When research stalls, the bottleneck is often not the methodology or recruiting but a lack of internal consensus on the target audience. The first step should always be audience definition. If the team can't agree, then the initial research project must be to define and validate the audience itself.
Effective, fast research isn't about skipping steps but about rightsizing the effort. Instead of defaulting to a previous method like "10 interviews," teams should determine the minimum insight needed to mitigate the specific risk at hand, using that to define the research scope and approach.
To make research resonate, don't just present findings. Frame the readout as a narrative that begins with the stakeholders' known assumptions and concerns. This creates a compelling journey. Enhance impact by assigning 'homework,' like a curated podcast of interview clips, to foster direct empathy.
Instead of arguing for more time, product leaders should get stakeholder buy-in on a standardized decision-making process. The depth and rigor of each step can then be adjusted based on available time, from a two-day workshop to an eight-month study, without skipping agreed-upon stages.
To avoid stakeholders undermining research results later ('you only talked to 38 people'), proactively collaborate with them before the study to define the minimum standard of rigor they will accept. This alignment shifts the conversation from a post-mortem critique to a pre-launch agreement, disarming future objections.
To overcome the challenge of reaching non-customers in B2B, leverage specialized firms like GLG or Bridger. These networks can connect you with specific, hard-to-reach personas (e.g., CFOs of Global 2000 companies) for interviews within days, turning a major research blocker into a simple logistical task.
When using LLMs to analyze unstructured data like interview transcripts, they often hallucinate compelling but non-existent quotes. To maintain integrity, always include a specific prompt instruction like "use quotes and cite your sources from the transcript for each quote." This forces the AI to ground its analysis in actual data.