Progressive Personalization: designing a better personalized experience
By: Jeffrey Macintyre
Tags: Product Design, Product Management, Personalization, Discovery, User Experience

Progressive Personalization: designing a better personalized experience

A framework to design for personalization, at any scale, in 2019.

Like many of you, I’ve worked both long and widely enough in digital to see personalization brave its journey from promise to pratfall to myth, and back again. Lately, it’s made the run from white whale to white-hot all over again. Personalization is re-emerging today with newfound relevance, sophistication and ubiquity.

But the past has a way of being prologue, doesn’t it?

Current status: Stranded, and stuck, in the personalization gap

Credit: Mark Riston for Marketing Week, June 21, 2017.

In the shadow of personalization’s more celebrated successes—the Amazon chestnut that 35% of total revenues are owed to the recommendations UX—various Cassandras highlight its shortcomings: personalization subjects consumers to filter bubbles and overzealous retargeting. I regard these as speed bumps to more evolved, nuanced and satisfying user experiences to come.

The real problem with personalization is executional.

I started Bucket, a consulting and research practice, on the heels of realizing where both my client needs were going, and where my skills were soon to be needed. What I’ve seen is sobering. The practice of personalization, in short, has not kept pace with technology. Personalization remains a top priority among CIOs and CMOs, and the hallmark of countless digital products.

For many, however, the inevitable move to personalization is one of steering right for the rocks, and certain shipwreck, if user experience professionals do not step forward soon.

Why?

The parable of the blind men and the elephant: Due to its broad swath of applications, and the disparate stakeholders or functional teams involved, personalization is uniquely predisposed to this particular operational problem of misaligned or shifting approaches to execution.

It’s not just that personalization is very much a content proposition, that requires this kind of expertise at hand.

It’s not that the solutions marketplace is so glutted and overrun, with standards and best practices few and far between.

Those are simply factors of the personalization gap as I’ve defined it.

However daunting, those factors can be addressed.

What cannot be overcome is the deafening silence among UX designers, strategists and technologists, whom need instead to explain that:

the tooling alone is not enough, and what personalization needs, more than anything, to be successful is clear, rigorous, thoughtful design.

Accenture calls personalization the challenge of “[h]ow to uniquely serve everyone without overwhelming anyone,” and reflects on humans moving from 50,000 square-foot shopping experiences with 100,000 products merchandised to a 4-inch screen regaling us with 10 million.

It’s a conversation we haven’t been having because:

personalization is a broader mosaic of various yet interdependent (or, particularly for our users, alike) technologies than is commonly understood; and

we’ve lacked for handholds that could guide a framework to designing holistically for personalized experience.

I have set upon making a wee dent in that.

The need: A decoder ring for designing personalized experience

For all the glare of AI going mainstream—for which personalization, in its many permutations, is ever the fruit of the tree—my own consulting tour of duty in this space tells me that many seem not to recognize that solving for personalized experience—maybe more familiarly today known by its various components: testing and optimization, dynamic content targeting, bots (messaging and voice), notifications or recommendations—is already table stakes in certain domains like ecommerce, a best practice already matured into a standard expectation. At one level or another, consumer behavior indicates it will be an existential matter for yours soon enough.

Personalization doesn’t have to be a slow-motion shipwreck, both for those who shirk the voyage and those worried about where it’s taking their team. So, what is there to do in rising to this challenge? Two things:

Navigating the personalization gap is the immediate hurdle to clear. After that, it’s about how to design for personalized experience. What are the “breakpoints,” in responsive parlance? What is the right mix of interactions, modules, elements or features to be personalized—and what data and information architecture will support it?

Personalization is an ambition, not a static end-point. This involves identifying and naming what personalized product opportunity means for you and your organization: a ground truth of what personalized tech can deliver to differentiate and focus the organization’s efforts, however that ambition is scoped. To see the difference up close, look at how notifications are handled as a design pattern by Pinterest and moreso an engineering function by Quora.

At the risk of self-aggrandisement, I have prepared a bumper sticker for your consideration:

Designing for personalization is about taking ‘raw’ engineering and, through a ‘cooked’ design, delivering a consistently effective, holistic experience.

(Granted, I have in mind a lengthy sticker. And a big vehicle.)

To apply this process—to cook up interactions at different scales, and in different client settings—I came up with a framework I call progressive personalization.

What’s entailed in designing for personalization?

For the wealth of literature in the wider world of personalization, search and discovery, recommendations and adjacent domains, I’ve found precious little that takes for its focus a broad-based look at designing or product managing a personalization program.

Namely, how can UXers focus and stay integrative in their execution?

Designing a program and practice of personalization deemed operationally effective and holistic is a higher bar to clear than many may realize.

For instance, this is not a domain in which “lift and shift” is ever feasible. The current hype cycle around bots and voice shows well enough the limits of merely tactical shovelware-style applications that do little based on common, typically superficial or surface-related tasks users have previously completed on sites or apps.

Colin A. Eagan shows one way of representing the given “recipes” for personalized page elements. Such a chart is a snapshot for specifying designed interactions. Credit: ICF Olson.

