Only two of the following three options can be achieved: consistency, availability, and partition tolerance

Someone told me that growing from 30 to 60 is the biggest adjustment for a team or business.

I remember thinking, That's random. Each company is unique. I've seen teams of all types confront the same issues during development periods. With new enterprises starting every year, we should be better at navigating growing difficulties.

As a team grows, its processes and systems break down, requiring reorganization or declining results. Why always? Why isn't there a perfect scaling model? Why hasn't that been found?

The Three Things Productive Organizations Must Have

Any company should be efficient and productive. Three items are needed:

First, it must verify that no two team members have conflicting information about the roadmap, strategy, or any input that could affect execution. Teamwork is required.

Second, it must ensure that everyone can receive the information they need from everyone else quickly, especially as teams become more specialized (an inevitability in a developing organization). It requires everyone's accessibility.

Third, it must ensure that the organization can operate efficiently even if a piece is unavailable. It's partition-tolerant.

From my experience with the many teams I've been on, invested in, or advised, achieving all three is nearly impossible. Why a perfect organization model cannot exist is clear after analysis.

The CAP Theorem: What is it?

Eric Brewer of Berkeley discovered the CAP Theorem, which argues that a distributed data storage should have three benefits. One can only have two at once.

The three benefits are consistency, availability, and partition tolerance, which implies that even if part of the system is offline, the remainder continues to work.

This notion is usually applied to computer science, but I've realized it's also true for human organizations. In a post-COVID world, many organizations are hiring non-co-located staff as they grow. CAP Theorem is more important than ever. Growing teams sometimes think they can develop ways to bypass this law, dooming themselves to a less-than-optimal team dynamic. They should adopt CAP to maximize productivity.

Path 1: Consistency and availability equal no tolerance for partitions

Let's imagine you want your team to always be in sync (i.e., for someone to be the source of truth for the latest information) and to be able to share information with each other. Only division into domains will do.

Numerous developing organizations do this, especially after the early stage (say, 30 people) when everyone may wear many hats and be aware of all the moving elements. After a certain point, it's tougher to keep generalists aligned than to divide them into specialized tasks.

In a specialized, segmented team, leaders optimize consistency and availability (i.e. every function is up-to-speed on the latest strategy, no one is out of sync, and everyone is able to unblock and inform everyone else).

Partition tolerance suffers. If any component of the organization breaks down (someone goes on vacation, quits, underperforms, or Gmail or Slack goes down), productivity stops. There's no way to give the team stability, availability, and smooth operation during a hiccup.

Path 2: Partition Tolerance and Availability = No Consistency

Some businesses avoid relying too heavily on any one person or sub-team by maximizing availability and partition tolerance (the organization continues to function as a whole even if particular components fail). Only redundancy can do that. Instead of specializing each member, the team spreads expertise so people can work in parallel. I switched from Path 1 to Path 2 because I realized too much reliance on one person is risky.

What happens after redundancy? Unreliable. The more people may run independently and in parallel, the less anyone can be the truth. Lack of alignment or updated information can lead to people executing slightly different strategies. So, resources are squandered on the wrong work.

Path 3: Partition and Consistency "Tolerance" equates to "absence"

The third, least-used path stresses partition tolerance and consistency (meaning answers are always correct and up-to-date). In this organizational style, it's most critical to maintain the system operating and keep everyone aligned. No one is allowed to read anything without an assurance that it's up-to-date (i.e. there’s no availability).

Always short-lived. In my experience, a business that prioritizes quality and scalability over speedy information transmission can get bogged down in heavy processes that hinder production. Large-scale, this is unsustainable.

Accepting CAP

When two puzzle pieces fit, the third won't. I've watched developing teams try to tackle these difficulties, only to find, as their ancestors did, that they can never be entirely solved. Idealized solutions fail in reality, causing lost effort, confusion, and lower production.

