Matchmaking emerging technologies and user needs
A four-step process for uncovering the most promising use cases for innovative technologies
In 2013, Google unveiled a groundbreaking wearable device called Glass. Sleek and lightweight, resembling a pair of eyeglasses, Glass had a heads-up display, internet connectivity, and the ability to capture photos and videos at any moment without the need to pull out a smartphone. Though it garnered a small and enthusiastic following among tech enthusiasts, Glass was quietly shelved a few years later after failing to find a broader market niche.
It’s a familiar story in the tech industry. Every few years, a new innovation emerges and captures the hype cycle, whether it’s wearable devices, virtual reality, blockchain and NFTs, or more recently, next-generation AI. Companies invest millions, hoping to capitalize on the excitement. But without a compelling use case, they risk developing “a solution in search of a problem” that flops in the marketplace.
Ideally, we develop our solutions based on a deep understanding of the people who will use the tool and their specific needs. But in practice, product teams are often given an initiative from higher-ups to build according to certain specifications. When this happens, how can UX teams incorporate user needs and help build something valuable to them?
This is where matchmaking, a structured framework for identifying and evaluating potential use cases for new technologies, comes into play. By working backwards from the technology to discover user needs that align, we can establish a basic compatibility as a starting point for building more promising future designs.
What is matchmaking?
Matchmaking was developed by two human-computer interaction (HCI) researchers, Sara Bly and Elizabeth Churchill, in 1999. They described it as a four-step process, which we’ve adapted below:
Identify the technology’s key capabilities or characteristics.
Map capabilities to the broad user activities they support.
Identify relevant potential work domains or contexts.
Evaluate the fit and feasibility of the matches.
When product development teams follow the traditional development lifecycle, any number of bread and butter UX research methods can be used to identify user needs. For instance, two common viable options include in-depth user interviews or early-stage prototype concept testing.
However, if the team already possesses a “solution” or technical capability in development and is unsure about the most valuable specific use case or circumstances, the matchmaking framework can be a valuable approach.
We’ll take a closer look at each step in that process as Bly and Churchill practiced it using the then-emerging technology of virtual environments. We’ll also describe a more recent adaptation of the process, as used by Yildirim and colleagues in 2023, to develop novel AI use-cases. We’ll compare the examples and describe how UX teams might apply each step in their own contexts.
Step 1: Identify the technology’s key capabilities
First, we must establish the scope of the matchmaking exercise by defining our technology and its key characteristics.
In 1999, virtual environments were still in their infancy. Bly and Churchill described the technology as simple, two-dimensional, and using a combination of graphics and text. These distinctions helped set it apart from similar technologies like instant messaging tools or virtual reality. By clearly articulating these specifics, we can constrain the boundaries for future steps in the matchmaking process.
Identifying what the technology can do may be done at the level most appropriate to your team’s goals. Drawing from their experience using and researching the technology within a single company, Bly and Churchill identified at a high level what they felt were the most important qualities of virtual environments of the time: continuous accessibility, computational efficiency, the ability for synchronous and asynchronous communications, among a few others.
On the other hand, Yildirim and team wanted to develop resources applicable across many AI-relevant contexts and companies. Consequently, they were more exhaustive. Their first step involved compiling a list of dozens of examples of AI-enabled features integrated into existing products across a wide variety domains.
More importantly, they wanted to look under the hood of each feature to understand its technical underpinnings. To achieve this, they thoroughly reviewed secondary sources, including research papers, news articles, press releases, product descriptions, and API documentation. Reviewing each example in greater detail, they were able to simplify the AI’s capabilities by focusing on the inference it made in relation to the available data. This process ultimately resulted in the table presented below, outlining AI’s broad capabilities (as of 2023).
As UX professionals, we’re seldom domain experts on the technical aspects of the products we support. More often, this knowledge sits with our colleagues on the product and engineering teams. At this stage, when we need to understand the technical capabilities of our solution, we may interview them or another subject-matter expert. Alternatively, or as a supplementary approach, we may be able to gather this information through secondary research.
Step 2: Map capabilities to the user activities they support
With a clear understanding of what our solution is and what it can do, we must align those capabilities with broad descriptions of user activities and requirements.
Having already reviewed a combination of existing primary and secondary research, Bly and Churchill were able to identify common scenarios where the specific capabilities they had found useful for users of virtual environments would be beneficial. For instance, having a continuously accessible tool proved helpful in ongoing work, while asynchronous communications proved useful when collaborators were separated by time.
