More on Web3 & Crypto
CyberPunkMetalHead
3 years ago
It's all about the ego with Terra 2.0.
UST depegs and LUNA crashes 99.999% in a fraction of the time it takes the Moon to orbit the Earth.
Fat Man, a Terra whistle-blower, promises to expose Do Kwon's dirty secrets and shady deals.
The Terra community has voted to relaunch Terra LUNA on a new blockchain. The Terra 2.0 Pheonix-1 blockchain went live on May 28, 2022, and people were airdropped the new LUNA, now called LUNA, while the old LUNA became LUNA Classic.
Does LUNA deserve another chance? To answer this, or at least start a conversation about the Terra 2.0 chain's advantages and limitations, we must assess its fundamentals, ideology, and long-term vision.
Whatever the result, our analysis must be thorough and ruthless. A failure of this magnitude cannot happen again, so we must magnify every potential breaking point by 10.
Will UST and LUNA holders be compensated in full?
The obvious. First, and arguably most important, is to restore previous UST and LUNA holders' bags.
Terra 2.0 has 1,000,000,000,000 tokens to distribute.
25% of a community pool
Holders of pre-attack LUNA: 35%
10% of aUST holders prior to attack
Holders of LUNA after an attack: 10%
UST holders as of the attack: 20%
Every LUNA and UST holder has been compensated according to the above proposal.
According to self-reported data, the new chain has 210.000.000 tokens and a $1.3bn marketcap. LUNC and UST alone lost $40bn. The new token must fill this gap. Since launch:
LUNA holders collectively own $1b worth of LUNA if we subtract the 25% community pool airdrop from the current market cap and assume airdropped LUNA was never sold.
At the current supply, the chain must grow 40 times to compensate holders. At the current supply, LUNA must reach $240.
LUNA needs a full-on Bull Market to make LUNC and UST holders whole.
Who knows if you'll be whole? From the time you bought to the amount and price, there are too many variables to determine if Terra can cover individual losses.
The above distribution doesn't consider individual cases. Terra didn't solve individual cases. It would have been huge.
What does LUNA offer in terms of value?
UST's marketcap peaked at $18bn, while LUNC's was $41bn. LUNC and UST drove the Terra chain's value.
After it was confirmed (again) that algorithmic stablecoins are bad, Terra 2.0 will no longer support them.
Algorithmic stablecoins contributed greatly to Terra's growth and value proposition. Terra 2.0 has no product without algorithmic stablecoins.
Terra 2.0 has an identity crisis because it has no actual product. It's like Volkswagen faking carbon emission results and then stopping car production.
A project that has already lost the trust of its users and nearly all of its value cannot survive without a clear and in-demand use case.
Do Kwon, how about him?
Oh, the Twitter-caller-poor? Who challenges crypto billionaires to break his LUNA chain? Who dissolved Terra Labs South Korea before depeg? Arrogant guy?
That's not a good image for LUNA, especially when making amends. I think he should step down and let a nicer person be Terra 2.0's frontman.
The verdict
Terra has a terrific community with an arrogant, unlikeable leader. The new LUNA chain must grow 40 times before it can start making up its losses, and even then, not everyone's losses will be covered.
I won't invest in Terra 2.0 or other algorithmic stablecoins in the near future. I won't be near any Do Kwon-related project within 100 miles. My opinion.
Can Terra 2.0 be saved? Comment below.
Ajay Shrestha
2 years ago
Bitcoin's technical innovation: addressing the issue of the Byzantine generals
The 2008 Bitcoin white paper solves the classic computer science consensus problem.
Issue Statement
The Byzantine Generals Problem (BGP) is called after an allegory in which several generals must collaborate and attack a city at the same time to win (figure 1-left). Any general who retreats at the last minute loses the fight (figure 1-right). Thus, precise messengers and no rogue generals are essential. This is difficult without a trusted central authority.
In their 1982 publication, Leslie Lamport, Robert Shostak, and Marshall Please termed this topic the Byzantine Generals Problem to simplify distributed computer systems.
