Generative and Expansive Research: Which one should you use?
When thinking about how to design a user research strategy, it’s important to understand the difference between generative and expansive research, and how each is well-suited to address very different research objectives. So let’s take a look at exactly when each should be used.
But first… what is the difference between Generative and Exploratory research?
Generative Research: Identifies key opportunities and user needs, laying the groundwork for strategic direction. I liken it to the roots and trunk of a tree, providing a strong foundation while branching out to explore new avenues for growth. This approach is especially valuable for 'blue sky' research in uncharted areas or for developing entirely new products.
Exploratory Research: Improves existing ideas so that they are relevant and appropriate for your users. Think of this as the higher branches of the tree, where ideas are shaped, trimmed and optimized.
When to Use Generative Research
Generative research is most effective in the early stages of a product – to discover unmet user needs and behavioral patterns. It's useful for: New product or feature launches. You want to start off on the right foot with solid formative research to identify pain points and motivations. We used this approach in part in the Agency Dashboard research project I headed up. We were looking to understand issues that this new (to us)audience segment faces.
Generative research encourages designers and product owners to gain insight into their audience, often through a multitude of open-ended inquiries, so that design decisions are sensitive and responsive to users’ actual lives.
Some examples of generative research methodologies
User Interviews: you can investigate users’ motivations, learn about critical behaviors and frustration points; qualitative detail is crucial in designing with a specific user in mind
Field Studies: Observing users in their natural environments can reveal behaviors and preferences that might not emerge in a controlled testing setting. This approach offers design teams invaluable insights during the early stages of product development, uncovering unmet needs that drive meaningful innovation.
Diary Studies: Users log experiences over time, tracking emerging patterns and points of pain. Especially useful for products that operate in daily routines.
When to Use Expansive Research
Expansive research, meanwhile, provides:
A broader view. Often focusing on future trends. Market opportunities. New areas for growth.
This approach is valuable when:
Looking further afield: If the research aim is to uncover new markets or trends, broad-based studies examine shifts in user preferences, often beyond specific product areas.
Strategic planning and Innovation: Detailed research on shifting markets and user behaviour to make strategic recommendations and keep products on-trend with changing markets.
Some common expansive research methodologies
Trend Analysis: Observing industry and social trends helps you anticipate emerging shifts in user expectations and demand.
Scenario planning: Exploring different scenarios helps develop visions of how a product may be used in an evolving context — such as a technologically enhanced future — as a basis for long-term strategy and adaptation.
Cultural Probes: Users are asked to document their lives, enabling researchers to discover off-line social and cultural influences that could drive future adoption of products in the future.
Longitudinal studies: tracking user behaviours for long periods of time helps us discover how users’ needs and attitudes change – and also how products can continue to evolve with users.
Avoiding Common Pitfalls in Generative and Expansive Research
Both research types come with potential "gotchas" that researchers should anticipate. Here are few to look out for:
Lack of Clear Focus: Generative research aims to explore new opportunities, but without a defined scope, new researchers might collect a lot of interesting but irrelevant data. Be sure and define a clear problem statement when operating in a "blue sky" research format.
Bias in Interpretation: Both generative and expansive research requires openness, yet some researchers may inadvertently project assumptions onto the data, interpreting it through a personal or organizational lens. This can result in missed opportunities or misunderstood needs. Staying objective and involving multiple perspectives can help mitigate this.
Overwhelming Amounts of Data: Generative & expansive research often generates large, unstructured data sets from sources like interviews, observations, and open-ended surveys. These data sets can be challenging to analyze manually. Using AI tools can help by automatically identifying patterns, clustering similar themes, and even offering preliminary predictive insights. For example, AI might highlight common phrases or sentiments across interviews or suggest likely trends based on the data, helping researchers efficiently spot meaningful insights without sifting through everything manually.
Difficulty in Prioritizing Insights: Not all uncovered needs are equally valuable to pursue. Without experience in evaluating insights against strategic priorities, researchers might emphasize less impactful findings. Try using the "Eisenhower Method" I outlined in this article to help prioritize.
Stakeholder Buy-In: Research often reveals needs that don’t align with existing product or business strategies. Rsearchers may find it difficult to communicate the importance of these insights to stakeholders who are focused on short-term goals. It's important to understand the point of view of these stakeholders and focus on meeting both their short-term and long term goals. Build trust by showing you are a good strategic partner who is focused on hitting those company level OKR's
Balancing Exploration with Practicality: Expansive research can inspire innovative ideas, but without feasibility checks, researchers may focus on concepts that aren't realistically achievable. Balancing creativity with practicality is essential to transforming research insights into actionable strategies. Involve engineering teams early in the process—share findings with them and leverage their expertise to develop solutions that balance user needs with technical feasibility, maximizing the return on investment (ROI) for innovation and practicality.