S2 Ep24: Kill the project, not the product manager

S2 Ep24: Kill the project, not the product manager

Why most product teams cannot tell a dead project from an early one, and the three tests that separate a clean kill from a case worth tabling. SHOWNOTES You have rebuilt the business case three times, the customer calls went fine, and it still feels like you are polishing a tombstone. The problem is rarely a missing kill decision or a missing budget, it is that a dead project and an early one look identical from the outside. Who this is for: Product managers, R and D leads and gate committee members deciding whether to kill, fund or table a business case in life science tools and diagnostics. Jasmine Gruia-Gray gives Matt Wilkinson a three-test diagnostic for telling a genuinely dead case from one that is merely early: is the data gap objective or epistemic, what does your most resistant buyer say, and is this a product problem or a timing problem. She then defends the framework against three sharp objections, including the honest admission that tabling a case can quietly become a way to never decide. The episode closes with the two moves a nervous product manager can make on Monday and the one question that exposes a narrative dressed up as data. Key idea: Most teams do not have a kill problem or a funding problem; they cannot tell a dead project from an early one, and three tests separate them. What you will learn: * How to tell an objective data gap, which belongs in an assumption log, from an epistemic one, which means nobody has asked the right question yet * Why running the case against your most resistant buyer surfaces adoption costs a favourable panel will never show you, using concordance data as the worked example * How to separate a product problem from a timing problem, and when tabling with a dated re-entry trigger beats killing * Why the synthetic buyer panel replaces the broken step of rebuilding the model rather than adding a fourth checkpoint * Where the framework is exposed: a tabled case with no external owner tends to fade rather than die * The two Monday moves and the one-sentence evidence test that tells you whether you have a business case or a story Keywords: stage gate process, business case, product management, kill or table decision, synthetic buyer panel, voice of customer, life science tools, diagnostics commercialisation, R and D pipeline, market timing, PersonaAI, go-to-market Watch, subscribe and go deeper: Read the full piece, When to Walk Away: Kill the Business Case or Just Table It, at strivenn.com under Thinking. If you want help running this diagnostic on your own pipeline, including the synthetic buyer panel, Strivenn offers a free thirty minute PersonaAI consultation with concrete next steps either way. Subscribe to A Splice of Life Science Marketing and find the details at strivenn.com.

S2 Ep23: Your stage gate is a cheerleading session in disguise

For life science PMs: at the gate, treat dissenting customers as evidence. Enthusiast quotes only confirm what you hoped. Most stage gate reviews are built to help a product pass. Whether it should pass is a different question, and the gate rarely asks it. That design produces confident decks and expensive mistakes. Who this is for: product managers, commercial leaders and gate committee members in life science tools and diagnostics companies. Matt Wilkinson and Jasmine Gruia-Gray examine why gate processes reward building a strong case, and why that skill has little to do with whether the product should exist. They walk through a falsification approach that asks what would kill a programme, then get specific about revenue forecasts, customer research and the one question that exposes a weak case. The falsification shift changes the question the gate asks. It leaves the template alone. What you will learn: * Why a skilled PM can pass a gate the product should have failed * How to apply a falsification test to technical claims and to revenue forecasts * The three questions that decide whether a forecast survives the room * What to write on Monday when your gate template has no field for failure conditions * Why one customer who already solved the problem tells you more than ten enthusiasts * The one question to ask R and D, and why a pause is the signal you want Chapters: * [00:18] How gates end up rewarding optimism * [01:38] The system is working as designed * [02:15] Can the committee catch a well-built case? * [03:04] The falsification shift: prove yourself wrong * [03:48] Technical claims and revenue forecasts * [05:03] Experience does not immunise a committee * [05:41] The transparency penalty for honest PMs * [06:30] The culture problem and the post-launch debrief * [07:35] One experiment before the feasibility gate * [08:23] Getting R and D to test what they believe in * [08:57] Your first move on Monday * [09:31] Rethinking the customer research section * [09:54] The one question that cuts across all of it * [10:19] Where to go next Keywords: stage gate process, life science product management, product commercialisation, falsification testing, revenue forecasting, gate review, pre-mortem, buyer evidence, life science tools marketing, go-to-market strategy, product feasibility, commercial due diligence Watch the full episode and subscribe to A Splice of Life Science Marketing. Jasmine's full article, including the two-track Evidence Tier Framework and the gate-by-gate evidence standards for Feasibility, Development, Launch and Lifecycle, is at https://strivenn.com/thinking/stage-gates-should-ask-for-evidence-not-optimism. If you have a gate coming up, book a growth consultation at https://strivenn.com/contact or find Jasmine on LinkedIn.

