Category: Content Effectiveness

Categories
Content Effectiveness

Establishing the right quantity of content

Brands need to understand what key variables govern how much content they should produce.  As noted in my previous post, content strategists and content marketers tend to offer differing advice, and the rationale for their advice generally will focus on a specific aspect of content.  In this post, I outline how common goals for content can impact desired content quantity.  By having a more complete picture of impacts of different factors, brands will be better able to make informed decisions about how much content to produce.

photo of newsstand in Rome

What problem does growth solve?

Having a strategy for content is essential to implementing any content program, otherwise one is going to waste effort and miss important opportunities.  But even before developing a strategy, brands would benefit from having some sort of guideposts for understanding the utility of having more content.  Brands need a sense of what criteria to consider before they hire someone to help them develop a strategy that is based on goals that reflect assumptions about their problems.  That kind of criteria-based framework is not common currently.

Content strategy and content marketing each offer a perspective missing from the other.  Brands will want to ask what is their biggest issue: Is it having too much content (and too much poor quality content), which is overwhelming their audience, or is it not being heard in an increasingly noisy world?

Neither the strategy or marketing approach currently has a good conceptual framework for guiding the right amount of content over the long term. As a content strategist, I have a strong affinity with the approach of not pushing to add to an already large content stockpile, when this stockpile appears unvalued by audiences, and even by the brand itself.  That said, I believe this approach is valid only for the short term.   Though “less is more” can be an effective strategy in the short run, “more is more” will need to be where brands get to, provided it is done correctly.

Managing symptoms of too much content

One issue in particular draws much attention: that there is too much content, too much information, and too much of that is deemed poor.  Less attention is given to why so much content is out there.  There is an unspoken disdain for the people creating this content, as if they were foolish or willfully spiteful.  A common criticism admonishes those people in organizations who think of content “as a commodity” and who rush to publish “as much as possible.”

Rather than demonize the creators of unloved content, it is perhaps more constructive to understand their motives.  Content is created because there are incentives to create it that are based on a presumed demand for it — though the signals for the demand are often hard to read and are frequently misinterpreted.  It’s not just content that is growing: all kinds of things in our home and work lives are proliferating.  Marketers today need to communicate about hundreds of different toothpaste products, for example, not just a few dozen.  Complexity is a reality, even if one doesn’t like it.   What seems from one perspective to be unmanaged complexity, from another perspective can be seen as richness, diversity and choice.  The goal should not be to impose simplicity on audiences, but to hide complexity from them.

To grasp why quantity can be a hot button issue, consider the audience context.  Quantity can generate complexity for audiences.  Audience attention is adversely impacted by having to deal with irrelevant content. It is not just the amount of attention they are able to offer, it is the amount they are willing to offer.   Audience attention is capped, even if elastic within a narrow range.  Brands must manage quantity properly because audiences have limited attention to give to a brand’s content.

It is comparatively easy to stick to the safety of producing content for areas that one is absolutely sure that solid demand exists.  This is what GOV.UK did when it cut 75% of its content.  It is more difficult to interpret what content that one does not offer that one should offer.   It’s also essential to understand what content deserves fixing.  Rather than cut poorly performing content, it may be more beneficial to transform it.

Two kinds of quality problems: bad content and sad content

One should be careful not to equate poor quality content with useless content.  Content may not meet audience needs fully, but still offer them —  or the brand —  some value.  Such content is sad content, not bad content.  Brands should determine if there is a quantity surplus problem (too much useless content, but the rest is fine) or quality deficit problem (too little content is useful compared with the potential demand for it).  The two are not automatically the same issue, even though they often conspire together to damage the experience for audiences.  Brands face a choice: keep only useful content, or make all content useful.

Bad content should be cut: it offers no value to either the brand or the audience.  Sad content, in contrast, is content that in some way is valued by the brand but not the audience, or less commonly, valued by an audience but offering limited value to the brand.  Sad content can reflect general quality problems, but it also can reflect more specific issues relating to how to connect the right content with the right audience.  It may not be visible to an audience that is interested in it, or it might not be as relevant as it could be because it doesn’t provide sufficient details on the issues that matter most to them.

