Tag Archives: social media

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

How to create impact with shared content

The benefits of having your content shared are obvious.  You get “earned media.”  You increase your reach, and gain credibility by having trusted people distribute your message.  “Your customers become the channel,” to use the words of Forrester. But content that is shared is not necessarily content that is heard – at least by the people who you want to hear it.  If you focus too hard on increasing the number of times your content is shared, you loose focus on whether your content is having a real impact. Many publishers unfortunately focus on measuring sharing activity rather than sharing value.

The vanity of engineering followership

Publishers want to know how to get more people to use their content to further their business goals.  It’s an understandable target, but can produce a distorted approach.  The publisher starts to worry about behavior of audiences they don’t control, instead of worrying about what they do control: the qualities of their content.  The preoccupation with how audiences are conforming to the publisher’s plans can end up being counterproductive.

The publisher centric perspective will begin looking for “influencers” who can spread their message through their content. Publishers hope that if the content can get to the right influencers, those influencers can spread the content and it will be viewed by many others, and consequently have a huge impact. Grand expectations about the power of sharing are colored by concepts such as social contagion, the mechanism behind viral marketing.  Fans of viral marketing also point to research from behavioral economists and cognitive scientists suggesting that people respond unconsciously to various priming, and as a result can be “nudged” into certain actions.

As a practical matter, the mechanics of influence are messy. One forthcoming book on propagation in social networks concludes: “the existence of influence and its effectiveness for applications such as viral marketing depend on the datasets.” Duncan Watts of Microsoft Research notes in his book Everything is Obvious that influence is difficult to orchestrate, especially when confronted with a multitude of factors, each can intervene to shape people’s choices in differing ways. This is not to say people can’t be nudged; rather, the dynamics of influence are involved, and need to be approached with care.  Don’t expect nudging will necessarily provide magical results.

Not only is nudging complex, it can be a distraction. People are often most readily influenceable about things that matter little to them personally. Trying to sway the behavior of your audience fosters worries about follower numbers and fan loyalty – rather than whether audiences are getting value from what you provide them.

Never confuse your agenda with the audience’s

While the publisher may be worried about how to get audiences to do things with their content, the audience could care less about the goals of the brand. The audience thinks: how can I use this content for my personal needs? People have their own purposes for engaging with content that may differ from the publisher’s. Publisher goals can even infringe on audience needs when the brand has become too pushy in its messaging.

Brands often have vague goals for their content, and a vague sense of whom they are reaching and what impact that achieves. They often measure the wrong things as a result. The most obvious mistake is measuring loyalty and sharing rates, without respect to audience segment. A brand that has a core group of loyal fans that regularly shares their content sounds impressive. But who those fans are, and what business value results from the content shared is what matters. Brands need to dig deeper into what’s happening with their content to see what impact they are realizing from content that is shared.

One of the largest groups to share content routinely are middle age women on Facebook.  One could optimize content so that it gets shared on Facebook by this group, and increase the volume of shares. But one should first ask if this segment, defined in such broad terms, is the right segment you want to reach.  Does your audience you want to reach spend much time on Facebook?  Do they share on Facebook the kind of content you create?  What groups would you like to reach that aren’t active on Facebook?

The figments of trivial content

Light entertainment is some of the most shared content, things like cat videos and brand-themed games.  The mundane cat video shows several facets of the sharing process.  Some people will refuse to view a cat video.  Of those who do, some will view it, but wouldn’t think about sharing it, concerned they might look silly.  Others are happy to share the video with their friends, some of whom are annoyed and ignore it, others of whom are known to like the genre.  It is possible that the loyal fans watching the video can’t remember which brand was behind the video, while the annoyed recipients of shared video are very aware who’s behind it.  Attention can be surprisingly acute when annoyed.  A brand that simply measures the volume of shares, and their click through rates is in trouble because it doesn’t understand who is using its content or why they are doing so.

Social chit chat is the most common driver for sharing content.  Social media discussion is rarely momentous, and most content shared through social media is not momentous either. This characterization is not a judgement of people’s qualities, but a reflection of how humans communicate.  Linguists have long recognized that conversation performs a social function that is more important than its information function.  Social media is akin to spoken conversation.  Much content that is shared is a pretext for social interaction unrelated to the content itself.

