Lifecycle · Phase 7 of 8
Optimisation compares real performance against the success measures set during Planning, helping teams decide what to improve, continue or change next. 53 governed templates keep those decisions grounded in evidence.
Optimise is where teams measure how the product is performing after launch. Real usage is compared with the success measures set during Planning, helping teams understand what the data means and what should happen next. The aim is to turn live performance into continuous learning and improvement.
During Optimise, the team needs to be clear on:
Gensudo includes 53 Optimise templates, making it the second-largest phase in the library. The Product Manager leads with 14 templates, followed by the Creative Director with 12 and the Product Director with 11. Key documents include the Product-Market Fit Assessment, RICE Scoring Sheet, OKR Alignment Document and Customer Journey Map, helping teams review both product performance and customer experience. Gensudo’s quality scorecard and trust rail also help keep documents accurate as they evolve, with readiness updating as content is reviewed and improved.
Take the success measures written in the Plan phase and put the real numbers next to them. This comparison — promised versus actual — is the whole reason those measures were written down early.
A metric moving tells you something happened; it doesn't tell you why. Dig into the causes — behavioural, competitive, experiential — before committing to a response.
Revisit the Product-Market Fit Assessment and walk the Customer Journey Map against live behaviour. Products drift from their users gradually, then suddenly.
Run prioritisation again — this time with real data where estimates used to sit. Work that scored well on optimism often re-ranks dramatically on evidence.
Update the roadmap and objectives so the plan reflects what you now know. An optimisation cycle that ends in a slide deck instead of a changed plan hasn't finished.
Whatever you committed to in the Plan phase — that's the point of writing success measures down before the work starts. Post-launch is the wrong moment to choose metrics, because by then every number has a constituency. Read the promised measures first, diagnose what's behind them second, and only then consider whether new measures have earned a place.
Immediately — the first-days watch plan from the Launch phase is the front edge of it. But early data answers narrow questions (is it stable, are people arriving) while the deeper reads — fit, retention, journey friction — need enough usage to mean something. Optimisation is less a start date than a rhythm: read, diagnose, decide, feed back, repeat.
Diagnose before reacting. Bad numbers have many possible causes — the wrong audience arrived, the journey leaks at a seam, the market moved, or the product genuinely doesn't deliver — and each demands a different response. A disciplined optimisation phase treats disappointing data as an investigation brief, not a verdict. If the diagnosis keeps coming back structural, that's the signal to move honestly into Scale or Sunset.
Iteration is activity; optimisation is accountable activity. The difference is the reference point: optimisation reads real performance against success measures that were committed to in advance, diagnoses causes, and feeds decisions back into a documented plan. Teams that merely iterate can ship improvements indefinitely without ever answering the phase's core question — is it working?
Optimisation depends on measurement that went live before customers did. If the data isn't there to read, launch readiness is where it went missing.
Revisit the Launch phase →Eventually the optimisation loop poses a bigger question: double down, reposition, or retire well. Answer it deliberately.
Go to Scale or Sunset →Measure performance against the plan, reprioritise using live data and keep every document aligned as the product evolves.