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This Person Does Not Exist is a website that generates human faces with a machine learning algorithm. It takes real portraits and recombines them into fake human faces. We recently scrolled past a LinkedIn post stating that this website could be useful “if you are developing a persona and looking for a photo.” 

We agree: the computer-generated faces could be a great match for personas—but not for the reason you might think. Ironically, the website highlights the core issue of this very common design method: the person(a) does not exist. Like the pictures, personas are artificially made. Information is taken out of natural context and recombined into an isolated snapshot that’s detached from reality. 

But strangely enough, designers use personas to inspire their design for the real world. 

Personas: A step back

Most designers have created, used, or come across personas at least once in their career. In their article “Personas - A Simple Introduction,” the Interaction Design Foundation defines personas as “fictional characters, which you create based upon your research in order to represent the different user types that might use your service, product, site, or brand.” In their most complete expression, personas typically consist of a name, profile picture, quotes, demographics, goals, needs, behavior in relation to a certain service/product, emotions, and motivations (for example, see Creative Companion’s Persona Core Poster). The purpose of personas, as stated by design agency Designit, is “to make the research relatable, [and] easy to communicate, digest, reference, and apply to product and service development.”

The decontextualization of personas

Personas are popular because they make “dry” research data more relatable, more human. However, this method constrains the researcher’s data analysis in such a way that the investigated users are removed from their unique contexts. As a result, personas don’t portray key factors that make you understand their decision-making process or allow you to relate to users’ thoughts and behavior; they lack stories. You understand what the persona did, but you don’t have the background to understand why. You end up with representations of users that are actually less human.

This “decontextualization” we see in personas happens in four ways, which we’ll explain below. 

Personas assume people are static 

Although many companies still try to box in their employees and customers with outdated personality tests (referring to you, Myers-Briggs), here’s a painfully obvious truth: people are not a fixed set of features. You act, think, and feel differently according to the situations you experience. You appear different to different people; you might act friendly to some, rough to others. And you change your mind all the time about decisions you’ve taken. 

Modern psychologists agree that while people generally behave according to certain patterns, it’s actually a combination of background and environment that determines how people act and take decisions. The context—the environment, the influence of other people, your mood, the entire history that led up to a situation—determines the kind of person you are in each specific moment. 

In their attempt to simplify reality, personas do not take this variability into account; they present a user as a fixed set of features. Like personality tests, personas snatch people away from real life. Even worse, people are reduced to a label and categorized as “that kind of person” with no means to exercise their innate flexibility. This practice reinforces stereotypes, lowers diversity, and doesn’t reflect reality. 

Personas focus on individuals, not the environment

In the real world, you’re designing for a context, not for an individual. Each person lives in a family, a community, an ecosystem, where there are environmental, political, and social factors you need to consider. A design is never meant for a single user. Rather, you design for one or more particular contexts in which many people might use that product. Personas, however, show the user alone rather than describe how the user relates to the environment. 

Would you always make the same decision over and over again? Maybe you’re a committed vegan but still decide to buy some meat when your relatives are coming over. As they depend on different situations and variables, your decisions—and behavior, opinions, and statements—are not absolute but highly contextual. The persona that “represents” you wouldn’t take into account this dependency, because it doesn’t specify the premises of your decisions. It doesn’t provide a justification of why you act the way you do. Personas enact the well-known bias called fundamental attribution error: explaining others’ behavior too much by their personality and too little by the situation.

As mentioned by the Interaction Design Foundation, personas are usually placed in a scenario that’s a “specific context with a problem they want to or have to solve”—does that mean context actually is considered? Unfortunately, what often happens is that you take a fictional character and based on that fiction determine how this character might deal with a certain situation. This is made worse by the fact that you haven’t even fully investigated and understood the current context of the people your persona seeks to represent; so how could you possibly understand how they would act in new situations? 

Personas are meaningless averages

As mentioned in Shlomo Goltz’s introductory article on Smashing Magazine, “a persona is depicted as a specific person but is not a real individual; rather, it is synthesized from observations of many people.” A well-known critique to this aspect of personas is that the average person does not exist, as per the famous example of the USA Air Force designing planes based on the average of 140 of their pilots’ physical dimensions and not a single pilot actually fitting within that average seat. 