Brands have many goals for content

Sad content can be the by product of poorly executed attempts to realize different kinds of goals for the brand.  Rather than deride sad content, it is more useful to try to understand what it is trying to accomplish.  Generally these goals fall in four areas:

  • more frequent engagement with audiences
  • giving more specific kinds of information for audiences
  • providing more thorough explanations
  • getting more visibility, and helping audiences discover what you have to say

Each of these goals is worthwhile.  The following table summarizes how different brand goals relate to the quantity of content produced.  A goal may suggest producing less content, producing the same amount (but improving the quality), or creating more content.

Brand needs: Strategy: cull content (less content) Strategy: improve existing content (same amount of content) Strategy: expand content (more content)
More frequent engagement with audience     x
More precise content to improve relevance   x x
Better overall content quality x x  
More visibility in social or SEO x x x

The impact of goals on quantity

Engagement

The only unequivocal reason to create more content is to have fresh material to offer audiences.  This is a useful only to a limited degree, since only a portion of your audience will be willing to give more of their attention to the brand, and even for these people, there is a limit on how much attention they will be willing to offer.

Precision

To offer greater relevance to audiences often involves making content more precise.  Generally, this will involve adding more specialized content, or content with greater detail.  The simplest example of this are any of the big content providers: Amazon, Wikipedia, or Netflix.  They provide relevance to audiences by offering a big range of content addressing very specific interests.  Alternatively, a brand may improve existing content to make it less general, and give it more thematic variation.

Overall quality

To improve overall quality of content requires resources.  Generally, brands need to devote more resources to their existing content to make it better in terms of being topical for audiences, up-to-date, well written, having engaging media (quality graphics, photos, video) and so forth.  Although some incremental quality improvements are be possible with constant resources, most perceptible improvements will requirement more resources.   If additional funds aren’t available, brands face a strategic decision of whether to reduce the amount of content they offer in order to produce better quality content.   The “less is more” strategy can improve satisfaction with the more limited set of content that is published, but carries a potential cost of alienating people seeking content that is no longer available.  GOV.UK reduced duplicate content, and content that received little traffic, as part of their content culling.  But by drastically limiting content it may have reduced the level of self service and information transparency available to the UK public.

Clearly, any expansion of content will place a strain on content quality.  The case of Wikipedia provides an example.  Wikipedia is appreciated for the breadth of its content.  Many brands, from Apple to the BBC, use Wikipedia content in their products because Wikipedia is unrivaled in its breadth.  However, the quality of Wikipedia articles is quite variable, and despite having a sophisticated quality control process, poor quality content is not uncommon.  Even though Wikipedia is not a commercial product, the same issues confront commercial brands.  At times, it may be the case that publishing something is better than not publishing anything, when brands know their customers expect certain content from them, and the brand feels it has a responsibility to offer this content.

Visibility

The most difficult factor to gauge is how the quantity of content affects its visibility on search engines and in social media.  This is obviously important, since it impacts the discoverability of content, which in turn affects a brand’s ability to attract the audiences it desires.  On an atomic level, brands can promote individual content to raise its visibility through social media campaigns or search engine marketing, for example.  But the collective visibility of all the brand’s content is influenced by how audiences relate to the profile of the entire body of content.

Some agencies, such as Red Rocket (cited in my previous post) argue that more content automatically leads to increases in impressions, click throughs, rankings, and social media metrics.  Matt Cutts of Google notes that big websites don’t automatically get higher search rankings, so adding pages by themselves won’t improve search rank.  But he notes because additional pages typically drive more search queries and more page linking, these will help attract more visitors.

It can be argued that smaller organizations that need to attract interest around a specific core message will do better to limit the amount of content, so that audience traffic is directed to a limited set of content, rather than scattered across many different items of content.

