Determine whether experiment results reflect real differences or random chance to avoid making expensive decisions based on noise instead of signal.
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Get a grip on what's actually working and what needs course correction. Use data and experiments to make decisions instead of opinions. See how changes in one part of the system affect everything else. Random tactics don't compound, coordinated ones do.
Select tools across your growth stack using clear evaluation criteria. Avoid common pitfalls, ensure integrations work, and build a system that scales with your business.
Uncover specific pain points, validate assumptions, and reveal what actually drives buying decisions. Run research that produces actionable insights, not just interesting quotes.
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Run quarterly business reviews that assess current state, set ambitious but realistic goals, build actionable roadmaps, and define key results that keep everyone aligned.
Analyse monthly performance data across all four growth engines. Identify what is working, what is not, and make tactical adjustments using a structured decision framework.
Don't just "try something". Write a hypothesis predicting what will happen and why. Design the experiment with proper controls so you actually learn whether your hypothesis was right.
Don't just declare winners and move on. Extract the pattern, document why it worked, update playbooks and templates, and build an experimentation culture where testing is continuous.
Document your ideal customer's role, goals, and challenges to tailor messaging and prioritise features that solve real problems they actually pay for.
Attract prospects through valuable content that solves real problems, building trust and generating qualified leads who approach you.
Define events that start automation workflows so the right message reaches people at the right moment based on their actual behaviour not arbitrary timing.
Focus your entire organisation on the single metric that best predicts success at your current growth stage, avoiding distraction and misalignment.
Track revenue growth from existing customers through expansion and contraction to prove your product delivers increasing value over time.
Track your user journey through Acquisition, Activation, Retention, Referral, and Revenue to identify which stage constrains growth most.
Connect triggers to actions across systems so repetitive tasks happen automatically and teams can focus on work that requires judgement instead of admin.
Track how fast your pipeline of ready-to-buy leads grows to forecast sales capacity needs and spot when lead quality or sales efficiency changes.
Store raw data from all business systems in one place to run analyses and build reports that combine information across marketing, sales, and product.
Send a series of scheduled emails that educate prospects over time to stay top-of-mind without overwhelming them with aggressive sales pitches.
Calculate the total cost of winning a new customer to evaluate marketing efficiency and ensure sustainable unit economics across all channels.
Navigate competing priorities and secure buy-in by systematically understanding, influencing, and aligning internal decision-makers toward shared goals.
Log emails, calls, and meetings automatically to understand what drives deals forward and coach reps based on actual behaviour rather than guesswork.
Build distribution through your personal brand and network where your expertise and story attract customers who trust you before your company.
Calculate how many users you need in experiments to detect meaningful differences and avoid declaring winners prematurely based on insufficient data.
Prioritise tasks systematically by sorting them into urgent-important quadrants, focusing effort on high-impact activities.
Articulate the specific outcome customers get from your solution to communicate why they should choose you over doing nothing or using alternatives.
Define pipeline progression steps to standardise how reps advance opportunities and give managers visibility into where deals stall or convert unexpectedly.
Structure experiments around clear predictions to focus efforts on learning rather than random changes and make results easier to interpret afterward.
Maintain an unchanged version in experiments to isolate the impact of your changes and prove causation rather than correlation with external factors.