The same limitation applies to mental aspects of people. Have you ever heard a famous person say, “They took what I said out of context! They used my words, but I didn’t mean it like that.” The celebrity’s statement was reported literally, but the reporter failed to explain the context around the statement and didn’t describe the non-verbal expressions. As a result, the intended meaning was lost. You do the same when you create personas: you collect somebody’s statement (or goal, or need, or emotion), of which the meaning can only be understood if you provide its own specific context, yet report it as an isolated finding. 

But personas go a step further, extracting a decontextualized finding and joining it with another decontextualized finding from somebody else. The resulting set of findings often does not make sense: it’s unclear, or even contrasting, because it lacks the underlying reasons on why and how that finding has arisen. It lacks meaning. And the persona doesn’t give you the full background of the person(s) to uncover this meaning: you would need to dive into the raw data for each single persona item to find it. What, then, is the usefulness of the persona?

The relatability of personas is deceiving

To a certain extent, designers realize that a persona is a lifeless average. To overcome this, designers invent and add “relatable” details to personas to make them resemble real individuals. Nothing captures the absurdity of this better than a sentence by the Interaction Design Foundation: “Add a few fictional personal details to make the persona a realistic character.” In other words, you add non-realism in an attempt to create more realism. You deliberately obscure the fact that “John Doe” is an abstract representation of research findings; but wouldn’t it be much more responsible to emphasize that John is only an abstraction? If something is artificial, let’s present it as such.

It’s the finishing touch of a persona’s decontextualization: after having assumed that people’s personalities are fixed, dismissed the importance of their environment, and hidden meaning by joining isolated, non-generalizable findings, designers invent new context to create (their own) meaning. In doing so, as with everything they create, they introduce a host of biases. As phrased by Designit, as designers we can “contextualize [the persona] based on our reality and experience. We create connections that are familiar to us.” This practice reinforces stereotypes, doesn’t reflect real-world diversity, and gets further away from people’s actual reality with every detail added. 

To do good design research, we should report the reality “as-is” and make it relatable for our audience, so everyone can use their own empathy and develop their own interpretation and emotional response.

Dynamic Selves: The alternative to personas

If we shouldn’t use personas, what should we do instead? 

Designit has proposed using Mindsets instead of personas. Each Mindset is a “spectrum of attitudes and emotional responses that different people have within the same context or life experience.” It challenges designers to not get fixated on a single user’s way of being. Unfortunately, while being a step in the right direction, this proposal doesn’t take into account that people are part of an environment that determines their personality, their behavior, and, yes, their mindset. Therefore, Mindsets are also not absolute but change in regard to the situation. The question remains, what determines a certain Mindset?

Another alternative comes from Margaret P., author of the article “Kill Your Personas,” who has argued for replacing personas with persona spectrums that consist of a range of user abilities. For example, a visual impairment could be permanent (blindness), temporary (recovery from eye surgery), or situational (screen glare). Persona spectrums are highly useful for more inclusive and context-based design, as they’re based on the understanding that the context is the pattern, not the personality. Their limitation, however, is that they have a very functional take on users that misses the relatability of a real person taken from within a spectrum. 

In developing an alternative to personas, we aim to transform the standard design process to be context-based. Contexts are generalizable and have patterns that we can identify, just like we tried to do previously with people. So how do we identify these patterns? How do we ensure truly context-based design? 

Understand real individuals in multiple contexts

Nothing is more relatable and inspiring than reality. Therefore, we have to understand real individuals in their multi-faceted contexts, and use this understanding to fuel our design. We refer to this approach as Dynamic Selves.

Let’s take a look at what the approach looks like, based on an example of how one of us applied it in a recent project that researched habits of Italians around energy consumption. We drafted a design research plan aimed at investigating people’s attitudes toward energy consumption and sustainable behavior, with a focus on smart thermostats. 

1. Choose the right sample

When we argue against personas, we’re often challenged with quotes such as “Where are you going to find a single person that encapsulates all the information from one of these advanced personas[?]” The answer is simple: you don’t have to. You don’t need to have information about many people for your insights to be deep and meaningful. 

In qualitative research, validity does not derive from quantity but from accurate sampling. You select the people that best represent the “population” you’re designing for. If this sample is chosen well, and you have understood the sampled people in sufficient depth, you’re able to infer how the rest of the population thinks and behaves. There’s no need to study seven Susans and five Yuriys; one of each will do. 

Similarly, you don’t need to understand Susan in fifteen different contexts. Once you’ve seen her in a couple of diverse situations, you’ve understood the scheme of Susan’s response to different contexts. Not Susan as an atomic being but Susan in relation to the surrounding environment: how she might act, feel, and think in different situations. 

