Talent Density

Working at a Big Tech company means joining an environment that systematically attracts top-tier talent due to its high compensation and prestige. This creates a unique ecosystem where growth, learning, and problem-solving are significantly accelerated.

- **Talent Follows Compensation**: Competitive pay naturally draws in highly skilled engineers. While exceptions exist, the vast majority of engineers in Big Tech are above-average in both technical skills and motivation.
- **Noticeable Culture Shift**: Engineers often report that, compared to previous jobs, colleagues in Big Tech care more about code quality, engineering rigor, and continuous learning.
- **High Learning Potential**: Being surrounded by experienced and skilled engineers ensures a constant opportunity to learn. Teams often include individuals who are more experienced, creating a strong environment for mentorship and knowledge transfer.
- **Collective Wisdom and Problem Solving**: The scale and maturity of these organizations mean that most technical challenges have been encountered, and solved, before. Success often comes from leveraging internal networks to find engineers who’ve already navigated similar problems.
- **Standing on Shoulders of Giants**: Engineers thrive by observing and applying best practices from tech leads and staff engineers, accelerating their own effectiveness and impact through this ecosystem of shared expertise.

Competitive Pay

Big Tech companies are widely known for offering extremely competitive compensation packages, which is often one of the primary reasons engineers aim to work at these firms. This segment outlines both personal anecdotes and structural strategies that explain why compensation in Big Tech is so high.

- **Top-Tier Compensation Strategy**: Meta’s compensation goal, for example, is to pay in the 90th percentile—more than 90% of other tech companies—making total compensation significantly higher for engineers who make the transition into Big Tech.
- **Drastic Pay Increases**: It's common for engineers switching from smaller or non-FAANG companies to experience dramatic increases in pay - often as much as 50–100%.
- **High Cost of Living Justification**: These lucrative salaries help offset the cost of living in expensive tech hubs like Silicon Valley, New York City, and Seattle, where basic housing or property ownership can be out of reach without this level of income.
- **Prestige + Pay Combo**: Many engineers underestimate the financial upside of Big Tech and initially assume a 20–30% increase is generous. In reality, the combination of prestige and substantial pay is a powerful draw.

Big Tech companies are widely known for offering extremely competitive compensation packages, which is often one of the primary reasons engineers aim to work at these firms. This segment outlines both personal anecdotes and structural strategies that explain why compensation in Big Tech is so high.

- **Top-Tier Compensation Strategy**: Meta’s compensation goal, for example, is to pay in the 90th percentile—more than 90% of other tech companies—making total compensation significantly higher for engineers who make the transition into Big Tech.

- **Drastic Pay Increases**: It's common for engineers switching from smaller or non-FANG companies to experience dramatic increases in pay—often as much as 50–100%. Even the speaker was surprised by the magnitude of the raise upon joining Meta.

- **High Cost of Living Justification**: These lucrative salaries help offset the cost of living in expensive tech hubs like Silicon Valley, New York City, and Seattle, where basic housing or property ownership can be out of reach without this level of income.

- **Historical Offers Data**: In 2014, new grad offers ranged from $164K to $270K annually from Big Tech firms—Google, Facebook, and others. Salaries have only increased since then, due to ongoing talent wars and inflation.

- **Prestige + Pay Combo**: Many engineers underestimate the financial upside of Big Tech and initially assume a 20–30% increase is generous. In reality, the combination of prestige and substantial pay is a powerful draw.

Big Tech Is Not A Monolith

Although Big Tech is often discussed as if it's a uniform experience, that assumption doesn't hold up under scrutiny. The lesson clarifies that these companies, and even teams within them, can differ dramatically in culture, structure, and day-to-day work.

