Author: admin

An overview of high level strategy, with concepts of operation for illustration.

First Principles: Why do we need user research?

Without constant buyer and user feedback how would companies dial in and course correct product?  Researchers help answer the endless stream of questions around what to make and how to build. Great researchers translate learning to words and ideas anyone in a company grasps. Great researchers answer questions with data when decisions are made and not 3 weeks later. Great researchers can anticipate helpful inputs and work independently to answer questions not yet asked. When teams don’t have answers or don’t seek them out, they fly blind.

Scope: where we play. 

We answer questions involving pharma customer users and buyers on behalf of anyone on a product development team (CFT) or senior leadership, with primary accountability to product management. We deliver first insights to most questions within a day and build confidence over 1-2 weeks.

Okay, break down why that matters?  

Speed is key: History tells us that many successful companies first succeed because they are able to experiment, build, and refine more efficiently (and test more ideas) than competitors. Companies rarely get the right answer the first time.

Keep customers and buyers close. Know the difference: Successful teams frequently and opportunistically reach out to customers and develop proxies (e.g. academic scientists) to speed up work. Anyone, Product Managers, Designers, Engineers, could participate in research: Attending calls, putting ideas to customers or proxies, independently learning. No priesthood sits between the team and customers acting as a sole source of contact and interpretation. We believe teams work best, align well, and stay focused when each member hears directly from customers.  

Be cash aware, but not miserly:  BenchSci is not yet cash flow positive, nor is it entrenched. We run on a time clock. We earn seconds on days where deals sign, funding arrives. Market challenges cost us seconds. Because this clock never stops, we always need to consider the opportunity cost of our operational decisions. Have all the people you need and no more. Find a good compromise between speed and precision. If in doubt, go cheap and fast. 

So how do we enable agile-speed answers …with Pharma?

What does the customer landscape look like? Pharma scientists as a group are very distinct from consumer software targets like tax prep: 

• Not easy to recruit thru un-moderated testing pools (we tried)
• Show data they know in prototypes. Scientists respond very differently to data they know or techniques they work with frequently. 
• Expensive to recruit outside of customers. Engaging a research recruiting agency costs ~$1000 per hour, with a 2-4 week recruiting time, and limited ability to record
• Favors, no comp. Customer scientists cannot be paid in any way for feedback – they volunteer out of good will or self-interest
• Busy Scientists are busy, well paid, professionals: there are many calls on their time and many companies looking to sell or collect feedback. Setting up calls can take a week or two at best with scientists we have an established relationship with.
• Secrecy: Drugs take a long time to reach the market, with significant first to market advantage. Research takes a while but is easy to ‘steal.’ So Pharmas build walls of access and protect their data. 
• Difficult access: Onsite visits, particularly to facilities with manufacturing and research often require pre clearance and elaborate preparation, with limited ability to explore. Labs are often off limits, both due to secrecy and the polarizing nature of experiments which may use live animals.

Other qualities of our offering affect research: 

• Low organic usage per user (1-2 active monthly sessions for engaged users. A typical  session may span many looker sessions) 
• Low total numbers of very high value users- ‘lead scientists’ are equivalent to engineering or product directors in a company like ours. When we are successful we may have thousands of users: a company like Facebook has billions. That changes how we can learn.
• Flows are exploratory, not linear or narrative. Unlike shopping checkout, business forms, or other linear flows, scientists use us more as a non-linear workspace. This makes it harder to learn with high polish, mostly linear, limited interactivity, slide-like prototypes of the kind made by Figma.

How does the landscape shape our response? (our strategy for excelling in this environment) 

Solving for: low numbers of high value, difficult to access, unpaid customer participants.

• Pre-recruited proxy users – fast access, allows us to answer select questions with good correlation to ideal participants. Informally we call our set of industry scientists and academics our “academic pool,” and we compensate so we get reliable participation. 
• Make the most of time: focus on techniques which extract the richest data per session and make participants feel value so they come back(ex. fewer but high quality moderated sessions which make use of many sub techniques as opposed to many surveys with low response)
• No touch deep observation (aka FullStory, Looker). 
• Rely on available artifacts. We might not go to a lab, but we can see what story the slides, figures, sheets, and other artifacts tell about a process. For example, governance reports shed great insight into how customers value and contextualize different scientific information.

