Tag: relevance

As content becomes increasingly fragmented and modularized, its context gets lost. Many people advocate for understanding the context in which content is used, but they have different ideas about what the context of content represents. Current technological approaches to managing content pieces don’t address the full range of contextual dimensions.

Distinguish the delivery and the discovery contexts

Much discussion of the content context revolves around what the user is seeking. The concern is to get the right content to the user when they seek it. I refer to this as the delivery context.

The delivery context is about matching what is known about the user with the dimensional variables of the content. For example, we ideally want to know who the user is, what they know and have done already, their goal, and so on. With that information, a publisher can select appropriate content according to the topic, level of detail, formats, and perhaps even messaging.

An entire industry of content orchestration has emerged to develop insights and practices to address the user’s delivery context. It’s by no means a simple problem to solve, but it is one that promises great profits to marketers and others who want to push content to customers.

But another contextual dimension of content gets less attention: the discovery context. Users aren’t always waiting for the right content to find them. In many cases, a stream of predecided content is antithetical to what they seek. They need to define their own journey to discover what they should know more about.

Discovery supplies the context of meaning

Content does not always convey its meaning clearly, especially when statements are skimmed or read in isolation. Users may not understand something about the content, or they may read meanings into the content that aren’t explicitly stated. The discovery context concerns what users may want to know beyond the statement seen.

As content is increasingly decontextualized — appearing as snippets rather than as long-form articles — users must discover the missing context themselves. They must supply explanations beyond what is conveyed by the string of text.

The discovery context consists of three dimensions:

1. The context of collective understanding
2. The authorial context
3. The associative context

Collective understanding is the layer of knowledge the author might presume the reader knows, whether or not that is true for a specific individual reader. A statement might refer to people, places, dates, metaphors, or other things that are not described, only referenced. The reader is expected to understand these items and look them up if they don’t.

The authorial context refers to the broader context from which a statement was lifted. Authors can be quoted out of context. Bots pull snippets or will paraphrase the source material. The danger is that such text selections end up vague or misleading.

Even when a text snippet is an honest summary of what the author wished to convey, it may not be clear what point they were trying to make with the statement. Was the statement a claim or assertion, a warrant or evidentiary rationale, or the grounds or justification for their argument? In other words, what was the role of the statement, and what did the author assume the reader already knew when they made it?

The associative context concerns the broader context in which the user evaluates statements. What else is similar to this statement? How does it fit together with other statements?

The associative context becomes important as users rely on content that’s abstracted from its original source. They utilize snippets of content that have been compiled by others or by themselves. The associative context is a defined layer of curation, collecting related items together. Such curation provides meaning to users by allowing them to recontextualize the content fragments.

A simple example of an associative context is the highlights from a book. These highlights can be kept together to recall the key points of a book, but they can also be combined to compare how different books and authors discuss similar or related topics. The snippets by themselves convey limited information, but collectively they tell much more.

Discovery remains undersupported

Although there is an evident trend in content toward providing direct “answers” to users, it is also clear that such approaches can’t be “zero-shot” ones, where users must settle with the predefined answer the bot offers. Digital content is becoming more conversational and dialogical, allowing users to ask follow-up questions and get clarification.

Bots offer opportunities for discovering information, but the situation currently remains fragmented. Generative AI seems split into global solutions that can supply information relating to collective understanding (but not specific sources), and localized solutions that can answer questions about specific sources (but not general knowledge). To a large extent, the split seems driven by vendor advocacy of preferred models and technologies (big vs small models, and KG vs vector RAG) — debates that interest only engineers, not ordinary users.

Users have limited opportunities to build their own knowledge base and define their own associative context with these tools, which largely lack memory of what users have told them in the past.

Rather than expect a single technical solution to solve everything, we would be better served by having the freedom to compose our own suite of tools. Ebook devices provide one inspiration: they allow users to add their own dictionaries, notes, and highlights, and export snippets elsewhere. Google’s NotebookLM paradigm also points to ways to bridge local and global capabilities. Eventually, we may have many AI capabilities built into our browser.

Personal knowledge management may eventually succeed the list-making technologies of personal information management. Before we feel comfortable delegating tasks to AI agents, we will need to be confident we understand what we want and what’s available.

— Michael Andrews

Reader attention and reader relevance are often confused, which can result in bad decisions.

Marketers, SEO consultants, writers, and others like discussing how to produce content that attracts attention.  They promote “secrets” to grab the attention of readers, promising that organizations can control what people notice.  

The relevance of that content to readers is given far less attention than whether their content attracts attention.  Ensuring relevance isn’t about controlling readers but being responsible to them.

Yet, in the minds of many content professionals, attention and relevance are synonymous.  People will pay attention to content that’s relevant to them, and relevant content will attract attention. 

Unfortunately, this relationship, while ideally true, is more often untrue.

Attention can be irrelevant

The fallacy of equating attention and relevance is evident when we consider how much attention is wasted on irrelevant content. 

People look through content that promises to be relevant but isn’t. They overlook content they should notice but don’t see or read. One manifestation of this phenomenon is known as the “streetlight effect.”

We need to dispel the myth that if people “view” or “engage” with content, it is necessarily relevant to them. Breaking these assumptions is challenging because the foundations of content analytics and measurements are predicated on these metrics. It’s easy to measure clicks but harder to measure what people are thinking or wanting.

Toward a taxonomy of relevance

Once we consider all the circumstances in which readers might view irrelevant content, we begin to see how uncommon relevant content is in practice.  Sometimes, content is irrelevant to users unintentionally, but other times that irrelevance is intentional.

Let’s enumerate different variations of content relevance:

1. Matched relevance is when the content matches what the reader needs at the time they are seeking it. It’s great when we have this, but it is rarer than we think.
2. Misleading relevance is when the content suggests it will be about a topic relevant to the reader but is, in fact, about another topic. The content leads with something of interest but switches to the publisher’s alternative agenda, often to sell you something you weren’t looking to buy. Alternatively, the content might imply it is relevant to a certain reader but isn’t. Headlines mentioning “you” are notorious examples.  Readers expect the content to be about “me” but find it isn’t.
3. Spurious relevance is when content isn’t about what it purports to be. Much content represents itself as objective but reflects the bias of its publisher, who is selective in the information they highlight or downplays their role in commissioning content developed by others. Content cloaking occurs with misleading product demos, testimonials, and vagueness in claims. 
4. Overgeneralized relevance occurs when the content is so broad that the reader has difficulty seeing what specifically they need to know and consider. Content that promises to offer “everything you need to know” is a prime example of this genre, but it’s prevalent in content that makes less sweeping claims, such as user manuals.
5. Hidden relevance is when something that would be relevant to the reader is buried in the content to obscure finding it. This situation arises through poor content planning, such as mixing too many topics in a single item or trying to address audiences with divergent interests.  In addition to confusion arising from poor execution, sometimes relevance is intentional.  Organizations like to bury bad news.  They will invite customers to read their updated terms and conditions but make it difficult to see what has changed that could impact the customer. 
6. Mistimed relevance happens when content is provided too early to be relevant because the reader isn’t considering the topic or isn’t ready to make a decision.  Alternatively, the content may be communicated after it is immediately useful. Organizations offer “more of the same” information because a customer has previously made a similar one-off decision.  While mistimed relevance generally leads to attention avoidance, it sometimes sparks confusion concerning the referent, such as an email “concerning your recent purchase” that’s actually a pitch to get you to buy more. 

Noisy attention-mongering triggers wariness

Attention is squandered when relevance isn’t established. Customers become cynical and don’t take statements at face value.

The relevance of content depends on the trust it elicits.

–Michael Andrews