- **Big Tech is not a monolith**: Companies like Google, Amazon, Meta, Apple, and Microsoft may appear similar, but each has distinct norms and quirks. For example, Apple and Amazon differ significantly in operating style.
- **Team-level variation is substantial**: Within any large company - especially one with thousands of engineering teams—there’s huge internal variability. At Google, for instance, it’s unrealistic to expect all teams to operate the same way.
- **Meta as a case study in divergence**: Meta operates almost like four separate companies due to its major acquisitions (Instagram, WhatsApp, Oculus), each with its own leadership structure and internal culture.
- **Caveat for the audience**: The content that follows reflects common experiences shared by 70–80% of Big Tech engineers, but there will be outliers. Some parts may resonate; others may not - and that’s normal.

What Exactly Is Big Tech?

While many people associate "Big Tech" with the largest household names in the industry, the definition can, and should, be broader. This lesson provides a useful lens for identifying companies that operate like Big Tech, even if they’re not part of the traditional FAANG group.

- **Traditional Big Tech**: Commonly refers to the largest companies by market cap - *Microsoft, Google, Amazon, Apple, and Meta*. Sometimes Netflix is included (as in “FAANG”), but it's structurally different, e.g. Netflix historically had no engineering levels and treated everyone as senior engineers.
- **Critique of the FAANG Acronym**: Netflix is singled out as not entirely representative of "Big Tech" norms, being smaller in scale and structurally more unique than the others.
- **Broader Definition**: Companies with *$50B+ market cap* and *2,500+ engineers*, along with similar performance expectations, leveling systems, and engineering culture as the Big Five, should also be considered Big Tech. This includes companies like *Uber, Airbnb, and Stripe*.
- **Why This Matters**: Focusing only on the top 5–6 companies can be limiting. Many other companies now offer equivalent opportunities in terms of compensation, growth potential, and engineering culture, especially as they reach scale.

This broader perspective is helpful when evaluating job options and team culture - not all Big Tech companies are the usual suspects.

The Path To Career Success

Before diving into the pros and cons of Big Tech, we introduce two foundational career frameworks that offer different approaches to growth. These models help structure decision-making as one considers roles at large tech companies.

- **Explore vs. Exploit (David Epstein’s “Range”)**: In the early career stages, it’s valuable to gather a wide range of experiences—trying different roles or disciplines—to discover personal strengths and interests. This sampling period can lead to deeper, more informed commitments later.
- **Cultivate Passion through Skill (Cal Newport’s “So Good They Can’t Ignore You”)**: Rather than waiting to find a passion, pick a field and become excellent at it. Passion often emerges through mastery and impact in a chosen domain.
- Both frameworks offer valid paths and should be selected based on personal goals and life stage. The talk later connects these frameworks to the realities of working in Big Tech.

The AI Frontier: Get Paid To Build Smarter LLMs

The future of AI is being built by a handful of companies: OpenAI, Anthropic, and DeepMind, among others. One of the core ingredients for improving the performance of LLMs is the use of reinforcement learning from human feedback (RLHF).

Join us for a live conversation with Mercor's cofounder and CTO, [**Adarsh Hiremath**](https://www.linkedin.com/in/adarsh-h-623941204) hosted by Taro's cofounder and CEO, [**Rahul Pandey**](https://www.linkedin.com/in/rpandey1234/). Mercor is a key partner to enable RLHF for many frontier model companies. As part of this, Mercor offers 1000s of part-time, remote-friendly roles with competitive compensation.

This is a new type of white-collar work that involves debugging, validating, and creating scenarios for LLMs. As the capabilities of models improve, the expertise required to train models keeps increasing. One of the highest-priority areas is around coding, which means Software Engineers have a special role to play in this type of work.

This event explains the pros and cons of AI lab gig work, what exactly the work entails, and how much you can earn.

Some topics we’ll cover:

- Typical hourly rates and the compensation model
- Project-to-project variability
- Unique challenges of remote collaboration in this type of work
- What does the day-to-day look like with this type of work?
- How does this work impact the development of LLMs and AI agents?
- How will this work evolve over the next 5 years?

There will also be time for open Q&A from the audience.