Visibility involves a mixture of overall authority (utilization) of all content, and the authority (use) of specific items being promoted.  If a lot of content is weak, and fails to drive traffic, sharing or discussion, then the collective value of this content is weak.  If a few items are able to drive traffic, sharing and discussion, these items can be influential.  However, it can be risky to have expectations for gaining influence in a handful of content, hoping that it will become popular.  Relying on a narrow set of content to drive popularity could either involve narrowing the range of audiences it will appeal to only the most mainstream, or else trying to be too broad to find popularity, and failing to gain strong appeal from any group.

So from the perspective of content visibility, more-is-more, assuming the content is unique enough to get search and link traction.  Big sites having pages with high traffic also get a halo effect, where more niche pages are likely to get a lift through their domain authority.  When a brand is perceived as a source for content, it becomes a destination.

Can more content be better for audiences?

In several dimensions, audiences benefit from access to more content.  How does this square with the systemic problem that audiences face regarding information overload?  If attention is limited, does more content improve relevance, or lessen it?  This is the key question for brands.

In the future, brands should be able to produce more content that is more relevant for audiences: higher quality, more specific and precise, and delivered when audiences want to see it.  Audiences won’t feel overwhelmed, but more engaged, because the content more closely matches their specific needs and interests.  Developing this capability will require:

  • improvements in content production capabilities,
  • improvement in content categorization and discovery
  • improvements in content delivery intelligence

Note that such improvements are not free and will cost money.  Also, process and technology improvements won’t eliminate the problem of sad content, even if they reduce the issue.

Perhaps most importantly, automation and process alone won’t create great content.  The content with the most outsized impact will likely be content that received the most human attention, just as it is today.  Examples from award winning advertising and journalism show us that thoughtful and creative content gets the most engagement, and resonates more strongly with audiences.  Even when using technology to carefully optimize pages through repeated A/B testing, human attention and judgment are crucial to improving engagement.

The field of content is far from having an easy-to-understand and reliable framework to guide the right amount of content to produce.  But I hope that this overview provides practical guideposts to frame that discussion.

— Michael Andrews

Categories
Content Effectiveness Content Marketing

How much content is enough?

How much content is enough?  Neither content strategy nor content marketing currently offers a complete framework to guide how much content a brand needs to produce.  Each has a distinct orientation that is largely reactive to the problems it is most concerned with.  These orientations conflict with each other.  Brands need better guidance.

Why the quantity of content matters

Everyone involved with content agrees about some basics:

  • content is important to the success of brands
  • doing it well can be expensive
  • it is important that brands make an appropriate investment

Brands will reasonably want to know what is the right amount of content to create, and how much is enough.  They don’t want to waste money creating unnecessary content, and don’t want to miss the benefits that can be gained from quality content because they produce too little.

This seemingly simple question — how much content is enough — turns out to be surprisingly difficult to get a consistent, straightforward answer to.  It’s a basic question with profound implications, affecting both the size of investment in content, as well as how that investment is structured.  It’s also the question, more than any other, on which practitioners of content strategy and content marketing are likely to disagree.

If we look at general tendencies, we see that content strategists more often than not advise brands to offer less content: to be more selective in the content they present and the messages they communicate.  Content marketers tend to advise brands to offer more content: to be more active in how much content they communicate with audiences.   I am sure each approach will acknowledge exceptions can apply — but the contrast between the emphasis of the approaches is stark.   The question of how much content to offer reveals a deep philosophical difference between the approaches, and the different ways each perspective values content.

Let’s review a few anecdotal statements for clues into how each approach thinks about the issue.  I will then ask “lateral” questions to consider strategic issues related to the statement.  These “food for thought” questions aren’t intended to challenge the value or accuracy of a fact or opinion expressed, or meant to imply anyone said something they did not say.  Rather, they are meant to spark a wider exploration of ideas associated with the topic of the statement.  I present them because I feel that much of the discussion to date has not been giving sufficient attention to these issues.