Solving for: Infrequent usage 

• Separate training from real sessions – cross software data correlation (SFDC training dates, users, looker data.)
• Extend use to academia to scale user numbers – i.e. we get more users we can learn from
• Automated question capture – we capture when someone puts a question to XXX, inputs a structured query to YYY or ZZZ, or any future input in a yet to be developed product. We then passively track their response to our output. 
• See every session – ex. fullStory segmenting

Solving for: Scientists respond differently to data they know

• Ensure scientific accuracy of research materials (low fidelity, high fidelity, engineered prototypes, cards for sort etc…)
• Where possible, match the materials to the participants background – e.x. Show publication examples relevant to the scientist’s specialty
• In prototypes, favor data accuracy and interaction over visual fidelity. I.e. A spreadsheet or informal engineering prototype may be richer for us than a refined figma. Vibe coding tools such as vercel can help in specific cases.

Solving for: Keeping up with engineering speed (agile) 

• Decentralized / High Autonomy — Dedicate a researcher to each team that matters. Give them full authority to do the work. Emphasize fast, informal, flexible working where insight for a question happens in a day not weeks. Emphasize direct PM/Engineering contact with users to pre-build empathy. These are hallmarks of a research operations concept called continuous discovery/validation.
• Keep cost per insight or session as low as possible- Lower cost means more sessions
• Focus team core on enabling, coaching,  and unblocking. For example: securing budget and executive buy-in, coordination of initiatives with go-to-market(GTM), coaching and teaching. 
• Reduce long lead/ tail items on research through automation or process reduction. E.x. recruiting and de-emphasized write-up.

Context –

Until recently, I ran user research benchSci, and mentored designers and product managers. We put heavy emphasis on effective decision support for product strategy (building the right things) in addition to the usual kinds of design/PM support. We did not do market/communications research. I’ll try to limit this to user research and not general productivity. Doctrinally we followed the principles of continuous discovery / validation with a small user base. 

Rules of the road for the Research team

AI helps if it accelerates some balance of speed or quality while respecting the rules.

• If you bring data to stakeholders too late, it’s not helpful.
• If the data you bring is misleading, its not helpful.
• numbers speak powerfully, but avoid misleading quantification.
• avoid jargon- always assume you speak to a design/product outsider with no interest in the finer points of CIs and statistical significance.
• stakeholders only tend to check work when the conclusions are inconvenient and trust it when they agree with it. Have your homework ready.

We Explored AI for

• rapid prototyping
• synthetic users
• data analysis
• automating review/extraction from recorded calls
• LLM as judge for design evaluation
• writing for UX
• general writing
• perf reviews
• making historical research accessible.

Not Explored

We did not explore uses where we already had reliable and effective automation or absolutely required human oversight (recruiting /compensation pipeline)

Where LLMs really helped: 

1. Writing for UX. Designers are often tasked with key bits of text in the UI. As a group, designers do not write well. This was a big win for new products/features.
2. For us, 1 in 10 studies demanded low visual fidelity, highly interactive prototypes, with domain accurate content. Figma sucks at that, particularly tabular data and data visualizations. Vibe coding really helped that. This benefited new / early ideas more than shipped features in refinement/optimization. When it made the difference, it really made a difference.
3. Spreadsheet like a pro. 
   There was always some project that required fusing different data, even for things like survey data fused to SFDC outputs. Advanced spreadsheet work is a kind of coding. The research team can do a little stats, we’re perceptive observers, and good interviewers. Not everyone on team can own spreadsheets like a financial analyst. This helped a lot with a small number of critical projects.
4. Tone on general comms- 
   Used ‘LLM as judge’ on communications. For critical written company communications and things like performance reviews, LLMs definitely helped avoid gotchas— they didn’t improve speed or productivity but made sure poorly chosen words didn’t trigger morale or cause defensiveness. 
5. Making historical research accessible 
   We tried a few approaches to this, starting with adding research reports to a notebookLM. In the end we found more consistent results by translating research reports to markdown and bullet pointing them for LLM. Getting the organization to go and ask was an institutional barrier we worked to overcome: PMs still preferred to use the researcher on team as a human encyclopedia in meetings. Senior leaders, when they chose to ask approached me directly.