As teams develop and change, they should embrace CAP, acknowledge there is a limit to productivity in a scaling business, and choose the best two-out-of-three path.

Never design a product without explicit metrics and tracking tools.

Imagine driving cross-country without a dashboard. How do you know your school zone speed? Low gas? Without a dashboard, you can't monitor your car. You can't improve what you don't measure, as Peter Drucker said. Product managers must constantly enhance their understanding of their users, how they use their product, and how to improve it for optimum value. Customers will only pay if they consistently acquire value from your product.

I’m Solomon Ayanlakin. I’m a product manager at CredPal, a financial business that offers credit cards and Buy Now Pay Later services. Before falling into product management (like most PMs lol), I self-trained as a data analyst, using Alex the Analyst's YouTube playlists and DannyMas' virtual data internship. This article aims to help product managers, owners, and CXOs understand product metrics, give a methodology for creating them, and execute product experiments to enhance them.

☝🏽Introduction

Product metrics assist companies track product performance from the user's perspective. Metrics help firms decide what to construct (feature priority), how to build it, and the outcome's success or failure. To give the best value to new and existing users, track product metrics.

Why should a product manager monitor metrics?

• to assist your users in having a "aha" moment

• To inform you of which features are frequently used by users and which are not

• To assess the effectiveness of a product feature

• To aid in enhancing client onboarding and retention

• To assist you in identifying areas throughout the user journey where customers are satisfied or dissatisfied

• to determine the percentage of returning users and determine the reasons for their return

📈 What Metrics Ought a Product Manager to Monitor?

What indicators should a product manager watch to monitor product health? The metrics to follow change based on the industry, business stage (early, growth, late), consumer needs, and company goals. A startup should focus more on conversion, activation, and active user engagement than revenue growth and retention. The company hasn't found product-market fit or discovered what features drive customer value.

Depending on your use case, company goals, or business stage, here are some important product metric buckets:

All measurements shouldn't be used simultaneously. It depends on your business goals and what value means for your users, then selecting what metrics to track to see if they get it.

Some KPIs are more beneficial to track, independent of industry or customer type. To prevent recording vanity metrics, product managers must clearly specify the types of metrics they should track. Here's how to segment metrics:

1. The North Star Metric, also known as the Focus Metric, is the indicator and aid in keeping track of the top value you provide to users.

2. Primary/Level 1 Metrics: These metrics should either add to the north star metric or be used to determine whether it is moving in the appropriate direction. They are metrics that support the north star metric.

3. These measures serve as leading indications for your north star and Level 2 metrics. You ought to have been aware of certain problems with your L2 measurements prior to the North star metric modifications.

North Star Metric

This is the key metric. A good north star metric measures customer value. It emphasizes your product's longevity. Many organizations fail to grow because they confuse north star measures with other indicators. A good focus metric should touch all company teams and be tracked forever. If a company gives its customers outstanding value, growth and success are inevitable. How do we measure this value?

A north star metric has these benefits:

• Customer Obsession: It promotes a culture of customer value throughout the entire organization.

• Consensus: Everyone can quickly understand where the business is at and can promptly make improvements, according to consensus.

• Growth: It provides a tool to measure the company's long-term success. Do you think your company will last for a long time?

How can I pick a reliable North Star Metric?

Some fear a single metric. Ensure product leaders can objectively determine a north star metric. Your company's focus metric should meet certain conditions. Here are a few:

1. A good focus metric should reflect value and, as such, should be closely related to the point at which customers obtain the desired value from your product. For instance, the quick delivery to your home is a value proposition of UberEats. The value received from a delivery would be a suitable focal metric to use. While counting orders is alluring, the quantity of successfully completed positive review orders would make a superior north star statistic. This is due to the fact that a client who placed an order but received a defective or erratic delivery is not benefiting from Uber Eats. By tracking core value gain, which is the number of purchases that resulted in satisfied customers, we are able to track not only the total number of orders placed during a specific time period but also the core value proposition.
   