Given that each person is representative of a part of the total population you’re researching, it becomes clear why each should be represented as an individual, as each already is an abstraction of a larger group of individuals in similar contexts. You don’t want abstractions of abstractions! These selected people need to be understood and shown in their full expression, remaining in their microcosmos—and if you want to identify patterns you can focus on identifying patterns in contexts.

Yet the question remains: how do you select a representative sample? First of all, you have to consider what’s the target audience of the product or service you are designing: it might be useful to look at the company’s goals and strategy, the current customer base, and/or a possible future target audience. 

In our example project, we were designing an application for those who own a smart thermostat. In the future, everyone could have a smart thermostat in their house. Right now, though, only early adopters own one. To build a significant sample, we needed to understand the reason why these early adopters became such. We therefore recruited by asking people why they had a smart thermostat and how they got it. There were those who had chosen to buy it, those who had been influenced by others to buy it, and those who had found it in their house. So we selected representatives of these three situations, from different age groups and geographical locations, with an equal balance of tech savvy and non-tech savvy participants. 

2. Conduct your research

After having chosen and recruited your sample, conduct your research using ethnographic methodologies. This will make your qualitative data rich with anecdotes and examples. In our example project, given COVID-19 restrictions, we converted an in-house ethnographic research effort into remote family interviews, conducted from home and accompanied by diary studies.

To gain an in-depth understanding of attitudes and decision-making trade-offs, the research focus was not limited to the interviewee alone but deliberately included the whole family. Each interviewee would tell a story that would then become much more lively and precise with the corrections or additional details coming from wives, husbands, children, or sometimes even pets. We also focused on the relationships with other meaningful people (such as colleagues or distant family) and all the behaviors that resulted from those relationships. This wide research focus allowed us to shape a vivid mental image of dynamic situations with multiple actors. 

It’s essential that the scope of the research remains broad enough to be able to include all possible actors. Therefore, it normally works best to define broad research areas with macro questions. Interviews are best set up in a semi-structured way, where follow-up questions will dive into topics mentioned spontaneously by the interviewee. This open-minded “plan to be surprised” will yield the most insightful findings. When we asked one of our participants how his family regulated the house temperature, he replied, “My wife has not installed the thermostat’s app—she uses WhatsApp instead. If she wants to turn on the heater and she is not home, she will text me. I am her thermostat.”

3. Analysis: Create the Dynamic Selves

During the research analysis, you start representing each individual with multiple Dynamic Selves, each “Self” representing one of the contexts you have investigated. The core of each Dynamic Self is a quote, which comes supported by a photo and a few relevant demographics that illustrate the wider context. The research findings themselves will show which demographics are relevant to show. In our case, as our research focused on families and their lifestyle to understand their needs for thermal regulation, the important demographics were family type, number and nature of houses owned, economic status, and technological maturity. (We also included the individual’s name and age, but they’re optional—we included them to ease the stakeholders’ transition from personas and be able to connect multiple actions and contexts to the same person).

To capture exact quotes, interviews need to be video-recorded and notes need to be taken verbatim as much as possible. This is essential to the truthfulness of the several Selves of each participant. In the case of real-life ethnographic research, photos of the context and anonymized actors are essential to build realistic Selves. Ideally, these photos should come directly from field research, but an evocative and representative image will work, too, as long as it’s realistic and depicts meaningful actions that you associate with your participants. For example, one of our interviewees told us about his mountain home where he used to spend every weekend with his family. Therefore, we portrayed him hiking with his little daughter. 

At the end of the research analysis, we displayed all of the Selves’ “cards” on a single canvas, categorized by activities. Each card displayed a situation, represented by a quote and a unique photo. All participants had multiple cards about themselves.

4. Identify design opportunities

Once you have collected all main quotes from the interview transcripts and diaries, and laid them all down as Self cards, you will see patterns emerge. These patterns will highlight the opportunity areas for new product creation, new functionalities, and new services—for new design. 

In our example project, there was a particularly interesting insight around the concept of humidity. We realized that people don’t know what humidity is and why it is important to monitor it for health: an environment that’s too dry or too wet can cause respiratory problems or worsen existing ones. This highlighted a big opportunity for our client to educate users on this concept and become a health advisor.

Benefits of Dynamic Selves

When you use the Dynamic Selves approach in your research, you start to notice unique social relations, peculiar situations real people face and the actions that follow, and that people are surrounded by changing environments. In our thermostat project, we have come to know one of the participants, Davide, as a boyfriend, dog-lover, and tech enthusiast. 

