Turning Listener Research Into a Smarter Content Strategy

I started by gathering context from Elle, reviewing podcast analytics, and listening to episodes. This helped me separate what the team already knew from what it was assuming, and identify the decisions the research needed to inform. I then built the research plan around those decisions rather than treating the project as a general audience survey.

I used the survey to look for broader patterns and semi-structured listener interviews to understand the “why” behind them. The survey showed that hands-on PMM experience and practical frameworks were each valued by 68% of respondents, while seniority alone was selected by only 5%. Interviews and analytics added nuance: listeners wanted practical, candid lessons from people close to the work, and often chose episodes because the topic addressed a current problem rather than simply because of the guest’s profile.

The key strategic decision was not to overreact to the interest in AI or any single data point. I cross-referenced themes across the survey, interviews, and analytics, then recommended strengthening the podcast’s existing practitioner-led case-study position rather than dramatically repositioning it. That led to recommendations for problem-first episode packaging, more intentional guest selection, applied AI content grounded in workflows and limitations, stronger episode navigation, and topics around internal influence and proving PMM value.

I translated the findings into an analysis workbook, a final research report, and prioritized recommendations that Elle and the other apprenticeship workstreams could use. A useful buy-in moment came when the research validated several of Elle’s instincts while adding sharper guidance on guest seniority, AI, packaging, and listener usefulness. The work also became an external LinkedIn case study, extending its value beyond the internal decision-making process.

I organized the work around the problem each method solved rather than treating the tools as a stack. I reviewed the podcast’s existing analytics exports in Google Sheets to understand what was happening, used Google Forms to collect broader listener patterns, and used semi-structured interviews—with Gemini meeting notes and transcripts—to understand the “why” behind those patterns. I then cross-referenced all three sources before turning findings into recommendations.

ChatGPT was the most significant accelerator. During analysis, I used it to synthesize survey data from Google Sheets, compare audience segments, cross-reference survey and interview insights, build much of a multi-tab Excel analysis workbook, and structure the final report and recommendations. It also helped create a Google Apps Script that generated much of the survey in Google Forms, reducing manual setup time.

AI did not replace the research judgment. I still defined the strategy, chose the questions, conducted the interviews, evaluated conflicting evidence, and decided which patterns were meaningful enough to recommend. ChatGPT could surface themes and organize information, but it also made it tempting to analyze everything. I had to narrow the scope, pressure-test its outputs, and distinguish actionable patterns from interesting observations. I would use the same combination again, but only with that human review built into the process.

The project succeeded against its original goal: improving decision quality by creating a clearer, evidence-based understanding of the Product Marketing Adventures audience and turning it into actionable guidance. I synthesized 19 analyzed survey responses and three listener interviews into a multi-tab analysis workbook, a written research report, and prioritized recommendations.

The clearest measurable evidence came from the research itself. Hands-on PMM experience and practical frameworks were each selected by 68% of respondents as important guest qualities, while seniority was selected by only 5%. AI was also a strong topic signal, but listeners wanted applied AI content grounded in practical workflows, real outcomes, limitations, and human judgment. These findings turned broad assumptions into recommendations around practitioner-led content, problem-first episode packaging, guest selection, internal-influence topics, and stronger episode navigation.

The practical change was that Elle had a more structured, listener-backed foundation for decisions that had previously relied more heavily on intuition and podcast analytics. I would not claim that the research caused audience growth or that every recommendation was implemented: there was no long enough post-research measurement period to connect the work to download or retention metrics. Its strongest result was improved decision quality, plus reusable hypotheses and recommendations to test over time. The work was also repurposed as an external case study, extending its value beyond the internal research deliverable.