2. Focus metrics need to be quantifiable; they shouldn't only be feelings or states; they need to be actionable. A smart place to start is by counting how many times an activity has been completed.
   

3. A great focus metric is one that can be measured within predetermined time limits; otherwise, you are not measuring at all. The company can improve that measure more quickly by having time-bound focus metrics. Measuring and accounting for progress over set time periods is the only method to determine whether or not you are moving in the right path. You can then evaluate your metrics for today and yesterday. It's generally not a good idea to use a year as a time frame. Ideally, depending on the nature of your organization and the measure you are focusing on, you want to take into account on a daily, weekly, or monthly basis.
   

4. Everyone in the firm has the potential to affect it: A short glance at the well-known AAARRR funnel, also known as the Pirate Metrics, reveals that various teams inside the organization have an impact on the funnel. Ideally, the NSM should be impacted if changes are made to one portion of the funnel. Consider how the growth team in your firm is enhancing customer retention. This would have a good effect on the north star indicator because at this stage, a repeat client is probably being satisfied on a regular basis. Additionally, if the opposite were true and a client churned, it would have a negative effect on the focus metric.
   

5. It ought to be connected to the business's long-term success: The direction of sustainability would be indicated by a good north star metric. A company's lifeblood is product demand and revenue, so it's critical that your NSM points in the direction of sustainability. If UberEats can effectively increase the monthly total of happy client orders, it will remain in operation indefinitely.

Many product teams make the mistake of focusing on revenue. When the bottom line is emphasized, a company's goal moves from giving value to extracting money from customers. A happy consumer will stay and pay for your service. Customer lifetime value always exceeds initial daily, monthly, or weekly revenue.

Great North Star Metrics Examples

🥇 Basic/L1 Metrics:

The NSM is broad and focuses on providing value for users, while the primary metric is product/feature focused and utilized to drive the focus metric or signal its health. The primary statistic is team-specific, whereas the north star metric is company-wide. For UberEats' NSM, the marketing team may measure the amount of quality food vendors who sign up using email marketing. With quality vendors, more orders will be satisfied. Shorter feedback loops and unambiguous team assignments make L1 metrics more actionable and significant in the immediate term.

🥈 Supporting L2 metrics:

These are supporting metrics to the L1 and focus metrics. Location, demographics, or features are examples of L1 metrics. UberEats' supporting metrics might be the number of sales emails sent to food vendors, the number of opens, and the click-through rate. Secondary metrics are low-level and evident, and they relate into primary and north star measurements. UberEats needs a high email open rate to attract high-quality food vendors. L2 is a leading sign for L1.

Where can I find product metrics?

How can I measure in-app usage and activity now that I know what metrics to track? Enter product analytics. Product analytics tools evaluate and improve product management parameters that indicate a product's health from a user's perspective.

Various analytics tools on the market supply product insight. From page views and user flows through A/B testing, in-app walkthroughs, and surveys. Depending on your use case and necessity, you may combine tools to see how users engage with your product. Gainsight, MixPanel, Amplitude, Google Analytics, FullStory, Heap, and Pendo are product tools.

This article isn't sponsored and doesn't market product analytics tools. When choosing an analytics tool, consider the following:

• Tools for tracking your Focus, L1, and L2 measurements

• Pricing

• Adaptations to include external data sources and other products

• Usability and the interface

• Scalability

• Security

An investment in the appropriate tool pays off. To choose the correct metrics to track, you must first understand your business need and what value means to your users. Metrics and analytics are crucial for any tech product's growth. It shows how your business is doing and how to best serve users.

This could cost you.

Content creators may have heard about Google's "Helpful content upgrade."

This change is another Google effort to remove low-quality, repetitive, and AI-generated content.

Why should content creators care?

Because too much content manipulates search results.

My experience includes the following.