Mercor is an AI-powered recruiting platform that has become one of the largest sources for human labor for foundation model companies. Mercor has raised more than $100 million dollars and is located in San Francisco, CA. Explore [**relevant jobs on Mercor here**](https://work.mercor.com/?referralCode=91aab11f-ce55-40b3-9c2d-f74576cac28f) (100+ Software Engineering jobs available).

Decoding the One Big Beautiful Bill Act for Tech Workers

New tax law, same tax code for tech workers… mostly.\
\
President Trump signed the One Big Beautiful Bill Act into law on July 4, 2025.\
\
In terms of the impact to your taxes, this bill largely maintains the status quo by making permanent most of the tax changes introduced on a temporary basis through the Tax Cuts and Jobs Act in 2017.\
\
It does, however, add a few new wrinkles to the tax code.\
\
I’m hosting a live webinar next week to break it all down for tech professionals like you.\
\
🎙️ Webinar: Decoding the One Big Beautiful Bill Act for Tech Workers\
🗓️ Date & Time: July 24, 2025 from 2-2:45 PM Pacific Time\
📍 Where: Live on Zoom\
💡 Especially relevant for:\
👉 Anyone in a high-income bracket that pays a lot in state & local tax\
👉 Folk planning to make Roth conversions in the next few years\
👉Those considering retiring at 65+ in the next few years\
👉 Anyone considering taking a career break or on an ACA plan\
\
We’ll cover:\
\
✅ Changes to the deductibility of state and local taxes\
✅ How the new bill affects ISO exercises and your likelihood of paying AMT\
✅ What's up with Section 174?\
✅ Changes to QSBS\
✅ "No tax on tips/overtime"\
✅ Deductibility of new U.S. car loan interest\
✅ The new "Trump account"\
✅ Impacts to Medicaid\
✅ The new bonus deduction for seniors\
✅ Updates to clean energy and EV credits\
✅ How the new rules impact ACA health insurance subsidies

Machine Learning System Design Ask & Learn

**Event description**

Join us remotely for an interactive Q&A with Ilya Reznik—Founder of MLEPath, veteran ML engineer with nearly 15 years at Meta, Twitter, and Adobe, and interviewer of hundreds of real-world ML system design (MLSD) interviews (plus nearly 1,000 mock sessions in the last two years). Whether you’re tackling your first MLSD interview or leveling up for senior roles.

- **Core frameworks & approaches**: how to structure your end-to-end design, balance scalability, latency, cost, and reliability
- **Key components & trade-offs**: data pipelines, model serving, feature stores, monitoring, and A/B testing
- **Real-world insights**: pitfalls Ilya’s seen candidates fall into—and what top performers do differently
- **Live feedback**: get tailored advice on designing everything from recommendation systems to real-time anomaly detection

Perfect for ML engineers aiming to land roles at FAANG, startups, or anywhere that demands rock-solid system design chops. Come ready with your toughest questions—and leave with a clear, actionable roadmap for your next MLSD interview.

How To Properly Measure Your Data

In this lesson, we explore the essential discipline of **measuring data effectively**, a foundational step in the DMAIC process. Drawing from Amazon’s own journey, we explain why rigorous data management, real-time reporting, and a culture of confronting the brutal facts are key to achieving operational excellence.

### Key Takeaways:

- **We built a disciplined system for data measurement**: Measurement goes far beyond selecting metrics—it requires clean data, reliable pipelines, centralized data warehouses, governance, security, and standardized reporting tools like Tableau or Looker.
- **We invested in an unbiased analytics culture**: Jeff Bezos asked our CFO to form an independent analytics team whose sole job was to uncover the truth, not protect orgs or executives. These analysts had both financial and operational alignment but prioritized integrity in reporting.
- **We created mechanisms to confront reality**: Inspired by Churchill and Jim Collins, we institutionalized a culture of *brutal facts*—establishing metrics and systems that allowed leadership to face problems head-on, rather than hide or distort them.
- **We operationalized this with the Weekly Business Review (WBR)**: The WBR report was auto-generated by 7 a.m. every Monday, standardized, and packed with hundreds of metrics—enabling every department to identify trends, anomalies, and action items before the company-wide WBR each Wednesday.
- **We learned that slow is fast**: Building accurate, automated, real-time measurement systems took time and leadership buy-in. But in the long run, these systems helped us move faster and make better decisions at scale.

