How do you scale and optimize the successful growth experiments and kill the failed ones?

Objective and Key Results (OKRs): - OKRs help in defining clear, measurable objectives with specific results that monitor progress and success. SMART Goals: - Specific, Measurable, Achievable, Relevant, Time-bound goals ensure that your growth experiments have clear objectives and measurable outcomes. Airbnb used SMART goals to increase the quality and quantity of their listings, which was a key metric. By focusing on photography as a lever to improve listing attractiveness, they hired professional photographers in New York City. The result was a significant increase in bookings, demonstrating the power of setting clear, measurable goals tied directly to business outcomes.

Defining your goal is the first step to defining, optimizing, and scaling your growth experiments. SMART is a good guideline for defining your objectives, designed to be realistic and help you know when you have completed them. OKR is another one (this one is a framework instead of a guideline) you can use to define your objective.

Growth experiments thrive or die by swift decisions. Analyze data ruthlessly. Double down on winning experiments by scaling resources and promoting best practices. For losers, cut your losses quickly. Free up resources and team bandwidth to focus on new ideas with higher growth potential. Remember, failing fast allows you to learn faster and iterate towards sustainable success.

Before launching an experiment, clarity on goals and metrics is key. From my experience, OKRs (Objective Key Results) outperform SMART goals by setting ambitious objectives and clear outcomes. For instance, in enhancing cohort-based education, my objective was to markedly improve student outcomes, measured by metrics like completion rates and post-course employment and KPIs like NPS score. OKRs are better than SMART goals in philosophy because they motivate us to set high targets and achieve significant, measurable impacts.

✅Scaling and optimizing successful growth experiments while learning from failed ones is essential for continual improvement. ✅To achieve this, implement a structured approach for analyzing both successful and failed experiments. ✅Take note of key metrics, customer feedback, and any unexpected outcomes from each experiment. Identify patterns and trends from successful experiments to replicate and scale them effectively. ✅In contrast, analyze failed experiments to understand the root causes of the failure and extract valuable lessons for future iterations. ✅By establishing an iterative learning process and continually refining your strategies, you can enhance your growth initiatives and achieve sustainable success.

Effective prioritization of hypotheses is key to efficient scaling and optimization: 1️⃣ Use the ICE framework (Impact, Confidence, Ease) to score each hypothesis. 2️⃣ Focus on hypotheses with high potential impact and feasibility. 3️⃣ Consider resource availability and required effort for implementation. 4️⃣ Prioritize hypotheses based on alignment with overall business goals. 5️⃣ Continuously reassess and reprioritize as new data and insights become available.

ICE Score: - Impact, Confidence, and Ease. This methodology helps in prioritizing hypotheses based on their potential impact, the team's confidence in them, and how easy they are to implement. RICE Score: - Reach, Impact, Confidence, and Effort. Similar to ICE but includes Reach as a factor, making it excellent for weighing more significant initiatives. Dropbox’s referral program is a notable example. The hypothesis was that giving both the referrer and the referee additional storage space would drive sign-ups. This hypothesis was prioritized highly because of its potential high impact and low effort. The result was a 60% increase in sign-ups month over month, a testament to prioritizing the right hypothesis.

A good growth hypothesis is based on research or data that clearly explains how it aligns with the objective. Secondly, it is testable. Thirdly, it helps with gathering marketing insights. Additionally, it enables a team to evaluate the potential impact of an idea, helping them to prioritize work with the highest impact.

A/B Testing: - A controlled experimentation approach where two versions (A and B) are compared against each other to determine which one performs better on a given conversion goal. Lean Startup Methodology: - Build-Measure-Learn loop encourages rapid iteration and learning from small, incremental experiments. Google is famous for running thousands of A/B tests annually to refine its user experience. A commendable example is testing 41 shades of blue for ad links to see which shade resulted in the highest click-through rate. This meticulous attention to detail and design of experiments underlines the value of even the smallest optimizations.

It becomes easier when you set this up scientifically. Think of this as input and output. Your inputs are your question, your hypothesis, and your experiment. Your outputs are your observations and analysis. Regarding the experiment, you may encounter some internal resistance, especially when it comes to bold changes, but to achieve radical results, you often have to try radical experiments.

A framework we use is: 1. Identify what is in disagreement 2. Based on this, what is the impact of this on the business 3. What are the steps for this to happen, how to do it? 4. Resource projection (hours, financial investment, etc.) 5. Projection on scale, if everything goes well, if it is adopted by the entire company, what is the return on this? 6. What do we need to do to validate this now? 6.1. What metrics will you measure if you are being efficient?

Data-Driven Decision Making: - Employing data analytics tools (like Google Analytics, Mixpanel, or Amplitude) to analyze experiment results and guide decisions. Five Whys: - A technique to drill down into the reasons behind a particular outcome, by asking "Why?" five times.

The main point is to analyze the metrics that were defined at the beginning of the test, and whether in fact the results were sufficient and satisfactory. The best thing is to tabulate the data, plot graphs and analyze whether the result was statistically significant.

Growth Model Framework: - Outlining how to scale growth, mapping out user acquisition, activation, retention, revenue, and referral. Pirate Metrics (AARRR): - Acquisition, Activation, Retention, Revenue, and Referral, focusing on optimizing each stage for growth. Spotify’s focus on personalized playlists like "Discover Weekly," which was initially an experiment, showcases scaling success. After proving its value by significantly increasing user engagement, Spotify optimized and integrated this feature into its core user experience, making it a staple of their growth strategy.

After analyzing the tests that brought sufficient results, it’s time to scale. How to scale up from this test and improve it. One possibility is to increase the sample little by little and optimize the results, when these possibilities are clearly identified.

In my opinion, killing or pivoting an experiment isn't about rejecting it entirely. A bad result doesn’t always mean bad content or a weak campaign. Sometimes, success comes down to timing, the audience, or the channel used. That same experiment might work better in a different context. Testing is about adapting, not giving up. Always challenge assumptions and try again, because the real mistake is thinking a failed test can’t work later. In growth marketing, every hypothesis deserves a second chance when adjusted to the right conditions.

Scaling and optimizing successful growth experiments while discontinuing failed ones is crucial for sustainable business growth. Here’s a structured approach to achieve this 1.Define Clear Metrics and KPIs 2. Establish Baselines 3. Expand Channels: By following this structured approach, you can effectively scale and optimize successful growth experiments while efficiently discontinuing those that do not deliver desired results, ensuring that resources are allocated towards the most impactful initiatives.

To enhance project effectiveness, I'll brainstorm with my team, create an Experimental Sheet, and calculate P.I.E Scores (Probability, Impact, Ease) for each experiment. We collectively assess ideas and prioritize experiments based on these scores. After implementation, we evaluate success using KPIs, specifically the Pirate Metric framework, to determine if an idea is successful or not.

If your experiment fails, you need to shut it down. I know it's tough to abandon your hard work, I have been there. But it's smarter to drop what isn’t effective rather than waste resources on it. Google's graveyard is the best example 👀. The upside is you can repurpose elements of the failed experiment, tweaking them into a new, potentially successful one.