Website admins seek high-quality guest posts from me. They send me AI-generated text after I say "yes." My readers are irrelevant. Backlinks are needed.

Companies copy high-ranking content to boost their Google rankings. Unfortunately, it's common.

What does this content offer?

Nothing.

Despite Google's updates and efforts to clean search results, webmasters create manipulative content.

As a marketer, I knew about AI-powered content generation tools. However, I've never tried them.

I use old-fashioned content creation methods to grow my website from 0 to 3,000 monthly views in one year.

Last year, I launched a niche website.

I do keyword research, analyze search intent and competitors' content, write an article, proofread it, and then optimize it.

This strategy is time-consuming.

But it yields results!

Here's proof from Google Analytics:

Proven strategies yield promising results.

To validate my assumptions and find new strategies, I run many experiments.

I tested an AI-powered content generator.

I used a tool to write this Google-optimized article about SEO for startups.

I wanted to analyze AI-generated content's Google performance.

Here are the outcomes of my test.

First, quality.

I dislike "meh" content. I expect articles to answer my questions. If not, I've wasted my time.

My essays usually include research, personal anecdotes, and what I accomplished and achieved.

AI-generated articles aren't as good because they lack individuality.

Read my AI-generated article about startup SEO to see what I mean.

It's dry and shallow, IMO.

It seems robotic.

I'd use quotes and personal experience to show how SEO for startups is different.

My article paraphrases top-ranked articles on a certain topic.

It's readable but useless. Similar articles abound online. Why read it?

AI-generated content is low-quality.

Let me show you how this content ranks on Google.

The Google Search Console report shows impressions, clicks, and average position.

Low numbers.

No one opens the 5th Google search result page to read the article. Too far!

You may say the new article will improve.

Marketing-wise, I doubt it.

This article is shorter and less comprehensive than top-ranking pages. It's unlikely to win because of this.

AI-generated content's terrible reality.

I'll compare how this content I wrote for readers and SEO performs.

Both the AI and my article are fresh, but trends are emerging.

My article's CTR and average position are higher.

I spent a week researching and producing that piece, unlike AI-generated content. My expert perspective and unique consequences make it interesting to read.

Human-made.

In summary

No content generator can duplicate a human's tone, writing style, or creativity. Artificial content is always inferior.

Not "bad," but inferior.

Demand for content production tools will rise despite Google's efforts to eradicate thin content.

Most won't spend hours producing link-building articles. Costly.

As guest and sponsored posts, artificial content will thrive.

Before accepting a new arrangement, content creators and website owners should consider this.

Sam Bankman Fried (SDBF) is a legendary con man. But the NYT might not tell you that...

Since SBF's empire was revealed to be a lie, mainstream news organizations and commentators have failed to give readers a straightforward assessment. The New York Times and Wall Street Journal have uncovered many key facts about the scandal, but they have also soft-peddled Bankman-Fried's intent and culpability.

It's clear that the FTX crypto exchange and Alameda Research committed fraud to steal money from users and investors. That’s why a recent New York Times interview was widely derided for seeming to frame FTX’s collapse as the result of mismanagement rather than malfeasance. A Wall Street Journal article lamented FTX's loss of charitable donations, bolstering Bankman's philanthropic pose. Matthew Yglesias, court chronicler of the neoliberal status quo, seemed to whitewash his own entanglements by crediting SBF's money with helping Democrats in 2020 – sidestepping the likelihood that the money was embezzled.

Many outlets have called what happened to FTX a "bank run" or a "run on deposits," but Bankman-Fried insists the company was overleveraged and disorganized. Both attempts to frame the fallout obscure the core issue: customer funds misused.

Because banks lend customer funds to generate returns, they can experience "bank runs." If everyone withdraws at once, they can experience a short-term cash crunch but there won't be a long-term problem.

Crypto exchanges like FTX aren't banks. They don't do bank-style lending, so a withdrawal surge shouldn't strain liquidity. FTX promised customers it wouldn't lend or use their crypto.