S2 Ep 21: The Tie is the Map

A tied poll becomes buyer segmentation once you read who chose each option and why.You run a poll and the result lands in a dead heat. Most people mark it inconclusive and pick the option they already liked.This is for life science marketers and commercial leaders who run preference tests on positioning, covers, campaign assets or messaging hierarchies. Matt Wilkinson ran three cover designs for his first book, The Buyer in the Loop, past two audiences at once: his LinkedIn network and a synthetic customer panel built from real buyer evidence. The headcount tied almost perfectly. This conversation unpacks why that tie carried more signal than any single winner could have, and how re-reading the votes by role exposed four separate markets hiding inside one count. Key idea: stop asking which option wins, start asking who chooses each option and why. What you will learn * Why a tied vote is a sign that you are looking at more than one marketHow to segment a poll by the role of each voter rather than the headline count * What a synthetic customer panel can tell you before a single human votesHow to spot the option your audience wants that you never put on the table * Where the honest limits of a small qualitative sample sit, and how to use it anyway * The first move to make in the two hours after a tie lands Chapters[ 00:20] The shrug that misreads a tied poll[01:50] Fifteen covers down to three[ 02:32] Nearly filing it as a failed experiment [02:47] Four markets hiding inside one count [04:03] Buying a signal as much as the content [05:00] The panel that mapped the disagreement first [06:16] Three objections a life sciences marketer would raise [11:03] What to do in the first two hours[ 11:32] The option nobody put on the table [12:32] What this changes about preference testing [13:46] Where to read the full blog Watch the full conversation, subscribe for more life science marketing strategy, and read the original blog at https://strivenn.com/thinking/your-audience-poll-came-back-split-read-who-is-hiding-in-the-tie

S2 Ep21: A Brand is Trust

Pride in the output is the human test AI cannot replace, and it is what protects life science brands. Somewhere in your marketing stack right now, an AI is publishing faster than anyone can check it. This episode is about the quiet way that speed erodes trust before you notice it has gone. Who this is for: CEOs, commercial leaders and marketers at life science tools and diagnostics companies who are scaling content with AI and cannot afford to be caught wrong. Matt Wilkinson and Jasmine Gruia-Gray unpack why brand is trust, why a single drifted claim costs more in front of a scientist than in any consumer market, and how a claim-by-claim fact-check table, discernment and synthetic customers keep AI output dependable rather than merely fast. Matt also shares the story behind his Marketing Week CX50 recognition as one of the top ten life sciences marketers in the UK, and why his first instinct was to assume it was a phishing scam.

S2 Ep20: AI Search Reads Structure, Not Content Quality

For life science marketers: why AI engines judge your pages on structure and schema, and how to stop yours being misread.We ran an AI search audit on its own website and the tool recommended building three pillar pages that already existed. The cause was five characters at the end of a URL, which made three of the company's most important pages invisible to the AI engines that buyers now use to build shortlists.This conversation is for life science marketing leaders, brand managers and commercial teams who are investing in serious content and want that content to be found by AI search.Matt Wilkinson and Jasmine Gruia-Gray work through a real diagnostic on strivenn.com, from the misclassified pillar pages to the slug and schema gaps behind them. They cover why AI engines read structural signals rather than content quality, what citation compression means for visibility, and how to move the fix out of the SEO backlog and into a revenue conversation.Key idea: AI engines classify pages by structural signals like URL slugs and schema, so even your best content stays invisible if the architecture reads as tactical.What you will learn:How a single branded URL suffix can make an authority page read as a throwaway campaign asset to an AI crawlerWhy content quality and content visibility are coming apart as AI search growsWhat citation compression is and why being absent from a three to five brand result set matters more than rankingHow to run the diagnostic yourself with Claude Cowork instead of paying for an SEO agencyHow to reframe a schema fix as a revenue visibility project so it clears the developer backlogThe first move for a brand manager who runs the test and finds their pages missingChapters:[00:19] The audit that made us stop and think[01:53] What the SEO audit got wrong[02:43] Five characters at the end of a URL[03:20] AI reads a different set of signals[04:07] The minority-behaviour objection[06:01] Why the ROI case is future-looking[06:47] Confidence in the audit and fixing schema at scale[08:15] The backlog failure mode[09:19] Reframing the fix as revenue visibility[10:08] The brand manager's first move[10:56] The unconsidered set[11:26] Building in publicKeywords: AI search, AI discoverability, life science marketing, citation compression, schema markup, URL structure, generative engine optimisation, buyer consideration set, answer engine optimisation, HubSpot, B2B buying behaviour, StrivennWatch the full conversation, subscribe to A Splice of Life Science Marketing, and read the full blog at https://strivenn.com/thinking/i-ran-an-ai-seo-audit-on-my-own-site