Consensus in a distributed computer network is the issue. Reaching a consensus on which systems work (and stay in the network) and which don't makes maintaining a network tough (i.e., needs to be removed from network). Challenges include unreliable communication routes between systems and mis-reporting systems.
Solving BGP can let us construct machine learning solutions without single points of failure or trusted central entities. One server hosts model parameters while numerous workers train the model. This study describes fault-tolerant Distributed Byzantine Machine Learning.
Bitcoin invented a mechanism for a distributed network of nodes to agree on which transactions should go into the distributed ledger (blockchain) without a trusted central body. It solved BGP implementation. Satoshi Nakamoto, the pseudonymous bitcoin creator, solved the challenge by cleverly combining cryptography and consensus mechanisms.
Disclaimer
This is not financial advice. It discusses a unique computer science solution.
Bitcoin
Bitcoin's white paper begins:
“A purely peer-to-peer version of electronic cash would allow online payments to be sent directly from one party to another without going through a financial institution.” Source: https://www.ussc.gov/sites/default/files/pdf/training/annual-national-training-seminar/2018/Emerging_Tech_Bitcoin_Crypto.pdf
Bitcoin's main parts:
The open-source and versioned bitcoin software that governs how nodes, miners, and the bitcoin token operate.
The native kind of token, known as a bitcoin token, may be created by mining (up to 21 million can be created), and it can be transferred between wallet addresses in the bitcoin network.
Distributed Ledger, which contains exact copies of the database (or "blockchain") containing each transaction since the first one in January 2009.
distributed network of nodes (computers) running the distributed ledger replica together with the bitcoin software. They broadcast the transactions to other peer nodes after validating and accepting them.
Proof of work (PoW) is a cryptographic requirement that must be met in order for a miner to be granted permission to add a new block of transactions to the blockchain of the cryptocurrency bitcoin. It takes the form of a valid hash digest. In order to produce new blocks on average every 10 minutes, Bitcoin features a built-in difficulty adjustment function that modifies the valid hash requirement (length of nonce). PoW requires a lot of energy since it must continually generate new hashes at random until it satisfies the criteria.
The competing parties known as miners carry out continuous computing processing to address recurrent cryptography issues. Transaction fees and some freshly minted (mined) bitcoin are the rewards they receive. The amount of hashes produced each second—or hash rate—is a measure of mining capacity.
Cryptography, decentralization, and the proof-of-work consensus method are Bitcoin's most unique features.
Bitcoin uses encryption
Bitcoin employs this established cryptography.
Hashing
digital signatures based on asymmetric encryption
Hashing (SHA-256) (SHA-256)
Hashing converts unique plaintext data into a digest. Creating the plaintext from the digest is impossible. Bitcoin miners generate new hashes using SHA-256 to win block rewards.
A new hash is created from the current block header and a variable value called nonce. To achieve the required hash, mining involves altering the nonce and re-hashing.
The block header contains the previous block hash and a Merkle root, which contains hashes of all transactions in the block. Thus, a chain of blocks with increasing hashes links back to the first block. Hashing protects new transactions and makes the bitcoin blockchain immutable. After a transaction block is mined, it becomes hard to fabricate even a little entry.
Asymmetric Cryptography Digital Signatures
Asymmetric cryptography (public-key encryption) requires each side to have a secret and public key. Public keys (wallet addresses) can be shared with the transaction party, but private keys should not. A message (e.g., bitcoin payment record) can only be signed by the owner (sender) with the private key, but any node or anybody with access to the public key (visible in the blockchain) can verify it. Alex will submit a digitally signed transaction with a desired amount of bitcoin addressed to Bob's wallet to a node to send bitcoin to Bob. Alex alone has the secret keys to authorize that amount. Alex's blockchain public key allows anyone to verify the transaction.