Alameda's balance sheet blurs SBF's crypto empire.

The funds were sent to Alameda Research, where they were apparently gambled away. This is massive theft. According to a bankruptcy document, up to 1 million customers could be affected.

In less than a month, reporting and the bankruptcy process have uncovered a laundry list of decisions and practices that would constitute financial fraud if FTX had been a U.S.-regulated entity, even without crypto-specific rules. These ploys may be litigated in U.S. courts if they enabled the theft of American property.

The list is very, very long.

The many crimes of Sam Bankman-Fried and FTX

At the heart of SBF's fraud are the deep and (literally) intimate ties between FTX and Alameda Research, a hedge fund he co-founded. An exchange makes money from transaction fees on user assets, but Alameda trades and invests its own funds.

Bankman-Fried called FTX and Alameda "wholly separate" and resigned as Alameda's CEO in 2019. The two operations were closely linked. Bankman-Fried and Alameda CEO Caroline Ellison were romantically linked.

These circumstances enabled SBF's sin.  Within days of FTX's first signs of weakness, it was clear the exchange was funneling customer assets to Alameda for trading, lending, and investing. Reuters reported on Nov. 12 that FTX sent $10 billion to Alameda. As much as $2 billion was believed to have disappeared after being sent to Alameda. Now the losses look worse.

It's unclear why those funds were sent to Alameda or when Bankman-Fried betrayed his depositors. On-chain analysis shows most FTX to Alameda transfers occurred in late 2021, and bankruptcy filings show both lost $3.7 billion in 2021.

SBF's companies lost millions before the 2022 crypto bear market. They may have stolen funds before Terra and Three Arrows Capital, which killed many leveraged crypto players.

FTT loans and prints

CoinDesk's report on Alameda's FTT holdings ignited FTX and Alameda Research. FTX created this instrument, but only a small portion was traded publicly; FTX and Alameda held the rest. These holdings were illiquid, meaning they couldn't be sold at market price. Bankman-Fried valued its stock at the fictitious price.

FTT tokens were reportedly used as collateral for loans, including FTX loans to Alameda. Close ties between FTX and Alameda made the FTT token harder or more expensive to use as collateral, reducing the risk to customer funds.

This use of an internal asset as collateral for loans between clandestinely related entities is similar to Enron's 1990s accounting fraud. These executives served 12 years in prison.

Alameda's margin liquidation exemption

Alameda Research had a "secret exemption" from FTX's liquidation and margin trading rules, according to legal filings by FTX's new CEO.

FTX, like other crypto platforms and some equity or commodity services, offered "margin" or loans for trades. These loans are usually collateralized, meaning borrowers put up other funds or assets. If a margin trade loses enough money, the exchange will sell the user's collateral to pay off the initial loan.

Keeping asset markets solvent requires liquidating bad margin positions. Exempting Alameda would give it huge advantages while exposing other FTX users to hidden risks. Alameda could have kept losing positions open while closing out competitors. Alameda could lose more on FTX than it could pay back, leaving a hole in customer funds.

The exemption is criminal in multiple ways. FTX was fraudulently marketed overall. Instead of a level playing field, there were many customers.

Above them all, with shotgun poised, was Alameda Research.

Alameda front-running FTX listings

Argus says there's circumstantial evidence that Alameda Research had insider knowledge of FTX's token listing plans. Alameda was able to buy large amounts of tokens before the listing and sell them after the price bump.

If true, these claims would be the most brazenly illegal of Alameda and FTX's alleged shenanigans. Even if the tokens aren't formally classified as securities, insider trading laws may apply.

In a similar case this year, an OpenSea employee was charged with wire fraud for allegedly insider trading. This employee faces 20 years in prison for front-running monkey JPEGs.