Content strategy perspectives on content quantity

Content strategists emphasize the tradeoff between the quality of content, and its quantity.  Improving the quality of content for audiences is a major purpose of content strategy.  Offering less content can result in better quality, for many reasons: the content gets more attention when created, it is keep up to date, it is easier for audiences to find, and it provides a clearer messaging to audiences.  Among content strategists there is even concern that content marketing is worsening the perceived problem of too much poor quality content.

Margot Bloomstein, an influential content strategist and author, speaks about having a“quantity verses quality discussion” with clients, where she asks them:  “Can we do it better, not just more?”    “Should we be just writing more content, or should we be looking at that content and saying, is it laser focused” to meet communication goals?  She asks clients “are they meeting objectives, or are we just doing more content marketing, and hope?”  Food for thought: Can you shrink your way to greatness? 

Jonathon Colman, a respected content strategist, currently at Facebook:  “If you want to see who the leading organizations of tomorrow are going to be, take a look at who’s doubling down on content — not quantity of content, but quality of content experiences and services.”  He summarizes: “Quality > Quantity!”  Food for thought: Can having high quality content alone substitute for not addressing a topic your audience is interested in, and expects you to provide to them?

The UK Government Digital Service (GOV.UK), implemented one of the highest profile examples of content culling: it cut out 75% of its web content, and improved the experience for audiences in the process.  “We don’t care about traffic, we don’t care about numbers. We just need people to get the information,” Sarah Richards, content officer, told  a Confab session last year.  Food for thought: Does this approach work for organizations that don’t have a captive audience?

Content marketing perspectives on content quantity

Content marketers believe that content is a cornerstone of marketing, and consequently more content needs to be created to support marketing.   There has been a rise of content agencies, and in-house content staffs have expanded quickly, an indication that more marketing content is being created than ever.

Like content strategists, content marketers acknowledge that content quality is important, and should not be sacrificed for the sake of producing more content.  But while they recognize the potential tradeoff between quantity and quality, they don’t see it as an iron law that can’t be overcome.  In other words, more content doesn’t necessarily have to be poorer quality, and therefore more content can be desirable.

Content marketers worry about not being seen by audiences.  So while quality is mentioned, the clear emphasis of the content marketing community is on producing more content.

Joe Pulizzi  of the Content Marketing Institute (CMI): “Australian, North American and UK marketers all produced more content over the last year compared with one year prior.”   Food for thought:  Are all these brands wrong to be producing more content? Alternatively, are they all seeing benefits from producing more?  Are some wrong, but others right, when following the same basic advice?

A CMI survey of B2B marketers: “Producing enough content is the biggest challenge.”  Food for thought:  What’s the true constraint?  Is this a real problem, or a manufactured one?

Red Rocket Media, a content marketing agency, writing in econsultancy: “Creating more content gets results,” and they present data they say proves it.  Food for thought:  Will offering more content get the brand more love, or just better vanity metrics?

Is quality or quantity more important?

There is a philosophical difference between content strategy and content marketing.   I’ll use a grossly simplified analogy to illustrate this difference (accept my apologies in advance).  Content strategy is about magnetism; content marketing is about outgoingness.   Content strategy believes people will find you if you are likable, while content marketing believes if you find people they will like you.  Quality enables magnetism for a brand; quantity (or at least the availability of fresh content) enables its outgoingness.

The focus on quality verses quantity by itself does little to inform us about how much content is enough.  It’s important to understand what someone has in mind when they talk about quality.  Everyone agrees quality matters, but don’t necessarily agree what quality is.  Most people agree what quantity is, though they don’t always agree why or how it matters.

Quality is rarely robustly defined, so it remains largely a subjective judgement. People have many different ideas of what constitutes quality: ranging from like-ability to high production values to informational accuracy to content utilization.  Personal ideas about quality shape many of the assumptions used to develop goals for content.