Solution
Now, apply bitcoin to BGP. BGP generals resemble bitcoin nodes. The generals' consensus is like bitcoin nodes' blockchain block selection. Bitcoin software on all nodes can:
Check transactions (i.e., validate digital signatures)
2. Accept and propagate just the first miner to receive the valid hash and verify it accomplished the task. The only way to guess the proper hash is to brute force it by repeatedly producing one with the fixed/current block header and a fresh nonce value.
Thus, PoW and a dispersed network of nodes that accept blocks from miners that solve the unfalsifiable cryptographic challenge solve consensus.
Suppose:
Unreliable nodes
Unreliable miners
Bitcoin accepts the longest chain if rogue nodes cause divergence in accepted blocks. Thus, rogue nodes must outnumber honest nodes in accepting/forming the longer chain for invalid transactions to reach the blockchain. As of November 2022, 7000 coordinated rogue nodes are needed to takeover the bitcoin network.
Dishonest miners could also try to insert blocks with falsified transactions (double spend, reverse, censor, etc.) into the chain. This requires over 50% (51% attack) of miners (total computational power) to outguess the hash and attack the network. Mining hash rate exceeds 200 million (source). Rewards and transaction fees encourage miners to cooperate rather than attack. Quantum computers may become a threat.
Visit my Quantum Computing post.
Quantum computers—what are they? Quantum computers will have a big influence. towardsdatascience.com
Nodes have more power than miners since they can validate transactions and reject fake blocks. Thus, the network is secure if honest nodes are the majority.
Summary
Table 1 compares three Byzantine Generals Problem implementations.
Bitcoin white paper and implementation solved the consensus challenge of distributed systems without central governance. It solved the illusive Byzantine Generals Problem.
Resources
Resources
Source-code for Bitcoin Core Software — https://github.com/bitcoin/bitcoin
Bitcoin white paper — https://bitcoin.org/bitcoin.pdf
https://www.microsoft.com/en-us/research/publication/byzantine-generals-problem/
https://www.microsoft.com/en-us/research/uploads/prod/2016/12/The-Byzantine-Generals-Problem.pdf
Genuinely Distributed Byzantine Machine Learning, El-Mahdi El-Mhamdi et al., 2020. ACM, New York, NY, https://doi.org/10.1145/3382734.3405695
Vitalik
4 years ago
An approximate introduction to how zk-SNARKs are possible (part 1)
You can make a proof for the statement "I know a secret number such that if you take the word ‘cow', add the number to the end, and SHA256 hash it 100 million times, the output starts with 0x57d00485aa". The verifier can verify the proof far more quickly than it would take for them to run 100 million hashes themselves, and the proof would also not reveal what the secret number is.
In the context of blockchains, this has 2 very powerful applications: Perhaps the most powerful cryptographic technology to come out of the last decade is general-purpose succinct zero knowledge proofs, usually called zk-SNARKs ("zero knowledge succinct arguments of knowledge"). A zk-SNARK allows you to generate a proof that some computation has some particular output, in such a way that the proof can be verified extremely quickly even if the underlying computation takes a very long time to run. The "ZK" part adds an additional feature: the proof can keep some of the inputs to the computation hidden.
You can make a proof for the statement "I know a secret number such that if you take the word ‘cow', add the number to the end, and SHA256 hash it 100 million times, the output starts with 0x57d00485aa". The verifier can verify the proof far more quickly than it would take for them to run 100 million hashes themselves, and the proof would also not reveal what the secret number is.
In the context of blockchains, this has two very powerful applications:
- Scalability: if a block takes a long time to verify, one person can verify it and generate a proof, and everyone else can just quickly verify the proof instead
- Privacy: you can prove that you have the right to transfer some asset (you received it, and you didn't already transfer it) without revealing the link to which asset you received. This ensures security without unduly leaking information about who is transacting with whom to the public.
But zk-SNARKs are quite complex; indeed, as recently as in 2014-17 they were still frequently called "moon math". The good news is that since then, the protocols have become simpler and our understanding of them has become much better. This post will try to explain how ZK-SNARKs work, in a way that should be understandable to someone with a medium level of understanding of mathematics.