So how does one design coherently for a series of touchpoints that are increasingly expected to offer a consistency of experience, and carry over user data and context, in order to achieve their goals? A static design comp is fine pre-launch, but this will necessary need continual care and feeding as business rules and algorithms are adjusted.

Oh, and how does one design for a program that has many “cooks” crisscrossing functional lines of digital, IT, marketing, content and the like? (Few resources tell this tale better than John Berndt’s Personalization Mechanics.) This is part of the reason why pre-personalization, an array of preplanning activities to validate readiness to proceed with a functioning program, is a valuable early step to eventual program success.

Take a step back. Considered carefully, there is a series of preconditions to designing a successful personalization program:

scoping a minimum viable product

customer journey and user profiling/attribution

a feature development focus

data readiness

functioning taxonomy and metadata specifications

content strategy and content program

benchmarking and measurement

a broader roadmap

This list is necessary incomplete and doesn’t speak to the solutions architecture, technical buildout and implementation, or migration. It’s at about this point many will want to cognitively bow out and call it a day: this is a lot of interdependent balls in the air.

Personalization is like that.

As we plainly see, executing on either personalization program or practices is a matter, above all, of orchestration.

The outcome all these activities drive toward is simple: it’s about getting a grip on strategic and operational complexity so that personalization can be managed as a focused, integrated set of user features in your digital footprint.

Your program’s north star: A single abstraction layer

At its core, my thesis is simple.

A successful personalization effort is the progression of a single abstraction layer shared across touchpoints. If this is your guiding outcome, the rest should follow with relative ease and focus. It’s a matter of having a single source of truth about how your users, and their context, are known and how their interactions influence the UX they are presented.

This is not to proclaim that all personalization must involve budget heartburn and sprawling scopes and schedules. Far from it. It’s really about responsibly yoking the effort, objectives and impact to the ambition and resources allocated. (Another time I’ll review the virtues of t-shirt sizing one’s program as a roadmapping exercise.)

From this point, it becomes far easier to assess the sweet spot of personalization that meets those parameters, which is what I call the personalized product opportunity. From here, a designer can begin to conceptualize brand- or market-differentiating features and user stories that realize that opportunity based on the outcome (and fixed requirement) of a single abstraction layer.

If this sounds like your mission, and the associated complexity is familiar, progressive personalization could be an approach useful to you.

For our next step, we need a mental model to make good on designing out the conceptual abstraction layer. Or maybe we need a pattern library of personalization? Or a launch pad for feature and functionality ideas. How about all of the above.

Let’s go berrypicking.

Credit: Marcia Bates on Wikipedia.

Progressive personalization is a framework for designing personalized experience

Progressive personalization is a framework to help designers, strategists and technologists assess where and how to build personalization into their digital products and touchpoints, and how to evolve its use across a spectrum of technologies and applications, in a singularly focused, holistically integrated manner. While the term progressive personalization has turned up from time to time (as a swapping mechanic in 1:1 matching feature technology), to the best of my knowledge the idea has not. Today there is literature on tactical design considerations for personalized or AI settings. There are tips on how to construct a program. But there is not an established way of thinking across features, products and into the program-level about personalized experience—especially in the broad range of ways and interfaces in which personalization is delivered, from AI to recommenders, and A/B testing to machine learning, and bots to voice.

The thrust of this exercise is to provide a durable lens to measure performance, assess user value and transfer a singular, apples-to-apples way of thinking across what are often described as disparate design settings today.

Historically, when I’ve been tasked with defining a content offering, I have returned to a much-traveled four-quadrant diagram, organized along the spectrum of intent and of interactivity. In my experience, designing an experience has typically meant solving for either a lean-back, low-intent experience, for instance, or a high-intent, lean-in one—or some variation thereof. Intent is also sometimes classified as skimming and diving user behavior, and interactivity can be as simple as context and at-hand interfaces. For example, try browsing on Echo sometime, it’s painful today: voice-only interfaces are better at search, where intent tends to be higher, than a general browse navigation behavior.

Meanwhile, don’t forget that, in navigating that personalization gap, we have in the background a sense of a hierarchy of personalized experience:

function (e.g., a search box)

(e.g., a search box) feature (e.g., notifications)

(e.g., notifications) experience (e.g., an email)

(e.g., an email) product (e.g., an app)

If personalization can be considered not just along a spectrum of applications and interfaces but also a progression of technical and design effort, we can start to dimensionalize and assess the design choices before us.

We have in the following three dimensions an idea of three fairly distinct stakeholder interests represented:

the hierarchy of personalized experience represents the work of digital product teams

the dimension of intent speaks to user investment in a given interaction

the dimension of interactivity speaks to how that interaction is designed to suit the behavior

I’ve been using these models like a trusty stool for years, but it took an unassuming conversation with Jenny Benevento to set me straight about a more sophisticated model—straight into the dojo of Marcia Bates, vanguard information scientist.

What is berrypicking

Bates pioneered the berrypicking thesis, which remains a revolutionary argument in how information environments should be designed.