Huge loans to executives

Alameda Research reportedly lent FTX executives $4.1 billion, including massive personal loans. Bankman-Fried received $1 billion in personal loans and $2.3 billion for an entity he controlled, Paper Bird. Nishad Singh, director of engineering, was given $543 million, and FTX Digital Markets co-CEO Ryan Salame received $55 million.

FTX has more smoking guns than a Texas shooting range, but this one is the smoking bazooka – a sign of criminal intent. It's unclear how most of the personal loans were used, but liquidators will have to recoup the money.

The loans to Paper Bird were even more worrisome because they created another related third party to shuffle assets. Forbes speculates that some Paper Bird funds went to buy Binance's FTX stake, and Paper Bird committed hundreds of millions to outside investments.

FTX Inner Circle: Who's Who

That included many FTX-backed VC funds. Time will tell if this financial incest was criminal fraud. It fits Bankman-pattern Fried's of using secret flows, leverage, and funny money to inflate asset prices.

FTT or loan 'bailouts'

Also. As the crypto bear market continued in 2022, Bankman-Fried proposed bailouts for bankrupt crypto lenders BlockFi and Voyager Digital. CoinDesk was among those deceived, welcoming SBF as a J.P. Morgan-style sector backstop.

In a now-infamous interview with CNBC's "Squawk Box," Bankman-Fried referred to these decisions as bets that may or may not pay off.

But maybe not. Bloomberg's Matt Levine speculated that FTX backed BlockFi with FTT money. This Monopoly bailout may have been intended to hide FTX and Alameda liabilities that would have been exposed if BlockFi went bankrupt sooner. This ploy has no name, but it echoes other corporate frauds.

Secret bank purchase

Alameda Research invested $11.5 million in the tiny Farmington State Bank, doubling its net worth. As a non-U.S. entity and an investment firm, Alameda should have cleared regulatory hurdles before acquiring a U.S. bank.

In the context of FTX, the bank's stake becomes "ominous." Alameda and FTX could have done more shenanigans with bank control. Compare this to the Bank for Credit and Commerce International's failed attempts to buy U.S. banks. BCCI was even nefarious than FTX and wanted to buy U.S. banks to expand its money-laundering empire.

The mainstream's mistakes

These are complex and nuanced forms of fraud that echo traditional finance models. This obscurity helped Bankman-Fried masquerade as an honest player and likely kept coverage soft after the collapse.

Bankman-Fried had a scruffy, nerdy image, like Mark Zuckerberg and Adam Neumann. In interviews, he spoke nonsense about an industry full of jargon and complicated tech. Strategic donations and insincere ideological statements helped him gain political and social influence.

SBF' s'Effective' Altruism Blew Up FTX

Bankman-Fried has continued to muddy the waters with disingenuous letters, statements, interviews, and tweets since his con collapsed. He's tried to portray himself as a well-intentioned but naive kid who made some mistakes. This is a softer, more pernicious version of what Trump learned from mob lawyer Roy Cohn. Bankman-Fried doesn't "deny, deny, deny" but "confuse, evade, distort."

It's mostly worked. Kevin O'Leary, who plays an investor on "Shark Tank," repeats Bankman-SBF's counterfactuals.  O'Leary called Bankman-Fried a "savant" and "probably one of the most accomplished crypto traders in the world" in a Nov. 27 interview with Business Insider, despite recent data indicating immense trading losses even when times were good.

O'Leary's status as an FTX investor and former paid spokesperson explains his continued affection for Bankman-Fried despite contradictory evidence. He's not the only one promoting Bankman-Fried. The disgraced son of two Stanford law professors will defend himself at Wednesday's DealBook Summit.

SBF's fraud and theft rival those of Bernie Madoff and Jho Low. Whether intentionally or through malign ineptitude, the fraud echoes Worldcom and Enron.

The Perverse Impacts of Anti-Money-Laundering

The principals in all of those scandals wound up either sentenced to prison or on the run from the law. Sam Bankman-Fried clearly deserves to share their fate.

Read the full article here.