean that a person who likes gardening will be interested in any content about the topic. General interest content may have wide appeal, but can be hard to recommend.
Narrowly focused, niche content usually addresses an immediate utilitarian need. In contrast, when audiences want to relax, feel informed, or be entertained, they often seek general interest content. Brands recognise the value of general interest content for building and sustaining relationships with audiences. Such content has become an important form of content marketing. The challenge is how best to offer such content to audiences.
Making more relevant recommendations
How can we provide recommendations to audiences about content they will be interested in, without assuming too much about who they are, or what they want? Suppose we could recommend relevant content to someone you don’t know — perhaps a first time visitor to your site. Suppose we could what types of content the person likes without suggesting content that is either too generic, or too specific.
Brands can improve their recommendations of general interest content by trying to understand the current emotional intent of the audience viewing content. To do this, brands need to understand more about the emotional characteristics of their content.
General interest content is content that might appeal to people who are not looking for content about the specific items discussed. Good general interest content appeals to a wide group of people who might little in common — they don’t fit some segment stereotype. And it manages to sound unique and distinctive, unlike most of the other content addressing the same broad topic.
Brands can benefit by asking what makes their content distinctive compared to others publishing on the same topic. What do audiences find most attractive? What is most different?
A content attractor is a quality of your content that resonates with certain audiences. It may be your approach to talking about a topic, or your point of view. It produces an emotional experience. Often it is the combination of two or three qualities that makes content distinctive and special.
When the qualities of the content match the preferences of the audience viewing the content, the audience feels validated emotionally.
What makes general interest content distinctive will vary. Publishers make choices that determine who the content appeals to. If you choose to provide practical advice that makes a difficult-to-understand topic more approachable, then you probably won’t appeal to people who are seeking the latest thought leadership insights on that topic.
Suppose you are a financial institution. Your customers have a wide range of knowledge and preferences about financial information. All are interested in general topics relating to finance, things like changes in taxes or interest rates, but no one article will please everyone. By considering how attractors influence who wants to view an item of content, you can better recommend other articles that have similar attractors. Some people pride themselves in how much they know compared to others, and will enjoy articles that challenge their knowledge. If they will click on an article entitled “Ten things you probably don’t know about upcoming tax changes” they expect to see things they didn’t know but would like to know. When they find such information, they feel rewarded, and want to find similar articles.
Someone lacking in confidence about finances will find the “Ten things you didn’t know” article intimidating. They might respond better to an article entitled “Keeping up with tax changes without stress” that tells the story of a family that has adopted a simplified approach to money management. Provided the article delivers on its promise, the customer may be emboldened to look at another article with a similar slant.
The attractors in the first article are the promise of exclusive information that is not widely known, access to views of leading tax accountants, and the rewards of being in the know. The attractors in the second article are the promise of no stress, and the satisfaction of an encouraging story about a family the reader can relate to. Depending on the popularity of content with these attractors, the brand may develop a range of content based on a combination of these attractors.
Finding which combinations are most effective will require you to know the qualities of your content, and monitor how much these qualities are being viewed. Fortunately, you can describe these attractors with metadata tags, and track their performance.
Content attractors can come in many guises. They may relate to:
- The attitude of the content — for example, is the content’s perspective visionary, championing of a cause, contrarian, or practical?
- The emotional experience of the content — for example, is the content motivating, funny, or surprising?
- How the content reveals the topic — for example, does the content share intimate true stories, provide a behind the scenes look at someplace familiar, or share first person perspectives through an interview?
- The organizing idea — for example, does the content offer lessons learned, situational anecdotes, or stories of critical turning points?
Knowing these attractors can help the publisher recommend content to audiences, and develop content that will most interest them.
Recommending relevant content
There are four basic steps to improving recommendations:
- Identify your general interest content
- Describe the important attractors for that content with tags
- Recommend content to audiences based on the kinds of content they have decided to look at
- Monitor what content is being viewed, and what recommendations seem to be most effective
As mentioned previously, your general interest content is content that audiences might find interesting even if they weren’t searching for it specifically. 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.
I’ve already touched on identifying content attractors. 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 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.
Relevance is never guaranteed — it requires constant follow up. 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.
Using content attractors to recommend content can improve audience engagement. Effective recommendations lead to more viewing of content. Audiences are more likely to identify with brands that offer content that fits their emotional needs, and will be more inclined to develop an ongoing relationship with the brand’s content.
The goal of this approach is to make the content more responsive to the interests of audiences as they are viewing content. It aims to make content more emotionally intelligent, aware of what it offers, and of how it is being perceived.