The urgency of genuine personalization

Personalization may be the most misused phrase relating to content.  It’s not hard to understand why people want to talk about personalization: it’s appealing to think you’ll see exactly what you want, especially has we get deluged with content.  But paradoxically most techniques of personalization actually involve tailoring content based on what other people do, rather than your own interests.  As a result, people miss out on content they might most want to view.

To get a sense of why personalization is so urgent, and so troublesome, consider the situation of Facebook.

Mark Zuckerberg said last year that Facebook wants to be “the best personalized newspaper in the world.”  Notwithstanding Facebook’s popularity, its users complain about the lack of relevancy for much of the content they see.  Facebook is optimized for promotion of content sharing, not for filtering of content based on individual preferences.  Those two goals to a large extent are in conflict with each other.  Facebook has chosen to fund its revenues through advertising and related services, which  accounts for about 90% of total revenues.  Brands want their content viewed and shared and their ads seen, so keeping them happy is a huge priority.   These various pressures come to a head with Facebook’s News Feed.  On average, a person may have 1500 potential messages Facebook considers relevant to them, and Facebook needs to prioritize these into a manageable quantity (they’ve decided that’s about 300).  “The News Feed algorithm responds to signals from you” Facebook explains.  But many signals seem to have little to do with the individual, and more to do with other parties: the interests of friends, strangers, and advertisers.   Factors influencing ranking include “number of comments, who posted the story, and what type of post it is (ex: photo, video, status update, etc.)” and “promoted posts.”  Facebook routinely revises its algorithm based on what tests show people in general like best.  But the options for the individual to choose what he or she wants specifically are few.  Facebook decides what types of content, and items of content, are most relevant, and individuals don’t get much choice in the matter.

What do we mean by personalization?

In the digital content arena, personalization lacks any widely agreed definition. As Aria Haghighi of Prismatic notes: “personalization is really young. I still think we don’t all agree necessarily on what personalization means.”   Part of the lack of agreement involves how to implement personalization on a technical level, but also it reflects a lack of common vision from providers about what they want personalization to offer.

Digital marketers generally refer to personalization as behavioral targeting, and often use the terms interchangeably.  Big data researchers typically see personalization as adapting content results on the basis of machine learning.  Curiously, the protagonist of the story, the person seeking content, is missing from the personalization discussion.  Instead, the discussion is centered on how to improve click through rates.

Serving people better should be the core reason for personalization, and it’s important to build a commonsense definition, rather than a mathematical one.  Merrium Webster defines personalize as “to mark (something) in a way that shows it belongs to a particular person.”  The key elements are that it is individual to a person, that the person owns that something.  Ownership implies having control.

My definition of personalization is when a person gets unique content that reflects their individual preferences.  Targeting, in contrast, is when a person gets non-unique content based on characteristics they share with others.  In both cases, the content provider prioritizes what content is delivered, but in the first case it is based on first-hand knowledge of what an individual is interested in, where in the second case, it is based on second-hand assumptions about what seems relevant to the individual.

Personalization is based on explicit individual preferences, not assumptions

To understand personalization, we need to separate two dimensions:

  • whether the “signal” is about the individual himself, or about the interests of a crowd who are assumed to be similar to the individual
  • whether the “signal” is an explicit expression of interest, or an implicit assumptions based on prior behaviors

The following table shows how different signals can be aligned with either individual or crowd, and can be either explicit or implicit:

Table showing how different kinds of explicit and implicit preferences and behaviors can influence content delivery
Table showing how different kinds of explicit and implicit preferences and behaviors can influence content delivery

I’ve simplified the classic approaches of Facebook, Amazon, and Google to highlight elements that are most salient in their respective approaches.  In practice, each uses a mixture of signals to rank and filter content, crunching hundreds of different largely behavioral signals.  These high volume content providers, and others who are far smaller,  offer individuals the impression that the results are personalized (described as being “for you”) when they are primarily based on the aggregation of data across users, rather than individual feedback.  While these aggregation techniques improve general relevance (fewer inappropriate items), I don’t believe these behavior-driven approaches are sufficient to give individuals what’s most relevant to them personally.

How to implement personalization

Personalization matters because it is the only way individuals will be able to cope with the volume of content they face now and in the future.  Too much information is the problem, and genuine personalization needs to be the solution.  Brands need to help individuals connect with the content they most want, and not simply content that’s an adequate fit.  To do that, they need to ask questions, and not just make assumptions.

Lots of content providers talk about offering personalization, but the techniques they rely on have big weaknesses.  In future posts, I will discuss why big data approaches can’t solve personalization, and why small data using individual feedback is essential.

—Michael Andrews