Ultimately, this lesson shows that **data measurement is not a tech problem—it’s a leadership imperative**. By treating it as a strategic function, we created a system that helped Amazon scale with confidence and clarity.

To dive deeper into Input Metrics Mastery and learn more from Bill and Colin, follow these links:

- Take the full course at their website: <https://workingbackwards.com/>
- Buy the book: <https://www.amazon.com/Working-Backwards-Insights-Stories-Secrets/dp/1250267595>
- Bill's LinkedIn: <https://www.linkedin.com/in/bill-carr/>
- Colin's LinkedIn: <https://www.linkedin.com/in/colinbryar/>

Amazon Case Study: Detail Pages

In this lesson, we apply everything we've learned about input metrics by walking through a real-world case from Amazon—how we evolved our **selection metric** to better align with customer needs. This story illustrates the complexity, rigor, and iteration involved in making metrics truly actionable and impactful.

### Key Takeaways:

- **We started by identifying strategic input metrics**: Inspired by Amazon’s **growth flywheel**, we identified controllable levers—like low prices, broad selection, and fast delivery—that directly improved the customer experience and fueled long-term growth.
- **We didn’t wait for perfect metrics to begin**: We initially used simple metrics like the number of product detail pages to represent selection, even if they were imperfect. Early measurement got teams moving in the right direction.
- **We learned through missteps**: Our first metric for selection encouraged teams to add lots of SKUs—regardless of demand. Sales didn’t grow, and excess inventory piled up. This forced us to iterate.
- **We refined our metric to reflect actual customer experience**: Over time, we evolved to more nuanced metrics like **detail page views for in-stock items**, and ultimately to **Demand-Weighted Coverage FastTrack In-Stock Percentage (DWC FT %)**—a metric that captured whether customers could *find*, *want*, and *get* an item quickly.
- **We aligned goals with customer value**: Category managers were now measured by how well their products met real customer demand and delivery expectations—not just raw catalog size.
- **We replaced estimates with precise real-time measurements**: When anecdotes didn’t match our metric outcomes, we realized the problem wasn’t in customer feedback—it was in how we were measuring. We moved from snapshot-based approximations to real-time software tracking, which required serious engineering investment.
- **We treated metric-building as an ongoing process**: Measurement accuracy improved over years of trial and error, and nearly every customer-facing metric eventually transitioned from rough proxies to live calculations.

### To dive deeper into Input Metrics Mastery and learn more from Bill and Colin, follow these links:

- Take the full course at their website: <https://workingbackwards.com/>

- Buy the book: <https://www.amazon.com/Working-Backwards-Insights-Stories-Secrets/dp/1250267595>

- Bill's LinkedIn: <https://www.linkedin.com/in/bill-carr/>

- Colin's LinkedIn: <https://www.linkedin.com/in/colinbryar/>

Weekly Business Review

###### In this lesson, we introduce the **Weekly Business Review (WBR)** as Amazon’s core mechanism for consistently analyzing, improving, and controlling business performance. This structured, weekly ritual helped us align leadership around metrics, dive deep into customer experience, and create a culture of truth-seeking and operational rigor.