Content acts a social lubricant.  It gives us something to talk about, when we feel like talking.  We comment on what we like or dislike, or agree with or disagree with.  These reactions are often superficial and predictable.  They involve little investment of effort, and result in little influence.  Much of the activity relating to sharing content creates no lasting impact at all.   Social chit chat doesn’t help brands much, because it neither changes people’s perception of the brand, nor spurs them to take action.

A lot of content that is shared is about vicarious experiences, imagining what you might do if you were the famous person in the news, or dreaming about places you might buy or visit.  Many people are happy to offer quick opinions to each other on topics with which they have no direct experience, relying on impressions and beliefs they acquired somewhere in the past. People never rethink their knowledge and perceptions when they engage with content superficially. They merely re-live old attitudes.

Trivial content doesn’t create an impact for a brand. Content needs to be meaningful for audiences for them to gain value from it.

Attracting brand awareness through sharing

Businesses should encourage the sharing of content to build favorable brand awareness with audiences they want to reach. Brands are successful when they know that people care about the content itself, and are not simply using the content as a way to size up their friend’s personality.  When the essence of the content has value for the audience, it reflects back positively on the brand.

Your content might be shared by people who are existing customers, have a strong interest in becoming a customer, or only have a casual interest in the brand.  Those who find your content may share the content with others who are not customers, who may or may not be looking to buy the services you offer, or even be familiar with your brand.  Whether or not either of the parties are actively in the market, if they match the general characteristics of the audience segments the brand is trying to reach, they can be valuable to engage with, since they may eventually want something from the brand, or be in a position to influence a peer who will want something.  We cannot predict what the buying status will be, so it is important to keep the focus of the content on being helpful for the viewer and to limit any hard sell.

Some marketers might prefer to have the content encourage people to take a specific action, instead of offering a brand focus.  Including a call-to-action can be appropriate for content when customers are in a buying mode, but is less appropriate for audiences who are getting information through the unsolicited referrals from peers.  In general, people will balk at spontaneously sharing content that seems sales-oriented unless the friend has already expressed interest in the product.  It feels pushy for one friend to recommend another buy something out of the blue.

Most content that gets shared will not have a call to action, or at most, a very weak one, such as signing up for more information.  Content that appears to be direct marketing will be shared little. People are less inclined to recommend things that have strings attached, that mixes friendship with commerce and feels like multilevel marketing. People don’t like feeling they are being nudged to do something, or feeling they are being taken advantage of. The sharing party has a reputation to maintain, of caring about the friend’s needs, not just their own pet interests.  While there is some variation in these social norms, people have a strong need to feel they are in control, rather than answering some else’s agenda.

Audiences need to care about your content

Even interesting, useful content that gets shared may not have an impact if the receiving party doesn’t look at it closely.  For the content to have impact, the receiving party needs to care about the content.

People face many choices when encountering content.  These choices indicate how much they care about the content.  Sharing content involves two basic steps, each with a series of associated decisions:

  1. The choice by the sharing party to share the content
  2. The choice by the receiving party to view the shared content

Someone encountering content will decide whether to ignore it, glance at it, or read it thoroughly.  From the brand’s perspective, it is vital that the content is read throughly, since it indicates stronger engagement that can shift perceptions.  After reading the content, the reader decides if she is done with the content or not.  She can save the content to use again later, or she may choose to share the content with friends or family.  It doesn’t matter if the content is shared through social media like Facebook, or simply email, as long as the intended recipients can access it easily.  The sharing party may also choose to add a note explaining their interest in the content.

The receiving party can ignore the content, glance at it, or read it thoroughly.  He may choose to respond to the sender with comments, developing a discussion around the content.  He may even refer the content to someone else.

Content creates an impact the deeper people engage with it, when they absorb the content and not just skim it.  As Nielsen notes, most pages are viewed for only 10 or 20 seconds. People get little value from most content they encounter. Audiences can be tough to please, but brands have a huge opportunity to distinguish themselves from the prevalence of ignorable content.

Content creates an impact when the receiving party has their perceptions in some way touched by the content they receive from friends.  If they discuss it, they likely thinking about how to apply it to their lives.

Content sharing also carries more impact when it seems like a personal recommendation, rather than an FYI.  The more the sharing seems like a recommendation, the more trustworthy it appears, and the more value it carries.  Some signals that the content shared is a recommendation is that it is unusual in some way (the sharing party doesn’t routinely share this type of content) or the sharing party offers personal comments, or engages in post-sharing discussion.

