Lean Analytics: book: Measuring Success: A Guide to Lean Analytics for Entrepreneurs

1. What is Lean Analytics and why is it important for entrepreneurs?

Entrepreneurs face many challenges and uncertainties in their journey of creating and growing a successful business. They need to constantly test their assumptions, validate their ideas, and measure their progress. But how can they do that effectively and efficiently? How can they know if they are on the right track or if they need to pivot or persevere? How can they avoid wasting time, money, and resources on things that don't matter? This is where Lean Analytics comes in.

Lean Analytics is a methodology that helps entrepreneurs use data to make better decisions and achieve product-market fit. It is based on the principles of Lean startup, which advocates for building products iteratively, learning from customer feedback, and applying the scientific method to entrepreneurship. Lean Analytics helps entrepreneurs answer two fundamental questions: What should I build? and How do I know I'm right?

To apply Lean Analytics, entrepreneurs need to follow these steps:

1. Define the stage of their business. Depending on the type and maturity of the business, entrepreneurs need to focus on different metrics and goals. For example, a new startup that is trying to find a problem-solution fit should focus on validating the problem and the customer segment, while a more established business that is trying to scale should focus on optimizing the acquisition and retention of customers.

2. Choose a key metric that matters. Entrepreneurs need to identify the one metric that best reflects the value they are creating for their customers and the success of their business. This metric should be actionable, accessible, auditable, and indicative of the business model. For example, a SaaS business might choose monthly recurring revenue (MRR) as their key metric, while a social media platform might choose daily active users (DAU).

3. Set a target for the key metric. Entrepreneurs need to establish a realistic and achievable goal for their key metric that will help them move to the next stage of their business. This goal should be specific, measurable, attainable, relevant, and time-bound. For example, a SaaS business might set a target of reaching $10,000 MRR in 6 months, while a social media platform might set a target of reaching 1 million DAU in a year.

4. Experiment and learn. Entrepreneurs need to design and run experiments that will help them improve their key metric and validate their hypotheses. They need to collect and analyze data, measure the impact of their actions, and learn from the results. They need to iterate and repeat this process until they reach their target or discover a new insight that changes their direction.

By using Lean Analytics, entrepreneurs can gain a deeper understanding of their customers, their market, and their business. They can make data-driven decisions that will help them achieve product-market fit and grow their business faster and smarter. They can also avoid common pitfalls and biases that can lead them astray or cause them to fail. Lean Analytics is not a magic formula or a silver bullet, but a powerful tool and a mindset that can help entrepreneurs succeed in the uncertain and competitive world of entrepreneurship.

2. How to build, measure, and learn from your experiments?

One of the core principles of lean analytics is to adopt the lean startup methodology, which is a systematic approach to creating and validating new products or services. The lean startup methodology consists of three main steps: build, measure, and learn. These steps are not linear, but rather form a feedback loop that allows entrepreneurs to test their assumptions, learn from the results, and iterate quickly.

The build-measure-learn loop can be applied to any aspect of a business, from the value proposition to the customer segments to the revenue model. The goal is to minimize the waste of time, money, and resources by focusing on the most critical hypotheses and finding the fastest way to test them. Here are some key points to keep in mind when applying the lean startup methodology:

- Build does not mean creating a fully functional product or service, but rather a minimum viable product (MVP) that has enough features to test a specific hypothesis. An mvp can be anything from a landing page to a prototype to a beta version. The idea is to get something in front of the potential customers as soon as possible and collect their feedback.

- Measure means defining the key metrics that will indicate whether the hypothesis is valid or not. These metrics should be actionable, meaning that they can inform the next decision or action. They should also be relevant, meaning that they reflect the actual value that the product or service provides to the customers. Examples of key metrics are conversion rates, retention rates, customer satisfaction, revenue, etc.

- Learn means analyzing the data collected from the measurement and drawing conclusions about the hypothesis. The learning can be either qualitative or quantitative, depending on the type and amount of data available. The learning should answer questions such as: What worked? What didn't work? Why? What can be improved? What should be changed? What should be kept? What should be discarded?

The build-measure-learn loop is an iterative process that can be repeated as many times as necessary until a product-market fit is achieved. A product-market fit is when the product or service meets the needs and expectations of the customers and generates sustainable growth. The lean startup methodology helps entrepreneurs to find the product-market fit faster and more efficiently by validating their assumptions and learning from their experiments.