### Key Takeaways:

- **We used the WBR to bridge strategy and detail**: Contrary to conventional advice, we didn’t choose between high-level strategy and operational detail—we embraced both. The WBR let senior leaders dive deep while still steering big-picture strategy.
- **We built the WBR around the customer journey**: Our report flowed from financials to marketing, browsing, shopping, fulfillment, and customer support. Each department leader owned and explained their metrics—not analysts—ensuring accountability and depth.
- **We started before everything was perfect**: We launched the WBR even before our metrics were fully refined. By including many metrics upfront, we learned over time which ones provided signal and which were noise.
- **We continuously refined our metrics**: In each WBR meeting, we didn’t just review results—we asked whether we were measuring the right things and how to improve clarity and relevance of our metrics.
- **We made it a disciplined habit**: Reports were automated and delivered every Monday by 7 a.m., enabling prep ahead of the all-company WBR. This rhythm created organizational focus, enabled early issue detection, and aligned everyone around the brutal facts of performance.

The WBR was not just a meeting—it was a **weekly dose of operational truth** that made Amazon more customer-obsessed, data-informed, and execution-focused.

To dive deeper into Input Metrics Mastery and learn more from Bill and Colin, follow these links:

- Take the full course at their website: <https://workingbackwards.com/>
- Buy the book: <https://www.amazon.com/Working-Backwards-Insights-Stories-Secrets/dp/1250267595>
- Bill's LinkedIn: <https://www.linkedin.com/in/bill-carr/>
- Colin's LinkedIn: <https://www.linkedin.com/in/colinbryar/>

The Do's and Don'ts For Good Metrics

Here's the exercise worksheet: [Metrics Template and Worksheet Combined (B2C + B2B)](https://firebasestorage.googleapis.com/v0/b/tech-career-growth.appspot.com/o/courses%2Fslides%2FMetrics%20Template%20and%20Worksheet%20Combined%20$B2C%20%2B%20B2B$.xlsx?alt=media&token=3951bfd7-ef9e-4c81-9245-80043a4973d0)

In this wrap-up, we assign a powerful, hands-on homework exercise: to generate a **comprehensive set of input and output metrics** that can answer three foundational business questions. This process, built on the tools and frameworks we've covered, is the first real step in building a metrics-driven, customer-obsessed organization.

### Key Takeaways:

- **We focus on three key questions**:

  1. How did our business do last week?

  2. What was the customer experience like last week?

  3. Are we on track to hit our goals?

- **We use four core methods to identify input metrics**:

  1. **Metrics Mapping** – Drill down from business goals to uncover metric drivers.

  2. **Customer Experience Mapping** – Map and measure every customer interaction.

  3. **Customers** – Mine support data and engage with end users directly.

  4. **Defects** – Use root cause analysis (like CoEs) to find and track preventable errors.

- **We cast a wide net—hundreds of metrics is a good start**: This is not about picking the “best” metrics yet. We brainstorm 200–300 across all functions without concern for whether they’re inputs or outputs, or even if they can be measured immediately.

- **We avoid vague or compound metrics**: Good metrics are discrete, observable, and repeatable by different people or systems. Avoid averages where possible—percentiles and RMS error are often better indicators of variation and performance.

- **We assign priorities and assess data readiness**: After brainstorming, we tag each metric as Priority 1, 2, or 3 and indicate whether the data is currently available. This helps build a phased implementation plan for metrics collection and review.

This assignment launches the foundational work needed to operationalize a **weekly business review (WBR)**. It sets the stage for measurement, analysis, improvement, and control—ensuring our teams are focused on the right inputs to drive the right outcomes.

To dive deeper into Input Metrics Mastery and learn more from Bill and Colin, follow these links:

- Take the full course at their website: <https://workingbackwards.com/>
- Buy the book: <https://www.amazon.com/Working-Backwards-Insights-Stories-Secrets/dp/1250267595>
- Bill's LinkedIn: <https://www.linkedin.com/in/bill-carr/>
- Colin's LinkedIn: <https://www.linkedin.com/in/colinbryar/>

Learning From Mistakes

In this section, we reflect on how **mistakes and operational defects** became valuable sources of new input metrics at Amazon. Through a disciplined **Correction of Errors (CoE)** process, we turned setbacks into opportunities for learning, system improvement, and metric discovery.