Yildirim and colleagues wanted to instead identify opportunities for brand-new AI-powered applications, so existing research would be of limited help. Instead, they facilitated workshops where they helped different teams of specialists ideate using different lenses. One key approach provided participants with an example-rich version of the AI-features table above, so that they could build ideas on the basis of what the technology is well-suited to do. In contrast to the typical workshop ideation approach which primarily considers personas and user journeys, this feature-led approach kept things focused on viability from the start.
In these examples, the teams of researchers used different approaches to identify potential areas where the technology’s capabilities might benefit users. For many technologies, some combination of primary and secondary research will help UX teams to form those connections, as in the case of Bly and Churchill working on virtual environments. But for wide-open problem spaces with little existing knowledge, such as novel AI use cases, facilitated exercises with other product team members may be a more effective starting point.
Step 3: Identify relevant potential work domains
With a broad list of user activities that the capabilities might support, it’s time to narrow to specific contexts, domains, or user groups, where those activities are common.
Bly and Churchill, who were, at the time of writing, a consultant and a researcher at Xerox PARC, respectively, were supporting a large organization with a diverse range of working streams. Further narrowing down the focus was key. Having some knowledge of the organization, they identified a few promising examples of teams where the activities described were frequent, and the capabilities identified might be put to productive use.
Each team was trying to coordinate work across dispersed members using a variety of communication methods, both new and traditional. These included a math and computer science research team, a sales and consulting team, a magnetic fusion science lab, and a team of middle-management in software development. We’ll revisit these teams in the next step.
In contrast, Yildirim and their colleagues had already limited ideation to domains where their colleagues had prior experience. For instance, they conducted one facilitated ideation workshop with a group of HCI specialists. They asked these specialists to consider familiar applications, such as ride-sharing services like Lyft and Uber, and vacation rental services like Airbnb and Vrbo. In another session, they collaborated with a group of data scientists and nurses to develop ideas for applications in intensive care units.
Depending on your context, you may need to adapt this step in the process as well. For instance, strategic UX teams working across a large variety of workstreams in a mature organization may have the flexibility to narrow down to a few promising groups, as did Bly and Churchill. However, if you’re working on a smaller team or within a more narrowly defined space, you may need to constrain the process from an earlier stage, as did Yildirim and colleagues.
Regardless, by the end of this step, you’ll have a sense of where the solution under development might be more valuable for specific populations.
Step 4: Evaluate the fit of the matches
In this final step, we aim to evaluate how well the specific domain’s activities and requirements align with the capabilities previously described.
Having identified four potential teams as contenders for whom virtual environments might be useful, Bly and Churchill conducted interviews and observations with each of these teams to understand their needs across the identified user activities. They summarized the match between their activities and the capabilities in a table, partially shown below.
Two of the four teams were large and involved in ongoing scientific research with frequent, lightweight interactions. They were selected as the most promising potential users, and Bly and Churchill focused their subsequent iterative design efforts with these teams.
After their ideation sessions, Yildirim and their colleagues used two different collaborative methods to evaluate the potential of their proposed use cases as a working group. The first method, known as the impact-effort matrix, is a widely used prioritization tool that plots the impact of a solution against the effort required to develop it. However, they also developed a unique method tailored to the challenges they identified in developing viable AI-based solutions. This method involves plotting the level of expertise required for a human to accomplish a task against the level of AI performance on that task.
The ultimate goal of this step is to identify the most promising and feasible match between users in a specific context and the technical solution. Depending on your technology and context, you may rely on conventional UX research methods such as interviews and observations, or you may facilitate collective judgments using tools like the impact/effort matrix or something more tailored to your specific needs.
From this point forward, the match has been made! Your team can focus continued design and research efforts in the areas identified.
The bottom line
When product teams build from a technology-first rather than a user-first foundation, they risk developing solutions in search of a problem. Nevertheless, hope is not lost: matchmaking, a structured framework developed and practiced by HCI researchers for over 20 years, offers a way to work backwards from cutting-edge solutions to those users whose needs align most closely.
By following its four steps—identifying key capabilities, mapping them to user activities, narrowing down to relevant domains, and evaluating the fit—UX professionals can help their partners focus efforts where they are most likely to succeed.
Drill deeper
Has your team been tasked with finding a compelling use case for a specific capability your company wants to bring to the market? Matchmaking may be a helpful approach.
We’ve taken a look at the broad considerations and a few real-world applications, but when it comes to your context, the devil’s in the details. The research experts at Drill Bit Labs, a leading UX and digital strategy firm, are here to help.
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