Davide is an individual we might have once reduced to a persona called “tech enthusiast.” But we can have tech enthusiasts who have families or are single, who are rich or poor. Their motivations and priorities when deciding to purchase a new thermostat can be opposite according to these different frames. 

Once you have understood Davide in multiple situations, and for each situation have understood in sufficient depth the underlying reasons for his behavior, you’re able to generalize how he would act in another situation. You can use your understanding of him to infer what he would think and do in the contexts (or scenarios) that you design for.

The Dynamic Selves approach aims to dismiss the conflicted dual purpose of personas—to summarize and empathize at the same time—by separating your research summary from the people you’re seeking to empathize with. This is important because our empathy for people is affected by scale: the bigger the group, the harder it is to feel empathy for others. We feel the strongest empathy for individuals we can personally relate to.  

If you take a real person as inspiration for your design, you no longer need to create an artificial character. No more inventing details to make the character more “realistic,” no more unnecessary additional bias. It’s simply how this person is in real life. In fact, in our experience, personas quickly become nothing more than a name in our priority guides and prototype screens, as we all know that these characters don’t really exist. 

Another powerful benefit of the Dynamic Selves approach is that it raises the stakes of your work: if you mess up your design, someone real, a person you and the team know and have met, is going to feel the consequences. It might stop you from taking shortcuts and will remind you to conduct daily checks on your designs.

And finally, real people in their specific contexts are a better basis for anecdotal storytelling and therefore are more effective in persuasion. Documentation of real research is essential in achieving this result. It adds weight and urgency behind your design arguments: “When I met Alessandra, the conditions of her workplace struck me. Noise, bad ergonomics, lack of light, you name it. If we go for this functionality, I’m afraid we’re going to add complexity to her life.”

Conclusion

Designit mentioned in their article on Mindsets that “design thinking tools offer a shortcut to deal with reality’s complexities, but this process of simplification can sometimes flatten out people’s lives into a few general characteristics.” Unfortunately, personas have been culprits in a crime of oversimplification. They are unsuited to represent the complex nature of our users’ decision-making processes and don’t account for the fact that humans are immersed in contexts. 

Design needs simplification but not generalization. You have to look at the research elements that stand out: the sentences that captured your attention, the images that struck you, the sounds that linger. Portray those, use them to describe the person in their multiple contexts. Both insights and people come with a context; they cannot be cut from that context because it would remove meaning. 

It’s high time for design to move away from fiction, and embrace reality—in its messy, surprising, and unquantifiable beauty—as our guide and inspiration.

As a UX professional in today’s data-driven landscape, it’s increasingly likely that you’ve been asked to design a personalized digital experience, whether it’s a public website, user portal, or native application. Yet while there continues to be no shortage of marketing hype around personalization platforms, we still have very few standardized approaches for implementing personalized UX.

That’s where we come in. After completing dozens of personalization projects over the past few years, we gave ourselves a goal: could you create a holistic personalization framework specifically for UX practitioners? The Personalization Pyramid is a designer-centric model for standing up human-centered personalization programs, spanning data, segmentation, content delivery, and overall goals. By using this approach, you will be able to understand the core components of a contemporary, UX-driven personalization program (or at the very least know enough to get started). 

Getting Started

For the sake of this article, we’ll assume you’re already familiar with the basics of digital personalization. A good overview can be found here: Website Personalization Planning. While UX projects in this area can take on many different forms, they often stem from similar starting points.      

Common scenarios for starting a personalization project:

• Your organization or client purchased a content management system (CMS) or marketing automation platform (MAP) or related technology that supports personalization
• The CMO, CDO, or CIO has identified personalization as a goal
• Customer data is disjointed or ambiguous
• You are running some isolated targeting campaigns or A/B testing
• Stakeholders disagree on personalization approach
• Mandate of customer privacy rules (e.g. GDPR) requires revisiting existing user targeting practices

Regardless of where you begin, a successful personalization program will require the same core building blocks. We’ve captured these as the “levels” on the pyramid. Whether you are a UX designer, researcher, or strategist, understanding the core components can help make your contribution successful.  