3. How to choose and focus on the key metric that drives your business growth?

One of the most important decisions you can make as an entrepreneur is to identify and focus on the key metric that drives your business growth. This metric, often called the One Metric That Matters (OMTM), is the single measure that best captures the core value that your product or service delivers to your customers. By tracking and optimizing the OMTM, you can align your team, prioritize your actions, and accelerate your learning.

However, choosing and focusing on the OMTM is not a trivial task. There are many potential metrics that you can measure, and each one may have different implications for your business model, your customer segments, your value proposition, and your competitive advantage. How do you decide which metric is the most relevant and meaningful for your business?

Here are some tips and guidelines to help you choose and focus on the OMTM:

- 1. Understand your business stage and goal. Depending on where you are in your entrepreneurial journey, you may have different objectives and challenges. For example, if you are in the problem-solution fit stage, your goal is to validate that you are solving a real problem for a specific customer segment. In this case, your OMTM may be something like customer interviews, problem validation surveys, or landing page conversions. On the other hand, if you are in the product-market fit stage, your goal is to validate that you have a viable and scalable business model. In this case, your OMTM may be something like customer acquisition cost, customer lifetime value, or retention rate.

- 2. Choose a metric that is actionable and testable. Your OMTM should be something that you can directly influence and improve with your actions and experiments. It should also be something that you can measure and track over time, preferably with a clear target or benchmark. For example, if your OMTM is revenue, you should be able to identify and test the key drivers of revenue, such as pricing, customer segments, channels, or features. You should also be able to set a realistic and achievable revenue goal based on your market size, customer demand, and competitive landscape.

- 3. Choose a metric that is relevant and meaningful for your customers. Your OMTM should reflect the core value that your product or service delivers to your customers. It should also be aligned with your customer's needs, expectations, and behaviors. For example, if your product is a social media platform, your OMTM may be something like daily active users, engagement rate, or viral coefficient. These metrics capture how often and how deeply your customers use and share your product. However, if your product is a B2B software solution, your OMTM may be something like customer satisfaction, net promoter score, or churn rate. These metrics capture how happy and loyal your customers are with your product.

- 4. Choose a metric that is simple and intuitive. Your OMTM should be easy to understand and communicate, both for yourself and for your team. It should also be easy to calculate and monitor, preferably with a single number or a simple formula. For example, if your OMTM is customer lifetime value, you should be able to estimate it with a simple equation, such as average revenue per customer multiplied by average customer lifespan. You should also be able to track it with a simple dashboard or a spreadsheet.

- 5. Choose a metric that is specific and focused. Your OMTM should be narrow and precise, not broad and vague. It should also be consistent and stable, not changing frequently or arbitrarily. For example, if your OMTM is customer acquisition, you should specify which customer segment, which channel, and which time period you are targeting. You should also stick to your OMTM until you achieve your goal or learn something new, not switch to a different metric every week or month.

To illustrate how to choose and focus on the OMTM, let's look at some examples of successful startups and their OMTMs:

- Airbnb: In the early days of Airbnb, the founders realized that the quality and quantity of the photos of the listings were the key drivers of their growth. They decided to make their OMTM the number of bookings per listing, and they experimented with different ways to improve the photos, such as hiring professional photographers, offering free photography services, or creating a photo guide for hosts. By focusing on this metric, they were able to increase their bookings by 2.5 times in a month.

- Dropbox: When Dropbox was trying to achieve product-market fit, the founder discovered that the most effective way to acquire new customers was through word-of-mouth referrals. He decided to make his OMTM the number of invites sent by each user, and he experimented with different ways to incentivize and reward referrals, such as offering free storage space, creating a viral video, or launching a referral program. By focusing on this metric, he was able to grow his user base from 100,000 to 4 million in 15 months.

- Netflix: When Netflix was trying to dominate the online streaming market, the company realized that the key to retaining and satisfying their customers was to offer personalized and relevant recommendations. They decided to make their OMTM the percentage of views driven by recommendations, and they experimented with different ways to improve their recommendation algorithm, such as collecting more user data, creating more categories, or launching a prize competition. By focusing on this metric, they were able to increase their customer loyalty and reduce their churn rate.

