10 lessons about how to improve tech & your success as a researcher, designer, product manager

Arpy Dragffy
5 min readFeb 9, 2023

2022 was a weird one. They’re the best because that’s when you see change happen in real-time and can imagine where the future is headed. Here are ten lessons from our work researching & solving product problems for many of world’s the largest corporations. If you have any questions, contact me arpy@ph1.ca

  1. UX research is more important than ever but also at a crossroads
  2. The biggest winners of the pandemic: creators
  3. The most powerful force to harness: relevance
  4. Many products score poorly on task assistance
  5. As products try to make the present more profitable, thinking about the future becomes more important than ever
  6. A huge opportunity to improve the future of products is to re-imagine analytics
  7. Some of the biggest ROI research questions today are how to overcome gatekeepers
  8. UX still suffers from metro-centric bias
  9. Blockchain and Web3 have a major UX problem and needed to crash
  10. AI will destroy many people’s careers and create entire new industries.

10 lessons from 2022

UX research is more important than ever but also at a crossroads. On one side, COVID put a fire on product teams to deliver new features and services. That newly-accelerated pace led to many new UXR headcount and also to projects moving much quicker than they should be. Steps were skipped in favor of delivery. Now that tech’s valuations have burst the very UXR that are critical to fixing these products are getting cut. UXR doesn’t yet have the seat at the table that it should and 2023 will be need to be a time for reinvention as UXR needs to prove its value during tighter times.

The biggest winners of the pandemic: creators. Having smart, passionate people sit at home bored resulted in mind-blowing creations. Creators are emperors of their own kingdoms because there are so many new tools to make them less reliant on others. And it isn’t just the podcasters and streamers, this includes the coders, musicians, artists, and much more will disrupt long-standing industries from within. An example is that you’ll see a growing proportion of musicians with no ambition of touring at all, who know how to ride the waves of algorithmic playlists, and use generative principles that flip the traditional music industry upside down.

The most powerful force to harness: relevance. The most consistent finding of all our projects has been that poor relevance is the reason your features/tools are under-utilized. They rarely read important content and often don’t understand your differentiators because neither seem relevant to how they think. Relevance may sound like the table-stakes of marketing and human-centred design, yet almost every org we’ve worked with gets it wrong because they’ve been guessing what people want. The rise of personalization has led to a false idol preaching easy answers. Personalization will fail unless you understand the end-to-end decision making process of different types of users, plus the types of content/solutions that would best address them. A tool won’t solve this, a culture of collecting insights will.

Many products score poorly on task assistance. Good products empower users by solving tasks that improve their lives. One product may be superior than another but another’s ability to assist users on the journey of understanding their problems, explain what solutions are available, and simply outline how to complete those tasks can be the game changer. This is why products that focus on doing one thing well tend to be beloved, while mega-apps that try and solve everything getting mixed reactions. So often products are designed for mature users, ones familiar with the tools and features. Meanwhile, the onboarding, education, and error assistance is where true LTV growth happens. These flows need to be examined more so as service design exercises.

As products try to make the present more profitable, thinking about the future becomes more important than ever. Somewhere along the way UXR has become a skill of optimizing the present, not of examining the possibilities of the future. The later enables products teams to appropriately plan for the changing needs of audiences. Thankfully futures thinking and forecasting has become its own domain bringing expertise to prepare teams for scenarios 5, 10 or more years into the future. With how tumultuous 2022 has been, research teams need to integrate this practice to stay ahead of disruptions.

A huge opportunity to improve the future of products is to re-imagine analytics. Part of futures thinking is to imagine new possibilities. One such opportunity is to reimagine analytics. A longstanding assumption has been that providing users with more data empowers them. Our research shows the opposite, analytical surfaces primarily benefit high data maturity users. Data intimidates most and can drive users to take the wrong actions because they don’t understand what the data means. In 2023 and beyond, I want to challenge orgs to make data more comprehensible and to minimize the use to traditional analytical surfaces: charts, tables, graphs. The average users need to know what matters and how to act on it, not have to decipher data sets.

Some of the biggest ROI research questions today are about how to overcome gatekeepers. Our projects often are about finding ways to solve for externalities, like Apple’s downward pressure on conversion rates and market cannibalization by 3rd party apps built on top of APIs. These are the problems that matter most to the bottom line and they require scanning how others are tackling these issues, changing landscapes impacting these issues, and testing a range of iterative solutions. Qual needs to be mixed with quant or else you won’t have a high confidence in your recommended solution(s).

UX still suffers from metro-centric bias. Even though it’s easier than ever to research with representative audiences, products and services aren’t considering the 95% of the world that doesn’t live in the United States’ coastal cities. This bias opens opportunities to challengers who want to focus on the growing population of elderly and low-to-mid technology-maturity audiences. And the bias is a systemic one too centred around North America and our mental models. Having spend years travelling recently, it makes complete sense why many products and services face barriers to growth on many continents.

Blockchain and Web3 have a major UX problem and needed to crash. I lost a lot in the crash, as did many others. I worked on many dozens of Web3 projects and believed that the tech could remove the gatekeepers that limit innovation and extort creators. But the reality is the scene needed to burst because it was flooded with bad money and bad actors. The scorched earth of blockchain valuations reset the priorities of builders and forced them to demonstrably solve important problems. The rebirth also is an opportunity to address the many UX problems that Web3 faced in attracting new users.

AI will destroy many people’s careers and create entire new industries. ChatGPT and Dall-E are solutions to problems we didn’t realize we had. It’s the classic technocratic model: build new models without understanding the consequences. I’ve spoken with designers who believe that these technologies ethically undermine the livelihood of artists by processing their work and stealing it at scale. Others question the ethics of deploying technology that will add master the creation of disinformation. The future is coming and these two problems require an entirely new specialization of legal services, authentication services, and brokers. It will lead to the further commodification of entry level skills —content, design, coding, planning— to such a degree that pretty soon there will be two tracks of job seekers: those that feed machines inputs & those that can create outputs without machines.



Arpy Dragffy

Customer Experience & Service Design | Head of Strategy of http://PH1.ca