Signum is restructuring its portfolio of life science products and pursuing new ones.
Before committing resources to building, the business needs to know whether an opportunity is real, what the potential solutions are, and how they fit into customers’ existing workflows.
For a legacy product, business needs to know whether it still meets the market, and what updating it would take.
- Stakeholder and SME workshops to surface the assumptions behind each opportunity, as well as to support engagement and ownership
- Data collection and analysis: 5–8 customer interviews per project, usage data, business data
- Synthesis of findings into a rich report, excellent to onboard any employee to a subject matter area
- Development of concepts and functional prototypes for the discovered opportunities
- Cross-functional work throughout the project with data science, sales, product and subject matter experts
- A new product direction for forecasting features. Turned out, our customers already heavily relied on a kind of forecasting that the team had dismissed from the start. My findings reset the scope of the forecasting features and surfaced four separate product opportunities, two of them unrelated to forecasting.
- A no-go at the gate, before build. A market dynamic we uncovered capped how much a campaign evaluation tool could affect customers’ business, so we concluded they would be unlikely to pay for it.
- An interface turned inside out. The Nordic patient data product had been architected around the shape of its datasets. I mapped the usage patterns cutting across that structure and re-architected the UI around them.
- A platform-level signal. Across projects, customers were consistently working around the platform rather than with it. The reason was that the tools gave them too little autonomy. This evidence is a tough nut to crack for the business, but it does inform foundational platform decisions, well beyond product prioritisation.
Business problem
Signum builds sales and patient data analytics for the life science industry. Its customers are mainly commercial teams at pharma companies (brand managers, sales analysts, business insights leads) who use Signum's products to understand market performance and support business decisions.
Signum regularly identifies potential product opportunities (new products, solutions, features) that could expand the value that the company delivers to its life science customers. I led end-to-end research and concept development to validate product opportunities before the company invests in building. Each project started with a hypothesis about a customer need and ended with a reframed or validated product direction, specific opportunity definitions, and product concepts.
Each study typically involved 5–8 customer interviews and was done in cross-functional collaboration with data science, sales, product and SMEs.
Examples in this composite case study are drawn from three projects:
Project A: Sales performance forecasting functionality
Project B: Marketing campaign evaluation tool
Project C: Nordic patient data product
Forming the hypotheses and research questions
Before I start interviewing, I run stakeholder and SME workshops in order to collect any relevant knowledge and articulate the assumptions behind the opportunity. We answer questions like:
- What are the customer types we think this is beneficial for?
- What are their presumed needs?
- What are the tasks that this product may support?
- What are the already known pain points?
This gathered knowledge directly shapes the research plan and gives it a clear purpose.
The workshops also serve an important alignment purpose, reinforced by the User Research Canvas artefact. I find that teams that have worked through their hypotheses together are better positioned to act on what the research finds.
User research canvas
Based on explorative stakeholder engagement, my preliminary research of the market, the available business data and usage data, I create a user research canvas of my own design.
User research canvas template of my own design
Its main feature is clarity of content and scope. I make sure that research objectives are aligned with the strategic business goals. This really pays off during reporting and ongoing alignment.
This artefact is the pillar of my research process from start to finish.
Designing for discovery
My interview guide covers the specific hypotheses relevant to a project at hand.
However I design it to go further into the surrounding workflow and organisational context:
- the broader context use of Signum's products and platforms, incl. the technical and organisational ecosystems of our customers;
- usage patterns, frictions, unmet needs;
- customer segments in terms of behaviors, goals, needs;
- what kind of decisions can the data support.
I combine strategic and tactical research objectives, validation of hypotheses and open-ended exploration.
Validated or reframed product direction
The most direct impact of this type of work is giving the business a systematic and rigorous base of evidence for a product decision.
A recurring pattern across these projects is that the research reframes the opportunity in ways the team didn't anticipate.
Project A: Sales performance forecasting functionality
The team's hypothesis was that customers needed a specific type of forecasting solution. We wanted to confirm that, and learn more about when and how they use forecasting.
Turned out, our customers had a significantly different understanding of what type of forecasting brings value. They already systematically employ a different kind of forecasting that our team dismissed from the start.
Those findings set a new scope for this product direction.
A generated concept sketch in place of product concepts which are under NDA
Project B: Marketing campaign evaluation tool
We went to investigate whether the performance of a specific type of campaigns can be meaningfully assessed, given the constraints of available data. And if so, what features would our customers need from a campaign evaluation tool?
We uncovered a market dynamic that drives this specific type of campaigns that we were exploring. This dynamic seriously limits the potential impact of the tool on our customers' business.
Those findings led to a no-go decision. We concluded that customers would be unlikely to pay for a product whose business impact was inherently limited.
Defined opportunity space
Each project identified multiple opportunities of varying scope and risk.
It is often a solution in the area that was not central to our exploration.
Project A: Sales performance forecasting functionality
I identified four separate product opportunities, only two of them related to forecasting.
The other two opportunities stemmed from other needs of the same customer segment. For example, I discovered an unmet need for a specific monthly reporting solution, which our company already had all building blocks for.
Broader product, platform and customer segment insights
My research explores a broad practical, organisational, behavioral, psychological context in which our products are used.
By design of my research, I consistently come to strategically significant peripheral findings.
Across multiple projects my research surfaced platform-level frictions and a recurring pattern of customers working around the platform rather than with it, because the tools didn't give them sufficient autonomy. This is a strong signal that carries methodological weight, and that's what allows the business to act on it with more confidence. It can inform decisions about platform development at a foundational level — well beyond product prioritization.
Interactive prototypes built with AI
I build prototypes of the concepts for the opportunities that come out of my research. This turns an abstract opportunity into something stakeholders can interact with, and helps scope the business case.
Project C: Nordic patient data product
The interface of this existing product had been architected around the structure of the datasets, rather than around the usage patterns.
I mapped the usage patterns that cut across that data structure, and turned the existing interface architecture inside out.
I also identified an opportunity to pull in additional data the company already had, to enrich the new dimensions rooted in usage patterns.
A fictional example to illustrate a hierarchy flip in the UI to move away from data structure towards usage patterns