As you can see, choosing and focusing on the OMTM can help you achieve your business goals faster and more efficiently. However, remember that the OMTM is not a static or fixed metric. It may change as your business evolves, as your market conditions change, or as you learn new insights. Therefore, you should always be ready to revisit and revise your OMTM as needed. The key is to keep measuring, learning, and improving. That's the essence of lean analytics.

4. How to identify and navigate the different phases of your startup journey?

One of the most challenging aspects of building a successful startup is knowing what stage you are in and what actions you need to take to move forward. Different stages require different strategies, metrics, and mindsets. In this section, we will explore the common stages of a startup journey and how to apply the principles of lean analytics to measure your progress and make data-driven decisions.

According to the lean Analytics book, there are six stages that most startups go through:

1. Empathy: This is the stage where you try to understand the problem you are solving, the market you are targeting, and the potential customers you are serving. You need to validate your assumptions and hypotheses by talking to real users, conducting surveys, interviews, and experiments. The key metric in this stage is problem/solution fit, which means that you have a clear and compelling value proposition that resonates with your target audience.

2. Stickiness: This is the stage where you try to build a minimum viable product (MVP) that delivers your core value proposition and solves the main pain point of your customers. You need to test your product with early adopters, collect feedback, and iterate quickly. The key metric in this stage is product/market fit, which means that you have a product that people want, use, and love.

3. Virality: This is the stage where you try to grow your user base by leveraging word-of-mouth, referrals, and social sharing. You need to optimize your product for virality, by adding features that encourage users to invite others, share their experiences, and create network effects. The key metric in this stage is viral coefficient, which measures how many new users each existing user brings in.

4. Revenue: This is the stage where you try to monetize your product and generate revenue from your users. You need to find the right business model, pricing strategy, and revenue streams that match your value proposition and customer segments. The key metric in this stage is customer lifetime value (LTV), which estimates how much revenue you can expect from each customer over their relationship with your product.

5. Scale: This is the stage where you try to expand your market reach and grow your business beyond your core niche. You need to explore new channels, markets, segments, and partnerships that can help you acquire more customers and increase your revenue. The key metric in this stage is customer acquisition cost (CAC), which measures how much you spend to acquire each new customer.

6. Innovation: This is the stage where you try to sustain your competitive advantage and avoid being disrupted by new entrants or substitutes. You need to constantly innovate your product, service, and business model, by adding new features, value propositions, and revenue streams that delight your customers and differentiate you from your competitors. The key metric in this stage is net promoter score (NPS), which measures how likely your customers are to recommend your product to others.

Each stage has its own challenges, opportunities, and pitfalls. By using lean analytics, you can measure what matters, learn from your data, and make informed decisions that move your startup forward.

5. How to apply the four steps of data-driven decision making: collect, report, analyze, and act?

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One of the core principles of lean analytics is to use data to make informed decisions that help you achieve your goals. However, data alone is not enough. You need to have a clear process for collecting, reporting, analyzing, and acting on the data that matters most to your business. Here are the four steps of data-driven decision making that you can apply to any stage of your entrepreneurial journey:

1. Collect: The first step is to collect the right data that reflects your key performance indicators (KPIs). KPIs are the metrics that measure your progress towards your objectives. For example, if your goal is to increase customer retention, you might collect data on churn rate, customer lifetime value, and customer satisfaction. You should also collect data from multiple sources, such as web analytics, surveys, interviews, and experiments, to get a holistic view of your customers and their behavior.

2. Report: The second step is to report the data in a way that is easy to understand and communicate. You should use visualizations, dashboards, and reports that show the trends, patterns, and insights from your data. You should also use benchmarks, targets, and thresholds to compare your performance with your expectations and industry standards. For example, you might use a line chart to show how your churn rate changes over time, and highlight the periods when it exceeds or falls below your target range.

3. Analyze: The third step is to analyze the data to find the root causes, correlations, and causalities that explain your performance. You should use statistical methods, such as hypothesis testing, regression analysis, and segmentation, to validate your assumptions and test your hypotheses. You should also use qualitative methods, such as customer feedback, user testing, and interviews, to understand the motivations, preferences, and pain points of your customers. For example, you might use a regression analysis to identify the factors that influence your churn rate, and then interview some of your churned customers to understand why they left.