From top to bottom, the levels include:

1. North Star: What larger strategic objective is driving the personalization program? 
2. Goals: What are the specific, measurable outcomes of the program? 
3. Touchpoints: Where will the personalized experience be served?
4. Contexts and Campaigns: What personalization content will the user see?
5. User Segments: What constitutes a unique, usable audience? 
6. Actionable Data: What reliable and authoritative data is captured by our technical platform to drive personalization?  
7. Raw Data: What wider set of data is conceivably available (already in our setting) allowing you to personalize?

We’ll go through each of these levels in turn. To help make this actionable, we created an accompanying deck of cards to illustrate specific examples from each level. We’ve found them helpful in personalization brainstorming sessions, and will include examples for you here.

Starting at the Top

The components of the pyramid are as follows:

North Star

A north star is what you are aiming for overall with your personalization program (big or small). The North Star defines the (one) overall mission of the personalization program. What do you wish to accomplish? North Stars cast a shadow. The bigger the star, the bigger the shadow. Example of North Starts might include: 

1. Function: Personalize based on basic user inputs. Examples: “Raw” notifications, basic search results, system user settings and configuration options, general customization, basic optimizations
2. Feature: Self-contained personalization componentry. Examples: “Cooked” notifications, advanced optimizations (geolocation), basic dynamic messaging, customized modules, automations, recommenders
3. Experience: Personalized user experiences across multiple interactions and user flows. Examples: Email campaigns, landing pages, advanced messaging (i.e. C2C chat) or conversational interfaces, larger user flows and content-intensive optimizations (localization).
4. Product: Highly differentiating personalized product experiences. Examples: Standalone, branded experiences with personalization at their core, like the “algotorial” playlists by Spotify such as Discover Weekly.

Goals

As in any good UX design, personalization can help accelerate designing with customer intentions. Goals are the tactical and measurable metrics that will prove the overall program is successful. A good place to start is with your current analytics and measurement program and metrics you can benchmark against. In some cases, new goals may be appropriate. The key thing to remember is that personalization itself is not a goal, rather it is a means to an end. Common goals include:

• Conversion
• Time on task
• Net promoter score (NPS)
• Customer satisfaction 

Touchpoints

Touchpoints are where the personalization happens. As a UX designer, this will be one of your largest areas of responsibility. The touchpoints available to you will depend on how your personalization and associated technology capabilities are instrumented, and should be rooted in improving a user’s experience at a particular point in the journey. Touchpoints can be multi-device (mobile, in-store, website) but also more granular (web banner, web pop-up etc.). Here are some examples:

Channel-level Touchpoints

• Email: Role
• Email: Time of open
• In-store display (JSON endpoint)
• Native app
• Search

Wireframe-level Touchpoints

• Web overlay
• Web alert bar
• Web banner
• Web content block
• Web menu

If you’re designing for web interfaces, for example, you will likely need to include personalized “zones” in your wireframes. The content for these can be presented programmatically in touchpoints based on our next step, contexts and campaigns.

Contexts and Campaigns

Once you’ve outlined some touchpoints, you can consider the actual personalized content a user will receive. Many personalization tools will refer to these as “campaigns” (so, for example, a campaign on a web banner for new visitors to the website). These will programmatically be shown at certain touchpoints to certain user segments, as defined by user data. At this stage, we find it helpful to consider two separate models: a context model and a content model. The context helps you consider the level of engagement of the user at the personalization moment, for example a user casually browsing information vs. doing a deep-dive. Think of it in terms of information retrieval behaviors. The content model can then help you determine what type of personalization to serve based on the context (for example, an “Enrich” campaign that shows related articles may be a suitable supplement to extant content).

Personalization Context Model:

1. Browse
2. Skim
3. Nudge
4. Feast

Personalization Content Model:

1. Alert
2. Make Easier
3. Cross-Sell
4. Enrich

We’ve written extensively about each of these models elsewhere, so if you’d like to read more you can check out Colin’s Personalization Content Model and Jeff’s Personalization Context Model. 

User Segments

User segments can be created prescriptively or adaptively, based on user research (e.g. via rules and logic tied to set user behaviors or via A/B testing). At a minimum you will likely need to consider how to treat the unknown or first-time visitor, the guest or returning visitor for whom you may have a stateful cookie (or equivalent post-cookie identifier), or the authenticated visitor who is logged in. Here are some examples from the personalization pyramid:

• Unknown
• Guest
• Authenticated
• Default
• Referred
• Role
• Cohort
• Unique ID

Actionable Data

Every organization with any digital presence has data. It’s a matter of asking what data you can ethically collect on users, its inherent reliability and value, as to how can you use it (sometimes known as “data activation.”) Fortunately, the tide is turning to first-party data: a recent study by Twilio estimates some 80% of businesses are using at least some type of first-party data to personalize the customer experience. 