Design content that’s inter-personal

Some content invites discussion. To get a discussion started, create content that is intrinsically discussable: content that’s inter-personal. Consider creating content that provides for different perspectives, or include a discussion guide with questions.

Think about what people want to discuss with their peers.  People tend to have ties with others who are like them (a phenomenon known as homophily) and therefore are likely to share content with people who have similar interests, and similar ways of looking at things.  These people may not feel they belong to a formal “community” centered on a topical interest, but they will generally have common friends within their social circle who provide a ready forum for discussion.

People most often share news stories that are emotional, especially when stories are happy (creating attraction) and interesting (creating surprise), according to research by Sonya Song.  Brands can leverage these insights by developing aspirational content that defies expectations – content with hope.  Sharing is more common for stories that present problems and their resolution, compared with strictly factual stories.

The most important goal is to inspire trust in your content.  The more trustful that people perceive your content, the more they will share it with friends.  With cynicism pervasive, trust is precious.  When people discover something they didn’t know previously and are willing to share that with people who are like themselves, they show they trust the information.

How to improve content impact

There is no simple formula to create meaningful content that is valued and recommended.  Be wary of trying reliable gimmicks to drive up click through rates.  There are many techniques to create teasing headlines promising exclusive information (“6 free benefits you can’t afford to miss.”)  Such headlines tease without informing.  These tactics can get content noticed, by sensationalizing and over promising, but they undermine the content experience, and damage the brand as well.  As audiences get saturated with such subtly manipulative headlines, credibility is even more essential.

The best way to improve the impact of your content is to look closely at the patterns of usage in your content.  The best indicator that content is valued is when readers have spent an above average time on pages for a certain type of content, a longer than average dwell time.

Examine available analytic data to determine what kinds of content are being shared routinely, and what kinds of content are not being shared often.  This data can come from your web analytics, social media analytics, and data from referring sources and link sharing services.

For types of content not being shared, look for content with large numbers of page views.  Determine that these pages are being actually read rather than merely “viewed”: that they have a dwell time appropriate for the size and complexity of the content.  For content that is being used but not being shared, try to determine everything you can about the audiences using the content, and their social media usage, to see if there are opportunities to position the content in a way to encourage easier sharing.  Also do a content audit to see if the content is lacks the qualities of meaning that people expect when they share and talk about content.  Perhaps the content is too dry and factual, is unremarkable, and does not generate discussion.

For content that you believe is valuable but is not being shared, try to uncover why people don’t share it.  Confirm that people feel the content is as valuable as you do, and probe into why they share content in general, and how they feel about sharing your content.

For the kinds of content you offer that does routinely get shared, explore any difference in audience segments as to how frequently they share content, or the specific types of content they choose to share.  Compare these patterns with your goals for the audiences you seek to reach.  Notice if you are missing any key audiences, or if a lower priority audience segment accounts for a large portion of the sharing.  Examine any significant variations in the types of content different audience segments choose to share.  If a certain type of content is not popular with a segment that shares other types of content, make sure that difference is warranted.  Social network analysis can sometimes reveal other interests of audience segments.   Sometimes you can address those interests in your content where you find they relate to your brand.

The more you understand why segments choose to share content, the more you will be able to optimize your content.  By comparing what is happening with your goals for each audience segment, you can work on improving the performance of your content.

Audience satisfaction can be inferred through analytics, but it is useful to get other kinds of feedback.  Try small experiments to test hypotheses.  Talk directly with customers about your content, their needs, and how they relate to content.  Also, try to test how well your content creates unaided recall.  Try to work to improve the memorability of your content, just as TV advertisers do.

By knowing more about why content performs as it does, you can act more strategically, focusing on high value priorities.  Over time, you will improve and get a better sense of how likely it will be that a certain kind of content will be shared by a certain audience segment.

Content sharing plays a crucial role in content strategy.  It helps brands build relationships with customers and prospective customers.  At the heart of content sharing is how content is valued, and discussed.  Improving content sharing requires a sustained effort.   Customers will notice that they find your content is valuable and want to share it, and the perception of your brand will benefit as that happens.

— Michael Andrews