Content strategists will argue the right amount of content is the amount that supports the goals of a defined content strategy, which will be unique to each organization.  Content marketers will argue as well that a plan with goals are needed, and the right amount of content is the amount that satisfies these goals.  Both perspectives might scoff at the idea that one would ask how much content is enough, in the absence of a strategy or plan.  They might consider the question naïve.  But brands would be reasonable to push back and ask: What is so mystical about the utility of content that prevents us from getting a sense of what is the right scale for content?  Can getting a sense of appropriate scale for content only be determined after some lengthy strategy review process?  Are there no guidelines at all?

Given the conflicting advice about how much content to produce, brands need better guidance.  This guidance needs to look at the complete picture, and not just react to worries about falling behind other brands in some particular area.  And that guidance needs to be specific about what is helped and hindered by a particular approach to increasing or decreasing content.  Most general advice either unrealistically promises “you can and should do it all” or vaguely advises that “it’s necessary to strike the right balance.”

Quality verses quantity is just one strategic trade-off that needs to be addressed in a content strategy.  It’s an important tradeoff, and one that deserves a deeper examination, especially given the wide variation in how people refer to quality.  There are other factors such as engagement, visibility and relevance to consider when determining how much content is enough. Each of these factors has its own tradeoffs.

So to answer whether a brand should be creating more content, or less, it pays to understand how different content goals will influence different directions for content quantity.  In a follow-on post, I will discuss in more detail how looking at four dimensions of content can help brands understand better the appropriate level of content to produce.

—Michael Andrews

Categories
Content Effectiveness

Don’t build your personalization on data exhaust

A lot of content that looks like it’s just for you, isn’t just for you.  You are instead seeing content for a category segment in which you have been placed.  Such targeting is a useful and effective approach for marketers, but it shouldn’t be confused with personalization.   The choice of what people see rests entirely with the content provider.

When providers both rely on exclusively their own judgments, and base those judgments on how they read the behaviors of groups of people, they are prone to error.  Despite sophisticated statistical techniques and truly formidable computational powers, content algorithms can appear to individuals as clueless and unconcerned.  To understand why the status quo is not good enough, we first need to understand the limitations of current approaches based on web usage mining.

Targeting predefined outcomes

Increasingly, different people see different views of content.   Backend systems use rules to make decisions concerning what to present to offer such variation.  The goal is a simple one: to increase the likelihood that content presented will be clicked on.  It is assumed that if the content is clicked on, everyone is happy.  But depending on the nature of the content, the provider may be more happy — get more benefit —  than the viewer by the act of clicking, and as a consequence present content with only a minor chance of being clicked.

A business user who is viewing a vendor sales website may see specific content, based on the vendor’s ability to recognize the user’s IP address.  The vendor could decide to present content about how the business user’s competitor is using the vendor’s product.  The targeted user is in a segment: a sales prospect in a certain industry.  Such a content presentation reflects the targeting of a type of customer based on their characteristics.  It may or may not be relevant to the viewer coming to the site (the viewer may be looking for something else, and does not care about what’s being presented).  The content presentation does not reflect any declared preference by the site visitor.  Indeed, officially, the site visitor is anonymous, and it is only through the IP address combined with database information from a product such as Demandbase that the inference of who is visiting is made.  This is a fairly common situation: guessing who is looking for content, and then guessing what they want, or at least, what they might be willing to notice.

Targeted ads are often described as personalized, but a targeted ad is simply a content variation that is presented when the viewer matches certain characteristics.  Even when the ad you see tested better with others in a segment of people who are like you, the ad you see is merely optimized (the option that scored highest) not personalized, reflecting your preferences.   In many respects it is silly to talk about advertising as personalized, since it is rare for individuals to state advertising preferences.

The behavioral mechanisms behind content targeting resemble in many respects other content ranking and filtering techniques used for prioritizing search results and making recommendations.  These techniques, whether they involve user-user collaborative filtering, or page-ranking, aim to prioritize the content based on other people’s use of the content. They employ web usage mining to guess what will get most clicked.

