ep. 18: Analogous inspiration as a tool to navigate emerging product design
8 min read
As a design researcher focused on emerging technology, my toolkit includes a range of approaches that can help product teams navigate ambiguity and anticipate potential futures. Today, I’ll discuss a design research method called analogous inspiration, which I often use to help teams develop new lenses through which they can view their problem and solution space. I’ll share examples and a practical guide for how to start applying analogous inspiration to navigate emerging product design.
What is analogous inspiration?
Analogous inspiration, aka “analogous research”, is a method for developing new lenses for viewing your problem and solution space, accomplished by looking outside your context (e.g., industry, environment, population) for inspiration in how others have tackled similar challenges. While the method of analogous inspiration as applied to product design was cultivated by IDEO, the general approach has been around for centuries.
In the Renaissance, Leonardo da Vinci invented a flying machine that mimicked his observations of the movements of birds and bats. Notably, he observed soaring birds in order to learn how they fly without flapping their wings, and was the first to document flight maneuvers we now call dynamic soaring. Fast forwarding to 1941, George de Mestral, a Swiss engineer, noticed the mechanism by which burrs attached to his dog's fur while walking in the Alps. Studying their mode of attachment inspired him to create Velcro. This practice of analyzing nature’s models, systems and elements for human use is called biomimicry.
Putting this in product development terms, da Vinci and de Mestral both found inspiration outside their industry that informed their product design. A more recent example from IDEO’s playbook is the use of analogous inspiration to redesign a patient surgical experience. The hospital team’s goal was to disrupt its industry by creating a service that focused not just on clinical outcomes but on the end-to-end patient experience. To do this, they studied airline travel. While hospitals and airports may seem like disparate contexts, they have several commonalities: both are highly regulated industries, involve many moving parts and coordination, and seek to deliver a customer-centric experience while getting a person from point A to B.
Coming onsite to study how an airport operates, the surgical team developed a new, shared language about their problem and solution space, seeing their design challenge through new lenses. For example, surgeons quickly noticed there were many different pathways for airline passengers (e.g., options for families or frequent fliers), in contrast to one option for patients. Inspired by insights from their observations at the airport, the team went on to map out several prototypes to test at the hospital, including a redesigned lobby experience, and a way to engage with patients with different levels of confidence and familiarity with the hospital system.
Analogous inspiration applied to emerging technology
As we just observed, analogous inspiration can be applied to any product design question. However, it can be particularly useful to learn from the analog of an environment or a population that is unreachable - or may not yet exist. In a previous Sendfull episode, I shared an example: “imagine your team wants to build a resort on Mars. Where might you go? Who are your target customers whom you’d want to learn from?
While traveling to Mars is probably infeasible, you could research existing resorts built in extreme conditions, such as an “undersea residence” or an ice hotel. You could read up on people who put themselves in extreme conditions, for lack of a better word, “fun” - analogs to potential future Mars resort-goers. These folks could include people who attend Burning Man, people who race yachts around the world, or Marathon des Sables runners, who partake in a six-day, 250 km run through the desert. Their goals and motivations can give you a glimpse into who might someday want to vacation on Mars, and consequently, how to build for their needs.”
I went on to apply analogous inspiration with the goal of developing spatial computing design principles. I sought to explore a scenario in which people effectively tackled blending digital objects with the physical world. My analog was aviation display design, where people have been designing heads-up displays (HUDs) and head-mounted displays (HMDs) for over 50 years. I then leveraged aviation human factors research to generate design principles to help reduce cognitive load and situational awareness for people using consumer spatial computing headsets.
For instance, aviation HUD design (left) commonly follows the proximity compatibility principle, where similar information, in the form of digital design elements overlaid on the environment, is grouped closer together to minimize cognitive load and maximize situation awareness. We can see this at play in a different context on the Apple Vision Pro default home screen (right), where communication apps, like Mail and Messages, are grouped closer together. Similarly, “functional” apps, like Settings at the App Store, are grouped closer together.