First-party data represents multiple advantages on the UX front, including being relatively simple to collect, more likely to be accurate, and less susceptible to the “creep factor” of third-party data. So a key part of your UX strategy should be to determine what the best form of data collection is on your audiences. Here are some examples:

There is a progression of profiling when it comes to recognizing and making decisioning about different audiences and their signals. It tends to move towards more granular constructs about smaller and smaller cohorts of users as time and confidence and data volume grow.

While some combination of implicit / explicit data is generally a prerequisite for any implementation (more commonly referred to as first party and third-party data) ML efforts are typically not cost-effective directly out of the box. This is because a strong data backbone and content repository is a prerequisite for optimization. But these approaches should be considered as part of the larger roadmap and may indeed help accelerate the organization’s overall progress. Typically at this point you will partner with key stakeholders and product owners to design a profiling model. The profiling model includes defining approach to configuring profiles, profile keys, profile cards and pattern cards. A multi-faceted approach to profiling which makes it scalable.

Pulling it Together

While the cards comprise the starting point to an inventory of sorts (we provide blanks for you to tailor your own), a set of potential levers and motivations for the style of personalization activities you aspire to deliver, they are more valuable when thought of in a grouping. 

In assembling a card “hand”, one can begin to trace the entire trajectory from leadership focus down through a strategic and tactical execution. It is also at the heart of the way both co-authors have conducted workshops in assembling a program backlog—which is a fine subject for another article.

In the meantime, what is important to note is that each colored class of card is helpful to survey in understanding the range of choices potentially at your disposal, it is threading through and making concrete decisions about for whom this decisioning will be made: where, when, and how.

Lay Down Your Cards

Any sustainable personalization strategy must consider near, mid and long-term goals. Even with the leading CMS platforms like Sitecore and Adobe or the most exciting composable CMS DXP out there, there is simply no “easy button” wherein a personalization program can be stood up and immediately view meaningful results. That said, there is a common grammar to all personalization activities, just like every sentence has nouns and verbs. These cards attempt to map that territory.

Picture this. You’ve joined a squad at your company that’s designing new product features with an emphasis on automation or AI. Or your company has just implemented a personalization engine. Either way, you’re designing with data. Now what? When it comes to designing for personalization, there are many cautionary tales, no overnight successes, and few guides for the perplexed. 

Between the fantasy of getting it right and the fear of it going wrong—like when we encounter “persofails” in the vein of a company repeatedly imploring everyday consumers to buy additional toilet seats—the personalization gap is real. It’s an especially confounding place to be a digital professional without a map, a compass, or a plan.

For those of you venturing into personalization, there’s no Lonely Planet and few tour guides because effective personalization is so specific to each organization’s talent, technology, and market position. 

But you can ensure that your team has packed its bags sensibly.

There’s a DIY formula to increase your chances for success. At minimum, you’ll defuse your boss’s irrational exuberance. Before the party you’ll need to effectively prepare.

We call it prepersonalization.

Behind the music

Consider Spotify’s DJ feature, which debuted this past year.

We’re used to seeing the polished final result of a personalization feature. Before the year-end award, the making-of backstory, or the behind-the-scenes victory lap, a personalized feature had to be conceived, budgeted, and prioritized. Before any personalization feature goes live in your product or service, it lives amid a backlog of worthy ideas for expressing customer experiences more dynamically.

So how do you know where to place your personalization bets? How do you design consistent interactions that won’t trip up users or—worse—breed mistrust? We’ve found that for many budgeted programs to justify their ongoing investments, they first needed one or more workshops to convene key stakeholders and internal customers of the technology. Make yours count.

​From Big Tech to fledgling startups, we’ve seen the same evolution up close with our clients. In our experiences with working on small and large personalization efforts, a program’s ultimate track record—and its ability to weather tough questions, work steadily toward shared answers, and organize its design and technology efforts—turns on how effectively these prepersonalization activities play out.

Time and again, we’ve seen effective workshops separate future success stories from unsuccessful efforts, saving countless time, resources, and collective well-being in the process.