What analytics measure

It is important to bear in mind that analytics measure actions that matter to brands, and not actions that matter to individuals.  The analytics discipline tends to provide the most generous interpretation of a behavior to match the story the brand wants to hear, rather than the story the audience member experiences.  Take the widely embraced premise that every click is an expression of interest.  Many people may click on a link, but quickly abandon the page they are taken to.  The brand will think: they are really interested in what we have, but the copy was bad so they left, so we need to improve the copy.  The audience may think: that was a misleading link title and the brand wasted my time; it needs to be more honest.  The link was clicked, but we can’t be sure of the intent of the clicking, so we don’t know what the interest was.

Even brands that practice self awareness are susceptible to misreading analytics.  The signals analyzed are by-products of activity, but the individual’s mind is a black box.  More extensive tracking and data won’t reliably deliver to individuals what they seek when individual preferences are ignored.

Why behavioral modeling can be tenuous

There are several important limitations of behavioral data.  The behavioral data can be thin, misleading, flattened, or noisy.

Thin data

One of the major weaknesses of behavioral data is when there isn’t sufficient data on which to base content prioritization or recommendations.  Digital platforms are supposed to enable access to the “long tail” of content, the millions of items that physical media couldn’t cope with.  But discovery of that content is a problem unsolved by behavioral data, since most of it has little or no history of activity by people similar to any one individual.  If only 20 per cent of content accounts for 80 per cent of activity, then 80 per cent of content has little activity on which to base recommendations.  It may nonetheless be of interest to individuals. Significantly, the content that is most likely to matter to an individual may be what is most unique to them, since special interests strongly define the identity of the individual.  But what matters most to an individual can be precisely what matters least to the crowd overall.  Content providers try to compensate for thin data by aggregating categories and segments at even higher levels, but the results are often widely off the mark.

Misleading signals

Even when there is sufficient data, it can be misleading.  The analytics discipline confuses matters by equating traffic volume with “popularity.”  Content that is most consumed is not necessarily most popular, if we take popularity to mean liked rather than used.  A simple scroll through YouTube confirms this.  Some widely viewed videos draw strong negative comments due to their controversy.  Other may get a respectable number of views but little reaction from likes or dislikes.  And sometimes a highly personal video, say a clip of someone’s wedding, will appeal to only a small segment but will get an enthusiastic response from its viewers.

Analytics professionals may automatically assume that content that is not consumed is not liked, but that isn’t necessarily true.  Behavioral data can tell us nothing about whether someone will like content when a backend system has no knowledge of it having been consumed previously.  We don’t know their interests, only their behavior.

Past behavior does not always indicate current intent.  Log into Google and search intensively about a topic, and you may find Google wants to keep offering content results you no longer want, because it prioritizes items similar to ones you have viewed previously.  The person’s interests and goals have evolved faster than the algorithm’s ability to adapt to those changes.

Perversely, sometimes people consume content they are not satisfied with because they’ve been unable to find anything better.  The data signal assumes they are happy with it, but they may in fact be wanting something more specific.  This problem will be more acute as content consumption becomes increasingly driven by automatic feeds.

Flattened data

People get “averaged” when they are lumped into segment categories.  Their profile is flattened in the process — the data is mixed with other people’s data to the point that it doesn’t reflect the individual’s interests.  Not only can their individual interests be lost, but spurious interests can be presumed of them.

Whether segmentation is demographic or behavioral, individuals are grouped into segments that share characteristics.  Sometimes people with shared characteristics will be more likely to share common interests and content preferences.   But there is plenty of room to make mistaken assumptions.  That luxury car owners over-index on interest in golf does not translate into a solid recommendation for an individual.  Some advertisers have explored the relationship between music tastes and other preferences.  For example, country music lovers have a stronger than average tendency to be Republican voters in the United States.  But it can be very dangerous for a brand to present potentially loaded assumptions to individuals when there’s a reasonable chance it’s wrong.

Even people who exhibit the same content behaviors may have different priorities.  Many people check the weather, but not all care about the same level of detail.  As screens proliferate, the intensity of engagement diminishes, as attention gets scattered across different devices.  Observable behavior becomes a weaker signal of actual attention and interest.  Tracking what one does, does not tells us whether to give an individual more or less content, so the system assumes the quantity is right.