By studying the analog of aviation display design, I developed new lenses through which I could view consumer spatial computing interface problem space (i.e., minimizing cognitive load and maximizing situation awareness for the user) and solution space (i.e., relevant design patterns). I then applied these patterns to develop consumer spatial computing design principles.
Applying analogous inspiration
The process of applying analogous inspiration can be broken into five steps:
Choose a goal to focus on. In the IDEO hospital redesign example, the team’s goal was to disrupt its industry by creating a service that focused on the end-to-end patient experience, rather than only clinical outcomes. Other examples from my own practice include: democratizing augmented reality (AR) creation, and making a more engaging, useful AI assistant.
Identify the specific problem you’re looking to solve. In the hospital example, this was improving the end-to-end hospital user experience. In the AR example, this was reducing barriers to AR creation. For the AI assistant, this was making AI-human conversation more naturalistic.
Find examples of that problem being solved in other contexts. For IDEO, this was the airport. For AR creation, this was studying how people used software that helped people create experiences without coding (e.g., code-free website building tools like Squarespace, visual scripting tools like Unreal’s Blueprint).
Note, if we frame the problem differently in the previous step, you might focus on a different analog. For instance, if a key use case you’re exploring is helping people previsualize physical designs before they’re manufactured, you could learn from people who regularly do this, such as environment designers or architects.
For the AI assistant, this was distilling key insights from information theory and psychology research on human conversation, to help define elements of an engaging conversation.Research: Choose an analog to study, and synthesize insights about the new lenses you’ve gained. If you have several analogs, prioritize the one that is most aligned with your goal and problem space. Your learning goals will determine what method you will use (e.g., contextual inquiry, interviews, secondary research) to study your problem space in the analog context.
Have your whole team participate (not just one person), to learn a new shared language with which you talk about problem space and potential solutions. Together, synthesize key insights about your new lenses and opportunity areas.Ideate. Generate a range of design solutions inspired by your new learnings. For example, you could use the ideation prompt, “How might we help people enhance a space’s overall usability and user experience using AR?”, inspired by learnings from environment designers and architects.
Keep in mind that the population you study for your analog is likely not your target audience. For example, in the IDEO example, airline passengers weren’t the end user, but could provide helpful insights to inform design for the actual end users - hospital patients. It’s also important that the team learns this new language together (e.g., if going in-field to observe the analogous context), rather than just having one person study the analog and relay the insight.
For more analogous inspiration “how-to”s, check out IDEO’s activity toolkit or designer Katie Shelly’s how-to video guide.
Takeaways
Applying analogous inspiration to emerging technology development can help teams develop a new language to define an ambiguous problem space, and generate new ideas for solutions. You can do this by identifying a goal and the problem you’re trying to solve, finding examples of that problem being solved in other contexts, choosing an analog to learn from, and ideating on solutions for your application, based on your new insights.
Human-Computer Interaction News
Oppo’s new prototype AR glasses with AI voice assistant: In another example of the increasing XR and AI overlap, Oppo launched the Air Glass 3, a prototype set of AR glasses with a voice assistant. The glasses are equipped with a voice assistant which is powered by AndesGPT, Oppo’s LLM. The Air Glass 3 needs to be tethered to an Oppo smartphone. Users can control the glasses with touch sensors on the side of the frame.
Cross-platform spatial game Cardboard Crashers released by MousePack: This multiplayer game works cross-platform with Meta’s Quest headset and mobile, where players on both platforms experience collisions simultaneously using the browser. Given that many people are likely to only have one mixed reality headset per household for the near future, this type of experience helps increase headset engagement by allowing the user to play with others on their existing devices, and removes a barrier to entry for those without a headset.
A survey of LLMs: This paper reviews some of the most prominent LLMs, including three popular LLM families (GPT, LLaMA, PaLM), and discusses their characteristics, contributions and limitations. This includes a survey of popular datasets prepared for LLM training, fine-tuning, and evaluation, and a review of widely used LLM evaluation metrics.
Want to chat about how to apply analogous inspiration to product design on your team? Reach out at hello@sendfull.com
That’s a wrap 🌯 . More human-computer interaction news from Sendfull next week.