Shows some promise, but not there for us

While applying just a foundation model might not work… there is much to try with hybrid approaches.

1. Synthetic users for ‘attitudinal/behavioral’ questions
    (i.e. ‘what are your biggest challenges with ‘x’ )Again, this is a mixed bag depending on how much underlying high-quality data we had. It felt like we never got to critical mass – it works great when say a newly hired PM or designer asks an ‘old’ question where established data exists.  We used a pseudo-RAG approach (.md files accessed via cursor prompt against different models) It wasn’t as good as our control (‘ask a researcher thru slack’) in terms of quality, but response time was better.
2. Getting insight from interview transcripts. Ideally automated insight.
   This is another one we really want to work, especially in an automated way. We had a lot of recorded conversations with customers well beyond research sessions. Both the services team and ours would love to get data from them without the time cost of manual review. So, we wrote some custom prompts as well as using dovetail’s evolving toolset. We saw improvement, but not enough maturity to save time: Commonly extracted data confused speakers (customer vs us), generalized away details, and depended on high quality transcripts. Bio research is a jargon heavy, with researchers from all over the world who have different command of English. It wouldn’t save us enough time while delivering accurate insight. This wasn’t strictly an LLM problem, it’s a data quality problem.

Unhelpful with state of art in 2025

We saw a good chance outputs may drive confidently bad decisions. Not confident that base foundational models will focus on these – it may require custom / external evaluators.

1. LLM as heuristic judge for basic usability issues. 
   We really wanted this to work. This is a subcategory for synthetic users. In early designs you often find a lot of issues with ‘basics,’ well established heuristics.  We tried a mix of custom prompts around this, submitting single screens. The hope was to increase speed for designs in a similar way to unmoderated testing but with somewhat better precision and a lot more efficiency. The LLM tended to miss on visual and behavioral, focusing on words in the screens or screen images.
     
2. General writing of reports for humans. 
   Thru trial and error we found the most effective reports for our company tend to be highly visual with very few words (also memes), accompanied by a short slack summary.  LLMs are great writers, but… summaries tended to miss the points we were trying to make. It’s very likely that newer tools for presentation creation will help here.
3. Study design -. In general, it didn’t save time or improve quality
4. Agentic Interface Design –  Depends on the end goal: If you seek to save hours on Figma production it may be possible with future models that encode and ‘speak’ interaction, cognitive psychology, and visual concepts and not just memorized markup. Compared to 20 years ago a lot of design today is not that experimental. Tools like magic patterns do a pretty good job of pulling from what’s out there and offer a nice way to use nondeterminism to generate variations. That can save time and help communication between non-designers and their teams.
5. Generating personas or foundational research with just a foundation model search.
   When asking foundational models, we got surprisingly accurate reads on how preclinical pharmaceutical research works at a high level. When asking models to create personas, the form was amazing, but the data was only between 50% and 85% accurate when compared to our data. I would have big concerns that PMs and Designers using this data without secondary checks would make consistently bad decisions with confidence.

One final thought: Don’t let AI isolate you from customers.

Anytime you put a layer between PMs and users is a fail. Face to face interviews drive alignment far more than any research report. In my opinion listening to and observing real users and customers directly and frequently makes the biggest difference in product quality and fit. The more isolated PMs, execs, and designers are from customers, the worse their decisions become. Many AI tools for research position themselves as a timesaver in automating every thing from study design to final reports. They typically position to the haerried PM or the research manager.

As it stands execs don’t often interact with customers. Bad PMs certainly don’t.

Apologies for the long rant, 
Jeremy