4. Act: The fourth and final step is to act on the data to improve your performance and achieve your goals. You should use experiments, such as A/B testing, multivariate testing, and cohort analysis, to test different variations of your product, service, or marketing strategy, and measure their impact on your KPIs. You should also use feedback loops, such as surveys, reviews, and ratings, to monitor the results of your actions and learn from your successes and failures. For example, you might use an A/B test to compare two different versions of your pricing page, and then use a survey to ask your customers how they feel about your pricing.

By following these four steps, you can use lean analytics to measure your success and optimize your business. Remember that data-driven decision making is not a one-time event, but a continuous cycle that requires constant experimentation and learning. As your business evolves, you should also revisit your KPIs, data sources, and analysis methods, and adapt them to your changing needs and goals.

6. How to understand and adapt to the different business models and goals of startups?

In the dynamic landscape of entrepreneurship, startups emerge with varying visions and operational blueprints. These nascent companies are often characterized by their agility and innovative approaches to solving market problems. However, not all startups are created equal; they differ fundamentally in their business models, growth trajectories, and underlying goals. Understanding these differences is crucial for entrepreneurs as they navigate through the competitive terrain, making informed decisions based on the type of startup they are steering.

1. The Lifestyle Startup: Entrepreneurs who pursue personal passions aligning with their business interests often lead lifestyle startups. These ventures are not necessarily focused on scaling up rapidly but rather on sustaining a particular quality of life. For instance, a surf enthusiast might open a surf shop by the beach, content with maintaining a business that supports their hobby.

2. The small Business startup: Unlike lifestyle startups, these are more traditional businesses like family restaurants or retail shops. They aim for steady income and gradual growth, often funded through personal savings or small business loans. An example would be a local grocery store that serves a community and grows organically.

3. The Scalable Startup: Here, the vision is grander. Founders believe they have a unique solution that will significantly impact the market. Funding for these startups typically comes from venture capitalists, and they aim to grow fast and large. A tech company developing an innovative app with global market potential exemplifies this category.

4. The Buyable Startup: Designed to be sold, these startups build a product or service with the intention of being acquired by a larger company. A tech startup developing a niche piece of software that complements the offerings of bigger players in the industry is a fitting example.

5. The Social Startup: Driven by a cause greater than profit, social startups aim to make a positive impact on society or the environment. They often rely on grants and community support to operate. An enterprise focusing on clean water solutions for underprivileged communities represents this type.

6. The Large Company Startup: These startups are initiated within a large company to develop new products or explore new markets. They have the backing of the company's resources but operate with the agility of a startup. An automotive company creating a division to explore electric vehicles could be considered under this category.

By recognizing the category their startup falls into, entrepreneurs can tailor their strategies, from marketing to analytics, to better suit their business model. For instance, a scalable startup would focus on metrics that reflect user acquisition and market penetration, while a small business startup might concentrate on customer satisfaction and local market share. This alignment of goals and metrics is what Lean Analytics advocates for, ensuring that entrepreneurs are measuring what truly matters for their specific type of startup.

7. How to iterate and improve your product and market fit using feedback loops and pivots?

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One of the most important aspects of lean analytics is the ability to learn from your data and make informed decisions based on it. However, data alone is not enough. You also need a clear process to guide your actions and validate your assumptions. This is where the lean analytics cycle comes in. The lean analytics cycle is a framework that helps you iterate and improve your product and market fit using feedback loops and pivots. It consists of the following steps:

1. define your business model and key metrics. Before you can measure anything, you need to have a clear idea of what you are trying to achieve and how you will measure your progress. A business model canvas is a useful tool to map out your value proposition, customer segments, revenue streams, cost structure, and key resources. A key metric is a single number that reflects the health and performance of your business. It should be aligned with your goals, actionable, comparable, and understandable.

2. Build a minimum viable product (MVP) and test it with real users. An MVP is the simplest version of your product that can deliver value to your customers and test your assumptions. It should be built with the least amount of time and resources possible, and it should focus on the core features that solve the main problem for your target market. You should test your MVP with real users and collect feedback and data on their behavior, preferences, and satisfaction.

3. Analyze your data and identify the gaps. Once you have some data from your MVP, you need to analyze it and see how it compares to your expectations and benchmarks. You should look for patterns, trends, outliers, and anomalies in your data and try to understand the reasons behind them. You should also identify the gaps between your current performance and your desired outcomes, and prioritize the most critical ones to address.

