I went straight to user empathy and started talking about willingness to pay, which felt right but I probably skipped over the obvious stuff like checking if it's a niche product with no substitutes.
Start by reframing the observation as a hypothesis about customer value and business strategy, not a judgment. Then systematically analyze the pricing from multiple angles—customer segments, competitive context, and company objectives—before proposing a data-driven action. Emphasize that as a PM, your reaction is to investigate and validate, not to immediately change the price.
Pro tip: Acknowledge that high prices can be intentional (e.g., premium positioning, price anchoring, or margin optimization) and that your first step is to understand the 'why' before jumping to solutions. This shows strategic thinking and avoids the trap of assuming the price is wrong.
Note the specific product, its price, and the context (e.g., store location, competitor prices). Form hypotheses about why it might be priced high, such as premium branding, low elasticity, or a pricing error.
Collect relevant data: sales volume, customer feedback, competitor pricing, and the product's role in the portfolio. Consider the drugstore's overall pricing strategy and target market.
Evaluate how the price aligns with business goals (e.g., revenue, market share, brand perception). Assess potential risks and opportunities of the current price for different customer segments.
Based on analysis, suggest possible actions: maintain price, adjust, bundle, or run promotions. Prioritize based on expected impact and effort, and consider A/B testing if feasible.
Make a clear recommendation with rationale, and define success metrics. Outline a plan to monitor results and iterate, emphasizing continuous learning.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.