A personalization practice involves a multiyear effort of testing and feature development. It’s not a switch-flip moment in your tech stack. It’s best managed as a backlog that often evolves through three steps: 

1. customer experience optimization (CXO, also known as A/B testing or experimentation)
2. always-on automations (whether rules-based or machine-generated)
3. mature features or standalone product development (such as Spotify’s DJ experience)

This is why we created our progressive personalization framework and why we’re field-testing an accompanying deck of cards: we believe that there’s a base grammar, a set of “nouns and verbs” that your organization can use to design experiences that are customized, personalized, or automated. You won’t need these cards. But we strongly recommend that you create something similar, whether that might be digital or physical.

Set your kitchen timer

How long does it take to cook up a prepersonalization workshop? The surrounding assessment activities that we recommend including can (and often do) span weeks. For the core workshop, we recommend aiming for two to three days. Here’s a summary of our broader approach along with details on the essential first-day activities.

The full arc of the wider workshop is threefold:

1. Kickstart: This sets the terms of engagement as you focus on the opportunity as well as the readiness and drive of your team and your leadership. .
2. Plan your work: This is the heart of the card-based workshop activities where you specify a plan of attack and the scope of work.
3. Work your plan: This phase is all about creating a competitive environment for team participants to individually pitch their own pilots that each contain a proof-of-concept project, its business case, and its operating model.

Give yourself at least a day, split into two large time blocks, to power through a concentrated version of those first two phases.

Kickstart: Whet your appetite

We call the first lesson the “landscape of connected experience.” It explores the personalization possibilities in your organization. A connected experience, in our parlance, is any UX requiring the orchestration of multiple systems of record on the backend. This could be a content-management system combined with a marketing-automation platform. It could be a digital-asset manager combined with a customer-data platform.

Spark conversation by naming consumer examples and business-to-business examples of connected experience interactions that you admire, find familiar, or even dislike. This should cover a representative range of personalization patterns, including automated app-based interactions (such as onboarding sequences or wizards), notifications, and recommenders. We have a catalog of these in the cards. Here’s a list of 142 different interactions to jog your thinking.

This is all about setting the table. What are the possible paths for the practice in your organization? If you want a broader view, here’s a long-form primer and a strategic framework.

Assess each example that you discuss for its complexity and the level of effort that you estimate that it would take for your team to deliver that feature (or something similar). In our cards, we divide connected experiences into five levels: functions, features, experiences, complete products, and portfolios. Size your own build here. This will help to focus the conversation on the merits of ongoing investment as well as the gap between what you deliver today and what you want to deliver in the future.

Next, have your team plot each idea on the following 2×2 grid, which lays out the four enduring arguments for a personalized experience. This is critical because it emphasizes how personalization can not only help your external customers but also affect your own ways of working. It’s also a reminder (which is why we used the word argument earlier) of the broader effort beyond these tactical interventions.

Each team member should vote on where they see your product or service putting its emphasis. Naturally, you can’t prioritize all of them. The intention here is to flesh out how different departments may view their own upsides to the effort, which can vary from one to the next. Documenting your desired outcomes lets you know how the team internally aligns across representatives from different departments or functional areas.

The third and final kickstart activity is about naming your personalization gap. Is your customer journey well documented? Will data and privacy compliance be too big of a challenge? Do you have content metadata needs that you have to address? (We’re pretty sure that you do: it’s just a matter of recognizing the relative size of that need and its remedy.) In our cards, we’ve noted a number of program risks, including common team dispositions. Our Detractor card, for example, lists six stakeholder behaviors that hinder progress.

Effectively collaborating and managing expectations is critical to your success. Consider the potential barriers to your future progress. Press the participants to name specific steps to overcome or mitigate those barriers in your organization. As studies have shown, personalization efforts face many common barriers.

At this point, you’ve hopefully discussed sample interactions, emphasized a key area of benefit, and flagged key gaps? Good—you’re ready to continue.

Hit that test kitchen

Next, let’s look at what you’ll need to bring your personalization recipes to life. Personalization engines, which are robust software suites for automating and expressing dynamic content, can intimidate new customers. Their capabilities are sweeping and powerful, and they present broad options for how your organization can conduct its activities. This presents the question: Where do you begin when you’re configuring a connected experience?

What’s important here is to avoid treating the installed software like it were a dream kitchen from some fantasy remodeling project (as one of our client executives memorably put it). These software engines are more like test kitchens where your team can begin devising, tasting, and refining the snacks and meals that will become a part of your personalization program’s regularly evolving menu.

The ultimate menu of the prioritized backlog will come together over the course of the workshop. And creating “dishes” is the way that you’ll have individual team stakeholders construct personalized interactions that serve their needs or the needs of others.