4. Learn from your data and decide whether to persevere, pivot, or perish. based on your data analysis, you need to decide what to do next. You have three options: persevere, pivot, or perish. Persevere means to continue with your current strategy and optimize your product and market fit. Pivot means to change one or more elements of your business model and test a new hypothesis. Perish means to abandon your idea and move on to something else. You should base your decision on the evidence from your data and the feedback from your customers, not on your gut feelings or opinions.

5. Repeat the cycle until you achieve product and market fit. Product and market fit is the stage where your product satisfies a strong market demand and generates sustainable growth. It is not a static state, but a dynamic one that requires constant monitoring and adjustment. You should repeat the lean analytics cycle until you reach product and market fit, and then continue to use it to optimize your performance and explore new opportunities.

Some examples of how the lean analytics cycle can be applied in different contexts are:

- A software startup that wants to create a new social media platform for travelers. They define their key metric as the number of active users per month. They build an MVP that allows users to create profiles, share their travel plans, and connect with other travelers. They test it with a small group of early adopters and collect data on their usage, engagement, and retention. They analyze their data and find out that most users are not interested in sharing their travel plans, but they are very interested in finding local recommendations and tips from other travelers. They decide to pivot and focus on building a feature that allows users to ask and answer questions about their destinations. They test the new feature with a larger group of users and collect more data and feedback. They repeat the cycle until they achieve product and market fit and grow their user base.

- A restaurant that wants to increase its sales and profitability. They define their key metric as the average revenue per customer. They build an MVP that consists of a new menu item that is based on customer feedback and market research. They test it with a limited number of customers and collect data on their sales, costs, and satisfaction. They analyze their data and find out that the new menu item is very popular and profitable, but it also increases the waiting time and the workload for the staff. They decide to persevere and optimize their product and market fit by adjusting their pricing, inventory, and staffing. They test the changes with more customers and collect more data and feedback. They repeat the cycle until they achieve product and market fit and increase their sales and profitability.

8. How to use the best tools and techniques for collecting, visualizing, and analyzing your data?

One of the most important aspects of lean analytics is choosing and using the right tools and techniques to collect, visualize, and analyze your data. There are many options available, but not all of them are suitable for your specific needs and goals. In this section, we will explore some of the best practices and recommendations for selecting and applying the lean analytics tools that will help you measure your success as an entrepreneur.

Some of the factors that you should consider when choosing your lean analytics tools are:

- The type and source of your data. Depending on what kind of data you want to collect and where it comes from, you may need different tools and methods to capture, store, and process it. For example, if you want to track user behavior on your website or app, you may need tools like Google analytics, Mixpanel, or Amplitude. If you want to collect feedback from your customers, you may need tools like SurveyMonkey, Typeform, or Hotjar. If you want to analyze data from your internal systems, such as sales, revenue, or inventory, you may need tools like Excel, Google Sheets, or SQL.

- The level and frequency of your analysis. Depending on how deep and how often you want to analyze your data, you may need different tools and techniques to visualize and interpret it. For example, if you want to get a quick overview of your key metrics and trends, you may need tools like dashboards, charts, or graphs. If you want to explore and discover patterns and insights in your data, you may need tools like pivot tables, filters, or segments. If you want to test and validate your hypotheses and assumptions, you may need tools like A/B testing, experiments, or statistical tests.

- The purpose and audience of your analysis. Depending on what you want to achieve and who you want to communicate with your data, you may need different tools and techniques to present and share it. For example, if you want to inform and educate your team or stakeholders, you may need tools like reports, slides, or newsletters. If you want to persuade and influence your customers or investors, you may need tools like stories, narratives, or pitches. If you want to collaborate and iterate with your peers or partners, you may need tools like comments, annotations, or feedback.

To illustrate how these factors can affect your choice of lean analytics tools, let's look at some examples of different scenarios and situations:

- Scenario 1: You are a solo founder of a new online marketplace that connects local sellers and buyers. You want to measure your product-market fit and customer satisfaction.

- The type and source of your data: You may want to collect data from your website, such as the number of visitors, sign-ups, listings, transactions, and reviews. You may also want to collect data from your customers, such as their demographics, preferences, needs, and feedback. You may need tools like Google Analytics, Mixpanel, SurveyMonkey, and Hotjar to capture and store this data.