### Key Takeaways:

- **We embraced mistakes as learning opportunities**: We openly discussed errors and failures—not to assign blame, but to understand what went wrong and how to prevent it next time.
- **We asked predictive questions**: For every defect, we asked: *What metric could have predicted or prevented this?* Often, the answer revealed a missing input metric that we needed to start tracking.
- **We built systems to measure what we were missing**: Once we identified a critical input metric, we prioritized building the tools and infrastructure to capture it—and ensured it was reviewed in our Weekly Business Review (WBR).
- **We used a structured CoE process**: The CoE included five steps—identifying the error, documenting and responding, root cause analysis (often using the “Five Whys”), developing corrective actions, and then implementing and monitoring those changes.
- **We integrated input metrics into operational reviews**: Many of the metrics that emerged from the CoE process became part of our regular metric reviews, helping us catch future problems early and improve reliability across systems.

This practice helped us create a culture where **defects became a trigger for innovation and deeper operational control**, not just patchwork fixes.

To dive deeper into Input Metrics Mastery and learn more from Bill and Colin, follow these links:

- Take the full course at their website: <https://workingbackwards.com/>
- Buy the book: <https://www.amazon.com/Working-Backwards-Insights-Stories-Secrets/dp/1250267595>
- Bill's LinkedIn: <https://www.linkedin.com/in/bill-carr/>
- Colin's LinkedIn: <https://www.linkedin.com/in/colinbryar/>

Get Hands-On Experience With Frontline And Customers

In addition to data and structured methods, **immersive, first-hand experiences and direct customer research** gave us some of the most impactful input metrics and ideas at Amazon. These approaches helped ground our innovation efforts in real customer needs and operational realities.

### Key Takeaways:

- **We gained critical insight from frontline experience**: During Amazon’s early years, leadership—including Jeff Bezos—regularly worked in fulfillment centers and answered customer service calls during peak seasons. These visceral experiences helped us better understand operational pain points and identify new input metrics.
- **We institutionalized hands-on learning**: The value was so great that we made it a requirement—every Amazon employee spends time in frontline roles each year. This creates empathy, sharpens customer focus, and surfaces measurable opportunities for improvement.
- **We used customer research to drive innovation**: Surveys, focus groups, and third-party data sources gave us clear, actionable feedback. A simple survey on why customers didn’t shop more often revealed four key barriers—shipping cost, delivery speed, selection, and price—which directly shaped the creation of Amazon Prime and our long-term priorities.
- **We traced key customer complaints back to inputs**: From that early research, we began measuring and improving core inputs like in-stock rate, selection breadth, delivery speed, and pricing—cementing them in our flywheel as long-term business drivers.
- **We leveraged sales and customer interactions in B2B contexts**: Especially in business-to-business scenarios, we worked closely with sales teams and met customers in person to gather input-rich feedback, helping us refine offerings and discover metrics specific to client pain points.

Together, these methods reinforce the importance of **direct, empathetic engagement with customers and operations**—not just to build understanding, but to generate input metrics that we can track, improve, and build entire services around.

To dive deeper into Input Metrics Mastery and learn more from Bill and Colin, follow these links:

- Take the full course at their website: <https://workingbackwards.com/>
- Buy the book: <https://www.amazon.com/Working-Backwards-Insights-Stories-Secrets/dp/1250267595>
- Bill's LinkedIn: <https://www.linkedin.com/in/bill-carr/>
- Colin's LinkedIn: <https://www.linkedin.com/in/colinbryar/>

Mine Customer Support Data

We expand beyond metrics mapping and customer experience mapping by introducing **additional, nuanced techniques** to help us continuously refine our input metrics. These customer- and defect-driven approaches give us real-world signals that are often overlooked—but are rich with opportunities for improvement.