Why ZK-SNARKs "should" be hard
Let us take the example that we started with: we have a number (we can encode "cow" followed by the secret input as an integer), we take the SHA256 hash of that number, then we do that again another 99,999,999 times, we get the output, and we check what its starting digits are. This is a huge computation.
A "succinct" proof is one where both the size of the proof and the time required to verify it grow much more slowly than the computation to be verified. If we want a "succinct" proof, we cannot require the verifier to do some work per round of hashing (because then the verification time would be proportional to the computation). Instead, the verifier must somehow check the whole computation without peeking into each individual piece of the computation.
One natural technique is random sampling: how about we just have the verifier peek into the computation in 500 different places, check that those parts are correct, and if all 500 checks pass then assume that the rest of the computation must with high probability be fine, too?
Such a procedure could even be turned into a non-interactive proof using the Fiat-Shamir heuristic: the prover computes a Merkle root of the computation, uses the Merkle root to pseudorandomly choose 500 indices, and provides the 500 corresponding Merkle branches of the data. The key idea is that the prover does not know which branches they will need to reveal until they have already "committed to" the data. If a malicious prover tries to fudge the data after learning which indices are going to be checked, that would change the Merkle root, which would result in a new set of random indices, which would require fudging the data again... trapping the malicious prover in an endless cycle.
But unfortunately there is a fatal flaw in naively applying random sampling to spot-check a computation in this way: computation is inherently fragile. If a malicious prover flips one bit somewhere in the middle of a computation, they can make it give a completely different result, and a random sampling verifier would almost never find out.
It only takes one deliberately inserted error, that a random check would almost never catch, to make a computation give a completely incorrect result.
If tasked with the problem of coming up with a zk-SNARK protocol, many people would make their way to this point and then get stuck and give up. How can a verifier possibly check every single piece of the computation, without looking at each piece of the computation individually? There is a clever solution.
see part 2
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Shruti Mishra
3 years ago
How to get 100k profile visits on Twitter each month without spending a dime
As a marketer, I joined Twitter on August 31, 2022 to use it.
Growth has been volatile, causing up-and-down engagements. 500 followers in 11 days.
I met amazing content creators, marketers, and people.
Those who use Twitter may know that one-liners win the algorithm, especially if they're funny or humorous, but as a marketer I can't risk posting content that my audience won't like.
I researched, learned some strategies, and A/B tested; some worked, some didn't.
In this article, I share what worked for me so you can do the same.
Thanks for reading!
Let's check my Twitter stats.
Tweets: how many tweets I sent in the first 28 days.
A user may be presented with a Tweet in their timeline or in search results.
In-person visits how many times my Twitter profile was viewed in the first 28 days.
Mentions: the number of times a tweet has mentioned my name.
Number of followers: People who were following me
Getting 500 Twitter followers isn't difficult.
Not easy, but doable.
Follow these steps to begin:
Determine your content pillars in step 1.
My formula is Growth = Content + Marketing + Community.
I discuss growth strategies.
My concept for growth is : 1. Content = creating / writing + sharing content in my niche. 2. Marketing = Marketing everything in business + I share my everyday learnings in business, marketing & entrepreneurship. 3. Community = Building community of like minded individuals (Also,I share how to’s) + supporting marketers to build & grow through community building.
Identify content pillars to create content for your audience.
2. Make your profile better
Create a profile picture. Your recognition factor is this.
Professional headshots are worthwhile.
This tool can help you create a free, eye-catching profile pic.
Use a niche-appropriate avatar if you don't want to show your face.
2. Create a bio that converts well mainly because first impressions count.
what you're sharing + why + +social proof what are you making
Be brief and precise. (155 characters)
3. Configure your banner
Banners complement profile pictures.
Use this space to explain what you do and how Twitter followers can benefit.
Canva's Twitter header maker is free.
Birdy can test multiple photo, bio, and banner combinations to optimize your profile.
Versions A and B of your profile should be completed.
Find the version that converts the best.
Use the profile that converts the best.
4. Special handle
If your username/handle is related to your niche, it will help you build authority and presence among your audience. Mine on Twitter is @marketershruti.
