Customer-Centric Product Strategy

• Product Discovery

  Demonstrated

  States hypotheses explicitly before testing them, picks the research method that fits the question, names its cost to the user, and rejects methods that would not answer it. His plan for a user conversation was right: a clear goal first, non-leading questions, and a sample not limited to existing users.

• Customer-Centric Product Strategy

  Partial

  Over the session he took a user segment from first idea to a strategy decision and kept the reasoning coherent at every step: who the users are, how to recognise them in product data, what the hypothesis is, and how to confirm it cheaply before investing. When a metric moved in his favour, he did not present it as a win. He first asked what caused it, considered explanations both inside and outside the product, asked whether the change would last, and proposed involving marketing to cost the next step against the alternative. That is strategy reasoned from evidence rather than from opinion. Two things separate this from a pass. His first instinct when segmenting was to rank by audience size and revenue rather than by the job each group needs done. And before recommending further investment, he did not yet check whether the new users actually stay, or how much of that market is still open. Those two checks decide whether a promising number is worth betting on.

• JTBD

  Partial

  Shows a genuine job-based instinct: unprompted, he identified a segment defined by the situation in which people use the product and what they need it to do. He did not apply that lens consistently; other segments were defined by what people consume rather than by the job, so the approach is present but not yet a habit.

• Prioritization

  Partial

  Approaches priority with a clear, business-aware process: understand the cause, estimate the cost of acting, compare the expected return with the alternative, and factor in market trends. The session gave this skill less time than the others, so I saw the method but not enough decisions under pressure to award a pass.

We worked through a product case from segmentation to a strategy decision, with a short user-interview exercise in the middle. You reasoned aloud the whole way, which made your thinking easy to follow and easy to assess. What stood out: - You see segments others miss. Your strongest segment was defined by a real situation of use and a real need, not by demographics, and you found it yourself. - You connect user behaviour to data. With nothing labelled in the data, you proposed observable behavioural signals that would identify the segment. This was the strongest part of the session. - You test your own answers. Twice, without being asked, you looked for people who would match your criteria for the wrong reasons. - You treat a belief as a hypothesis. You stated it explicitly before proposing how to check it. - You choose methods deliberately. You proposed a method that fits the question, named its cost to the user, and set aside another one because it would not answer what you needed to know. - You ask why before you act. Faced with a positive change in a metric, you looked for the cause first, including causes outside the product, and asked whether the change would last. - You plan research conversations well. Clear goal, non-leading questions, and a sample that is not limited to your own users. - You learn in real time. After the interview exercise you named your own slip immediately and moved straight to what to do differently. You also challenged one of my points, and it was a fair challenge. Where to grow next: - Make the job lens your default. You already defined one segment by its job; apply the same test to every segment. Two groups that consume the same thing in different situations usually have different needs. - In research conversations, go to one specific past occasion. Asking what happened last time produces facts; asking what people usually do produces generalities. Your plans are right; this is the execution step. - Widen the frame from the data to the strategy. You already look for the cause behind what the data shows, which many product managers skip. The next step is to ask whether a change is durable or a one-off, and to weigh what the product data cannot show: the size of the opportunity, its profitability, the competition, the cost of pursuing it. A sound reading of the metrics is where a strategic conclusion starts, not where it ends.

- Read "The Mom Test" by Rob Fitzpatrick. A short, practical guide to user conversations that produce facts rather than opinions: ask about specific past situations, about the person's life rather than your product, and never pitch. - Go deeper into Jobs to be Done. "Competing Against Luck" by Clayton Christensen is a good entry point; it shows how to describe segments by the job people hire a product for rather than by who they are or what they consume. - Practise interviewing in a safe setting. Ask a colleague to play a user who answers vaguely, and practise bringing the conversation back to one concrete occasion. - Learn the basics of market sizing and unit economics: estimating a market top-down and bottom-up, and how retention, LTV and acquisition cost decide whether a segment is worth investing in. - Read "Good Strategy Bad Strategy" by Richard Rumelt. It helps move from analysing data to forming a strategy: a clear diagnosis, a guiding choice, and the trade-offs that come with it.