### Key Takeaways:

- **We believe customer and shareholder interests are aligned**: At Amazon, we were driven by the conviction that obsessing over customers naturally serves the long-term interests of shareholders. This philosophy shaped our approach to metrics and improvement.
- **We treat every customer interaction as a source of insight**: Whether the "customer" is a buyer on Amazon or an internal team we support, we use their feedback to uncover meaningful input metrics that help us improve service and outcomes.
- **We mine customer support data as a diagnostic tool**: We tracked the top reasons customers contacted support—questions like “Where’s my stuff?” or complaints like “You sent the wrong item”—and used these to create new metrics such as “the switcheroo,” which measured item mix-ups.
- **We made problem frequency visible and actionable**: These contact reasons and metrics were reviewed weekly in our WBR, ensuring we remained focused on resolving the most impactful customer issues.
- **We talked directly to customer-facing teams**: We routinely engaged with customer support representatives to gather firsthand insights about recurring pain points by category—adding human intelligence to our data-driven approach.

These additional techniques may not generate as many metrics as mapping exercises, but they provide powerful context and depth to our understanding of what truly matters to customers.

To dive deeper into Input Metrics Mastery and learn more from Bill and Colin, follow these links:

- Take the full course at their website: <https://workingbackwards.com/>
- Buy the book: <https://www.amazon.com/Working-Backwards-Insights-Stories-Secrets/dp/1250267595>
- Bill's LinkedIn: <https://www.linkedin.com/in/bill-carr/>
- Colin's LinkedIn: <https://www.linkedin.com/in/colinbryar/>

Amazon Metrics Map Example

In this lesson, we walk through **two core techniques—Metrics Mapping and Customer Experience Mapping**—that helped us systematically discover and define the input metrics that truly matter. These methods gave us a more granular, customer-focused way to understand and improve our business performance at Amazon.

### Key Takeaways:

- **We used Metrics Mapping to break down strategic goals**: Starting with broad objectives like lowering cost structure, we recursively drilled down to discover the specific drivers (e.g., shipping cost, pick cost, forecast accuracy) and converted them into measurable input metrics.
- **We cast a wide net during brainstorming**: In the early stages of metrics mapping, we didn’t worry about whether we could measure something yet or whether it was immediately useful. Our priority was to be comprehensive.
- **We built Customer Experience Maps to capture every user touchpoint**: From site entry to search, checkout, and delivery, we identified every customer interaction and defined metrics for speed, quality, and cost—measured both from the customer's and Amazon’s point of view.
- **We stayed grounded in the customer’s perspective**: Instead of focusing on internal outcomes like revenue per page view, we measured how intuitive, fast, and helpful each experience was for the customer, ensuring our metrics aligned with long-term value creation.
- **We operationalized customer obsession**: These techniques gave us a structured, measurable way to uphold our customer-obsessed culture. Other companies have found these frameworks to be transformative in how they assess and improve their own customer experience and operations.

To dive deeper into Input Metrics Mastery and learn more from Bill and Colin, follow these links:

- Take the full course at their website: <https://workingbackwards.com/>
- Buy the book: <https://www.amazon.com/Working-Backwards-Insights-Stories-Secrets/dp/1250267595>
- Bill's LinkedIn: <https://www.linkedin.com/in/bill-carr/>
- Colin's LinkedIn: <https://www.linkedin.com/in/colinbryar/>

Control

In this final phase, we ensure that the gains achieved through improvements don’t erode over time. Control is about sustaining operational excellence, detecting deviations early, and building mechanisms—both human and automated—that keep the system performing at its best.

### Key Takeaways:

- **We implement control mechanisms to preserve gains**: Once we achieve desired service levels, we don’t assume they’ll stay that way. We actively maintain them through tools like regular audits, alarms, and ongoing data validation.
- **The Weekly Business Review (WBR) reinforces control**: Our WBR report includes 200–300 metrics. We review only the outliers, but by tracking all key indicators weekly, we maintain vigilance and are able to quickly detect when something goes off-course.
- **We rely on automation for precision and scalability**: From real-time alarms to machine-driven decision-making, we automate wherever possible. These systems are monitored to ensure they perform within tolerance thresholds.