5. Participate expertly
Proficiently engage while you'll have no audience at first. Borrow your dream audience for free.
Steps:
Find a creator who has the audience you want.
Activate their post notifications and follow them.
Add a valuable comment first.
6. Create fantastic content
Use:
Medium (Read articles about your topic.)
Podcasts (Listen to experts on your topics)
YouTube (Follow channels in your niche)
Tweet what?
Listicle ( Hacks, Books, Tools, Podcasts)
Lessons (Teach your audience how to do 1 thing)
Inspirational (Inspire people to take action)
Consistent writing?
You MUST plan ahead and schedule your Tweets.
Use a scheduling tool that is effective for you; hypefury is mine.
Lastly, consistency is everything that attracts growth. After optimizing your profile, stay active to gain followers, engagements, and clients.
If you found this helpful, please like and comment below.
Glorin Santhosh
3 years ago
Start organizing your ideas by using The Second Brain.
Building A Second Brain helps us remember connections, ideas, inspirations, and insights. Using contemporary technologies and networks increases our intelligence.
This approach makes and preserves concepts. It's a straightforward, practical way to construct a second brain—a remote, centralized digital store for your knowledge and its sources.
How to build ‘The Second Brain’
Have you forgotten any brilliant ideas? What insights have you ignored?
We're pressured to read, listen, and watch informative content. Where did the data go? What happened?
Our brains can store few thoughts at once. Our brains aren't idea banks.
Building a Second Brain helps us remember thoughts, connections, and insights. Using digital technologies and networks expands our minds.
Ten Rules for Creating a Second Brain
1. Creative Stealing
Instead of starting from scratch, integrate other people's ideas with your own.
This way, you won't waste hours starting from scratch and can focus on achieving your goals.
Users of Notion can utilize and customize each other's templates.
2. The Habit of Capture
We must record every idea, concept, or piece of information that catches our attention since our minds are fragile.
When reading a book, listening to a podcast, or engaging in any other topic-related activity, save and use anything that resonates with you.
3. Recycle Your Ideas
Reusing our own ideas across projects might be advantageous since it helps us tie new information to what we already know and avoids us from starting a project with no ideas.
4. Projects Outside of Category
Instead of saving an idea in a folder, group it with documents for a project or activity.
If you want to be more productive, gather suggestions.
5. Burns Slowly
Even if you could finish a job, work, or activity if you focused on it, you shouldn't.
You'll get tired and can't advance many projects. It's easier to divide your routine into daily tasks.
Few hours of daily study is more productive and healthier than entire nights.
6. Begin with a surplus
Instead of starting with a blank sheet when tackling a new subject, utilise previous articles and research.
You may have read or saved related material.
7. Intermediate Packets
A bunch of essay facts.
You can utilize it as a document's section or paragraph for different tasks.
Memorize useful information so you can use it later.
8. You only know what you make
We can see, hear, and read about anything.
What matters is what we do with the information, whether that's summarizing it or writing about it.
9. Make it simpler for yourself in the future.
Create documents or files that your future self can easily understand. Use your own words, mind maps, or explanations.
10. Keep your thoughts flowing.
If you don't employ the knowledge in your second brain, it's useless.
Few people exercise despite knowing its benefits.
Conclusion:
You may continually move your activities and goals closer to completion by organizing and applying your information in a way that is results-focused.
Profit from the information economy's explosive growth by turning your specialized knowledge into cash.
Make up original patterns and linkages between topics.
You may reduce stress and information overload by appropriately curating and managing your personal information stream.
Learn how to apply your significant experience and specific knowledge to a new job, business, or profession.
Without having to adhere to tight, time-consuming constraints, accumulate a body of relevant knowledge and concepts over time.
Take advantage of all the learning materials that are at your disposal, including podcasts, online courses, webinars, books, and articles.
Solomon Ayanlakin
3 years ago
Metrics for product management and being a good leader
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:
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.
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.
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:
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.
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.
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.
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.
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.