The dishes will come from recipes, and those recipes have set ingredients.

Verify your ingredients

Like a good product manager, you’ll make sure—andyou’ll validate with the right stakeholders present—that you have all the ingredients on hand to cook up your desired interaction (or that you can work out what needs to be added to your pantry). These ingredients include the audience that you’re targeting, content and design elements, the context for the interaction, and your measure for how it’ll come together. 

This isn’t just about discovering requirements. Documenting your personalizations as a series of if-then statements lets the team: 

1. compare findings toward a unified approach for developing features, not unlike when artists paint with the same palette; 
2. specify a consistent set of interactions that users find uniform or familiar; 
3. and develop parity across performance measurements and key performance indicators too. 

This helps you streamline your designs and your technical efforts while you deliver a shared palette of core motifs of your personalized or automated experience.

Compose your recipe

What ingredients are important to you? Think of a who-what-when-why construct: 

• Who are your key audience segments or groups?
• What kind of content will you give them, in what design elements, and under what circumstances?
• And for which business and user benefits?

We first developed these cards and card categories five years ago. We regularly play-test their fit with conference audiences and clients. And we still encounter new possibilities. But they all follow an underlying who-what-when-why logic.

Here are three examples for a subscription-based reading app, which you can generally follow along with right to left in the cards in the accompanying photo below. 

1. Nurture personalization: When a guest or an unknown visitor interacts with  a product title, a banner or alert bar appears that makes it easier for them to encounter a related title they may want to read, saving them time.
2. Welcome automation: When there’s a newly registered user, an email is generated to call out the breadth of the content catalog and to make them a happier subscriber.
3. Winback automation: Before their subscription lapses or after a recent failed renewal, a user is sent an email that gives them a promotional offer to suggest that they reconsider renewing or to remind them to renew.

A useful preworkshop activity may be to think through a first draft of what these cards might be for your organization, although we’ve also found that this process sometimes flows best through cocreating the recipes themselves. Start with a set of blank cards, and begin labeling and grouping them through the design process, eventually distilling them to a refined subset of highly useful candidate cards.

You can think of the later stages of the workshop as moving from recipes toward a cookbook in focus—like a more nuanced customer-journey mapping. Individual “cooks” will pitch their recipes to the team, using a common jobs-to-be-done format so that measurability and results are baked in, and from there, the resulting collection will be prioritized for finished design and delivery to production.

Better kitchens require better architecture

Simplifying a customer experience is a complicated effort for those who are inside delivering it. Beware anyone who says otherwise. With that being said,  “Complicated problems can be hard to solve, but they are addressable with rules and recipes.”

When personalization becomes a laugh line, it’s because a team is overfitting: they aren’t designing with their best data. Like a sparse pantry, every organization has metadata debt to go along with its technical debt, and this creates a drag on personalization effectiveness. Your AI’s output quality, for example, is indeed limited by your IA. Spotify’s poster-child prowess today was unfathomable before they acquired a seemingly modest metadata startup that now powers its underlying information architecture.

You can definitely stand the heat…

Personalization technology opens a doorway into a confounding ocean of possible designs. Only a disciplined and highly collaborative approach will bring about the necessary focus and intention to succeed. So banish the dream kitchen. Instead, hit the test kitchen to save time, preserve job satisfaction and security, and safely dispense with the fanciful ideas that originate upstairs of the doers in your organization. There are meals to serve and mouths to feed.

This workshop framework gives you a fighting shot at lasting success as well as sound beginnings. Wiring up your information layer isn’t an overnight affair. But if you use the same cookbook and shared recipes, you’ll have solid footing for success. We designed these activities to make your organization’s needs concrete and clear, long before the hazards pile up.

While there are associated costs toward investing in this kind of technology and product design, your ability to size up and confront your unique situation and your digital capabilities is time well spent. Don’t squander it. The proof, as they say, is in the pudding.

[U.K.] : MRC II Distribution, [2023]

[U.S.A.] : MVD Visual, [2016]

[France] : Arte Editions, [2021]

[France] : Arte Editions, [2021]

[New York] : Criterion Collection, [2014]

[U.K.] : HBO Entertainment ; Warner Bros., [2023]

[U.K.] : Trinity film, [2012]

[U.K.] : World Productions, [2021]

[France] : Twentieth Century Fox Home Entertainment, [2004]

[U.K.] : Icon Film, [2010]

[U.K.] : Dogwoof, [2017]

[U.K.] : Universal, [2020]