To dive deeper into Input Metrics Mastery and learn more from Bill and Colin, follow these links:

- Take the full course at their website: <https://workingbackwards.com/>
- Buy the book: <https://www.amazon.com/Working-Backwards-Insights-Stories-Secrets/dp/1250267595>
- Bill's LinkedIn: <https://www.linkedin.com/in/bill-carr/>
- Colin's LinkedIn: <https://www.linkedin.com/in/colinbryar/>

Building A Metrics Map

In this lesson, we explore **practical techniques** for identifying the input metrics that matter most. Through methods like **metrics mapping** and **customer experience mapping**, we gain a clearer, more quantitative view of what drives our business forward—especially from the customer’s perspective.

### Key Takeaways:

- **We prioritize input metrics and customer interactions**: Too often, businesses rely heavily on output metrics. We recommend looking deeper into input metrics—particularly those tied to customer behavior and experience.
- **Metrics Mapping helps us discover drivers of key metrics**: We start with a business goal or known metric, then recursively ask what drives it and how we can measure those drivers. This technique reveals a broad and useful set of input metrics.
- **The process is recursive and comprehensive**: We drill down from each metric or goal until we can’t go any further, building an extensive network of quantifiable drivers. Repeating metrics across branches is fine—what matters is completeness.
- **The end goal is a flat, exhaustive list of metrics**: While the mapping exercise may begin as a hierarchy, we ultimately compile all the discovered metrics into a single, non-hierarchical list for practical use and monitoring.
- **Finding input metrics is an ongoing process**: This isn’t a one-time task. We continuously revisit and expand our metric set as our business, goals, and customer needs evolve.

To dive deeper into Input Metrics Mastery and learn more from Bill and Colin, follow these links:

- Take the full course at their website: <https://workingbackwards.com/>
- Buy the book: <https://www.amazon.com/Working-Backwards-Insights-Stories-Secrets/dp/1250267595>
- Bill's LinkedIn: <https://www.linkedin.com/in/bill-carr/>
- Colin's LinkedIn: <https://www.linkedin.com/in/colinbryar/>

Improve

In the Improve phase, we focus on **deploying resources and initiatives** that shift our input metrics in the right direction, knowing that this in turn drives our desired output results. Improvement at Amazon has always been a cycle of innovation, experimentation, and rigorous measurement.

### Key Takeaways:

- **We prioritize inputs to drive results**: We align our teams, projects, and resources around improving leading indicators—our input metrics—which ultimately influence lagging outputs like sales and customer retention.
- **Click to Deliver Time became a key input metric**: We tracked every step of the fulfillment process and launched targeted initiatives to speed up delivery, knowing it directly impacted customer satisfaction and engagement.
- **We scaled operations through iterative improvements**: Over two decades, we grew our global delivery infrastructure—fulfillment centers, delivery stations, air cargo, and driver networks—through thousands of iterative changes, many of which were experiments that included both successes and failures.
- **We formalized improvement with the Working Backwards PR/FAQ process**: This mechanism ensures we innovate from customer needs, forcing teams to start with a press release and FAQ to define how a proposed improvement solves real problems.
- **Improvement and analysis form a feedback loop**: Every improvement is followed by measurement and analysis. This iterative cycle lets us reinforce what works and eliminate what doesn’t, ensuring continuous refinement of our systems.

To dive deeper into Input Metrics Mastery and learn more from Bill and Colin, follow these links:

- Take the full course at their website: <https://workingbackwards.com/>
- Buy the book: <https://www.amazon.com/Working-Backwards-Insights-Stories-Secrets/dp/1250267595>
- Bill's LinkedIn: <https://www.linkedin.com/in/bill-carr/>
- Colin's LinkedIn: <https://www.linkedin.com/in/colinbryar/>

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