As august a figure in information sciences as there is, Bates has in her career commended folks to the mission of understanding user behavior and to designing interfaces that accommodate those behaviors. Practitioners “comfortable with information, people, and technology” excel at this work—and data, users, and tech are critical legs of the personalization stool.

Her berrypicking thesis not only advanced a new modality of understanding users’ information-seeking behavior, but it also promulgated the following:

Maximizing the different modes of information retrieval is best. “If we want to meet users’ needs, we should enable them to search in familiar ways that are effective for them,” she writes. Elsewhere: “The more different strategies searchers can use an information store, the more retrieval effectiveness and efficiency is possible.”

“If we want to meet users’ needs, we should enable them to search in familiar ways that are effective for them,” she writes. Elsewhere: “The more different strategies searchers can use an information store, the more retrieval effectiveness and efficiency is possible.” One overarching architecture is nevertheless key. “A model containing a unified perspective, incorporating the full range of searcher behaviors in the information seeking process, may make it easier to design many more such features for information retrieval systems.”

“A model containing a unified perspective, incorporating the full range of searcher behaviors in the information seeking process, may make it easier to design many more such features for information retrieval systems.” Design is the way we construct a solution. “With this broader picture of information retrieval in mind, many new design possibilities open up.”

What she gives us in berrypicking is a Rosetta Stone of fundamental, root-level interaction behavior. (Nerd alert: Not to mention how she anticipates binge watching behavior in her consideration of consuming journal runs.)

Berrypicking, decoded for 2017 personalized experience

It’s not a stretch to see how Bates’ berrypicking perfectly encapsulates some (all?) of the prevailing modes of information retrieval today as well. We see a handful of recurring modes of interaction emerge that we now commonly understand today as:

browse search alerts (or push media, like email) consumption (episodically [a single clickthrough] or serially [bingeing])

Further, these four modes snap rather neatly to the four-quadrant model:

browse is generally high-intent, lean-back

search is generally high-intent, lean-in

alerts are low-intent, lean-back

consumption is low-intent, lean-in

Considered this way, a designer’s next step is to assess their understanding of user behavior, user needs, business value and product differentiation, and then focus accordingly. (I’d like to expand on this in future.)

Seen from this vantage, berrypicking and information sciences has the potential to be a skeleton key to personalization and AI. Used effectively, this approach can open any door before you in methodically designing experiences that address the needs and opportunity of users.

Consider these behavioral vectors of browse, search, alerts and consumption as a starting point in orienting your team about its personalization regimen, and sizing what will be effective in catering to the user needs you identify for each—with a sense of the higher-order user value clearly in view (more on that below) to evaluate your progress.

How does this work practically? A client scenario

To unpack this further, let’s drop down into a client example.

A product team for an ambitious video hardware startup gave me a brief to devise features and experiences that would clearly separate them from the status quo of episode program guides from the incumbent competition, and from card stack mausoleums of the new-line streaming services like Netflix. I was armed with industry-standard data and instructed that editorialized touch-up overtop could be provisioned. I came back with some four-quadrant concepting:

low-intent, lean-back: A suite of editorially curated “impulses” that will suggest TV and film titles prompted by user heuristics like hate-watching (it was fun specifying that from the data) and other nontraditional concepts.

that will suggest TV and film titles prompted by user heuristics like hate-watching (it was fun specifying that from the data) and other nontraditional concepts. low-intent, lean-in: A matchmaking recommendation feature for children’s viewing that, querying an industry data vendor API, produces recommended titles to watch based on the age and gender of children present.

that, querying an industry data vendor API, produces recommended titles to watch based on the age and gender of children present. high-intent, lean-back: A way to query “ice fishing” programming and not return results for the wider term of “fishing.” People want to pinpoint their interests, and sometimes less really is more when zeroing out noisier searches.

and not return results for the wider term of “fishing.” People want to pinpoint their interests, and sometimes less really is more when zeroing out noisier searches. high-intent, lean-in: A white-listing function that allows users to correct for recommendations “contamination” in multi-user settings (when there are no sub-profiles). In other words, a spouse’s viewing history could be suppressed or downvoted, at least at the level of recurring false positive matches suggested from the title library.

The overall idea was to differentiate the hardware’s software UX and feature set in a sustainable but remarkable enough way that it would readily surface product opportunity and marketplace differentiation. The method was to foreground the opportunity in terms of discrete user stories and lean product development, while anchoring them to a broader framework of the personalized product opportunity for the hardware client’s customers.

Progressive personalization is also a way of pinpointing precise function- or feature-level improvements that can move a personalization regime forward, without having to bet the farm all at once.

Your berrypicking may vary—that’s the point

While I have been employing this framework in my client work for a few years, I harbor no illusions that it provides perfect clarity in all situations. The argument I want to make is instead the necessity of a design architecture for personalization as a whole. Only that will maximize focus and planned outcomes, and make evolving the personalization benefit similarly successful, and the net effect greater, over time. See, for example, Colin Eagan’s model of bucketing for “tasks at hand” versus “big picture” messaging in personalized settings.