Noisy social data

Social media connections are a popular way to score users, and social media platforms argue that people who are connected are similar, like similar things, and influence each other.  Unfortunately, these assumptions are more true for in-person relationships than for online ones.  People have too many connections to other people in social channels for there to be a high degree of correlation of interests, or influence between them.  There is of course some, but it isn’t as strong as the models would hope.  These models mistake tendencies observable at an aggregated level, with predictability at the level of an individual.

Social grouping can be a basis for inferring the interests of a specific individual, provided people you know share your interests to a high degree, so you will want to view things they have viewed or recommend viewing.  That is most true for common, undifferentiated interests.  Some social groups, notably among teens, can have a strong tendency toward herd behavior.  But the strength and relevance of social ties cannot be assumed without knowing the context of the relationship.  One’s poker buddies won’t necessarily share one’s interests in religion or music.  Unless both the basis of the group and the topic of content are the same, it can be hard to assume an overlap.  And even when interests are similar, they intensity of interest can vary.

Social targeting of content considers the following:

  • how much you interact with a social connection
  • how widely viewed an item is, especially for people deemed similar to you
  • what actions your social connections take with respect to different kinds of content
  • what actions you take relating to a source of content

While it is obvious that these kinds of information can be pertinent, they are often only weakly suggestive of what an individual wants to view.  It is easy for unrelated inputs to be summed together to prioritize content that has no intrinsic basis for being relevant: your social connection “liked” this photo of a cat, and you viewed several photos last week and talk often to your friend, so you are seeing this cat photo.

At the level of personalization, it’s flawed to assume that one’s friends interests are the same as one’s own.  There can be a correlation, but in many cases it will be a very weak one. Social behavioral researchers are now exploring a concept of social affinity instead of social distance to strengthen the correlation.  But the weakness of predicting what you want according to who your acquaintances are will remain.

Mind-reading is difficult

The most recent hope for reading into the minds of individuals involves contextualization.  The assumption behind contextualization is that if everything is known about an individual, then their preferences for content can be predicted.  Not surprisingly, this paradigm is presented in a way that highlights the convenience of having information you need readily available.  It is, of course, perfectly possible to take contextual information and use this against the interests of an individual.  Office workers are known to ask for urgent decisions from their bosses knowing their boss is on her way to a meeting and can’t wait to provide a more considered analysis.  Any opportunistic use of contextual information about an individual by someone else is clearly an example of the individual losing control.

Contextual information can be wrong or unhelpful.  The first widespread example of contextual content was the now infamous Microsoft Clippy, which asked “it looks like you are about to write a letter…”   Clippy was harmless, but hated, because people felt a lack of control over his appearance.

Even with the best of intentions, brands have ample room to misjudge the intentions of an individual.

Can content preferences be predicted?

The problem with relying on behavior to predict individual content preferences comes down to time frame.  Because targeting treats individuals as members of a category of people, it ignores the specific circumstances that time introduces.  People may be interested in content on a topic, but not necessarily at the time the provider presents it.  The provider responds by trying again, or trying some other topic, but in either case may have missed an opportunity to understand the individual’s real interest in the content presented.  People may pass on viewing content they have a general interest in.  They think “not now” (it’s not the best time) or “not yet” (I have more urgent priorities).  Often times readiness comes down to the mood of the individual, which even contextualization can’t factor in.  Over time a person may desire content about something, but they don’t care to click when the provider is offering it too them.

If the viewer doesn’t have a choice over what they see, it’s not personalized.

A better way

There are better approaches to personalization.  The big data approach of aggregating lots of behavioral data has been widely celebrated as mining gold from “data exhaust.”  Data exhaust can have some value, but is a poor basis for a brand’s relationship with its customers.  People need to feel some control, and not as if they are being tracked for their exhaust.  Brands need an alternative approach to personalization not only to build better relationships, but to increase their understanding of their audiences so they can serve them more profitably.  In the following post, I will discuss how to put the person back into personalization.

— Michael Andrews