- The level and frequency of your analysis: You may want to analyze your data on a daily or weekly basis to monitor your key metrics and indicators, such as your conversion rate, retention rate, churn rate, and net promoter score. You may also want to analyze your data on a monthly or quarterly basis to explore and discover your customer segments, personas, journeys, and pain points. You may need tools like dashboards, charts, graphs, pivot tables, filters, and segments to visualize and interpret this data.

- The purpose and audience of your analysis: You may want to use your data to inform and educate yourself and your potential co-founders, advisors, or mentors. You may also want to use your data to persuade and influence your potential customers, partners, or investors. You may need tools like reports, slides, stories, narratives, and pitches to present and share this data.

- Scenario 2: You are a co-founder of a growing SaaS company that provides a cloud-based crm solution for small businesses. You want to measure your revenue growth and customer loyalty.

- The type and source of your data: You may want to collect data from your app, such as the number of users, accounts, features, and integrations. You may also want to collect data from your internal systems, such as your sales, revenue, costs, and profits. You may need tools like Amplitude, SQL, Excel, and Google Sheets to capture and store this data.

- The level and frequency of your analysis: You may want to analyze your data on a monthly or quarterly basis to monitor your key metrics and goals, such as your monthly recurring revenue, customer lifetime value, customer acquisition cost, and return on investment. You may also want to analyze your data on a yearly or bi-yearly basis to test and validate your business model, strategy, and assumptions. You may need tools like charts, graphs, A/B testing, experiments, and statistical tests to visualize and interpret this data.

- The purpose and audience of your analysis: You may want to use your data to inform and educate your team, stakeholders, and board members. You may also want to use your data to collaborate and iterate with your peers, partners, and vendors. You may need tools like slides, newsletters, comments, annotations, and feedback to present and share this data.

As you can see, there is no one-size-fits-all solution for lean analytics tools. You need to choose and use the tools and techniques that best suit your data, analysis, and purpose. By doing so, you will be able to collect, visualize, and analyze your data effectively and efficiently, and use it to measure your success as an entrepreneur.

9. How to use Lean Analytics to achieve success as an entrepreneur?

In the journey of entrepreneurship, the final stride is not merely about crossing the finish line; it's about understanding the race you've run. The metrics and data you've meticulously tracked now serve as the compass for future ventures. They are not just numbers but narratives that tell you where you've been successful and where you've faltered.

1. Identify the One Metric That Matters (OMTM): Your OMTM is the single most important metric at any given stage of your business. For a new social media platform, it might be daily active users, while for a SaaS business, monthly recurring revenue could be key. For instance, a subscription box service might focus on customer churn rate to gauge retention success.

2. Set Actionable Goals: Lean Analytics is about setting clear, actionable goals. If your OMTM is customer acquisition cost, set a specific target based on industry benchmarks and strive to improve it. A mobile app developer, for example, could aim to reduce the acquisition cost per user by 20% over three months through targeted ad campaigns.

3. Segment Data for Insight: Break down your data into segments to uncover insights. If you're running an e-commerce site, analyze customer behavior by traffic source or by new versus returning customers. This can reveal that customers from organic search spend more per visit than those from paid ads, guiding your marketing spend.

4. Pivot or Persevere: Use your analytics to decide whether to pivot or persevere. If a feature in your app isn't engaging users as expected, it's time to consider a change. When a food delivery startup noticed that users preferred ordering from curated menus, they pivoted to offer a more streamlined selection, which increased order volume.

5. Benchmark Against Competitors: Understand how you stack up against competitors. If you're a cloud storage company and your data shows a higher churn rate than industry standards, it's a signal to investigate and improve your customer retention strategies.

6. Iterate Rapidly: Implement changes quickly and measure the results. A/B testing different landing pages can lead to improved conversion rates. An online retailer might test two versions of a product page to see which one leads to more sales.

7. Scale with Confidence: Once you've found what works, scale it. If a particular referral program is bringing in high-value customers at a low cost, invest more resources into it. A tech startup, after finding that their referral program yielded a high lifetime value per customer, doubled down on this channel for growth.

By embracing these principles, entrepreneurs can navigate the complex waters of startup analytics, turning data into a strategic asset. It's about making informed decisions that are backed by evidence, not just intuition. The culmination of this process is not just in achieving success but in understanding how to replicate it, time and time again.

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