In-depth Analysis of friend.tech’s Pricing Model Core Design of Viral Marketing and Fission
Analysis of friend.tech's Pricing Model and Viral Marketing Design
Simplicity and elegance enable the creation of everything in life.
In the field of blockchain, does everything also stem from a similar “e=mc^2” equation?
Is uniswap’s x*y=k?
Is nft’s price=floorprice?
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Let’s put that aside for now.
Let’s first take a look at friend.tech, a socialfi that is currently popular.
In simple terms,
1. When you register an account and link it to your Twitter, you will perform two transactions:
- The first transaction: deposit more than 0.01 ETH into your account address to activate your wallet;
- The second transaction: you automatically buy your own share (recently renamed as “key”) with 0 ETH.
Then you will find that you have been
Anyone can buy your share without restrictions.
Its initial value is 0.0000625 ETH.
You will also find that a robot has silently bought you, and if you have too few followers, it will sell you at the original price after a while:
- The robot may calculate your value based on the number of Twitter followers and buy you to earn the price difference.
- The official has a 6-month activity to distribute 100 million points, aiming to short sell.
2. Then, when you want to buy or sell someone’s share, you will find that you can trade without restrictions, without liquidity constraints, because its price is determined by a built-in formula.
S is the total number of shares you are selling.
This formula means that
1. The price curve is consistent for everyone, and the same quantity of shares corresponds to the same price.
2. The absolute value of price growth is linear.
Price difference = (S+1)^2/16000 – S^2/16000
The price difference grows linearly.
3. However, the growth rate is exponential at the beginning, and as the number of shares reaches hundreds, the growth rate becomes gentle.
The proportion of price difference to price = ((2S+1)/1600) / S^2/16000
In simpler terms, you can understand this pricing method as stacking boxes in a queue. You can stack them up or take them from the top at any time. The absolute value of the money in the box grows linearly, but the initial growth rate is frightening. This means that
During the value discovery period:
As long as even one person behind you believes that the value of the share is undervalued and is willing to continue stacking, then you will profit.
During the value regression period:
You can only pray that you are the person who discovers the value regression, and you are the first to take away the big box from the top. Otherwise…….
In the high-value range, during the linear stability period:
It means a bit like blue chips, really carrying out price discovery
Its early model is extremely similar to Ponzi schemes, with expectations of airdrops, and you also need to compete with robots in terms of speed
But you will also find a problem with this mechanism:
The amount that can be cut is not very high?
The most expensive one now is the founder’s race
On August 22nd, there were only 154 holders corresponding to the price of 3.2 ETH DEETHPRICE
Below are the top users on August 22nd and 23rd, and the quantity has also
So, what is the upper limit of the shares that this price model can accommodate?
We use Warren Buffett’s lunch as an example
First, let’s do a simple calculation. In the short term, Warren Buffett’s lunch is worth $5 million, divided into S shares for the community
As you can see,
1. The size of the community that can be reached is relatively small, and the price is limited to a certain extent
2. It is not a subscription model that sells universal, cheap, and brain-consuming truths to the general public
What is truly sustainable in this pricing model is “selling scarcity”, not subscription
Although we can only chat for now
But chatting is just a floor price
Just give a floor price for chatting
Can I give you some work?
Do you want to see my true scarce resource?
In this stage, creators are also confused
So the gameplay is very simple
Buy whoever is famous and has more Twitter followers
The robot will scan whoever, and the FOMO crowd will rush to whoever
As for whether creators can provide enough scarcity in the future
That is a matter for the future
So the most important attribute of this formula is
Can it give you true scarcity pricing?
NFT gives a floor price
“S^2/16000” gives a top price?
Doing subscriptions and paid chats in web3
Isn’t that a waste?
Moreover, if you spend money, will others really want to chat with you?
Telling a story that is not known, having a lively airdrop, and then dispersing?
The effectiveness of the price still needs active empowerment,
You have to bring out the core scarce work in order to be worth a top price,
Thinking back to the 500 ETH from Teacher Xiaolai in the past
If placed in a pricing system, everyone can appreciate the beautiful curve together.
If we make Buffett’s lunch even more extreme:
From the perspective of the share holder, each transaction pays 5% to the creator. Assuming a rough valuation model of 20 times, the valuation of Buffett’s lunch for 20 years is suddenly given. Buffett himself assumes that he meets his own valuation when selling S shares.
Buffett himself acts as a market maker and buys all the shares before S shares.
The cost is
Then sell S shares at a price of (S^2/16000), buying one share and selling one stable price each time. In total, S shares are sold, and the profit is:
Let it equal to the valuation 5000000*20
“The price of a top NFT, the scale of a core community.”
But it can be seen that it is quite difficult to reach this price and scale.
The product direction determined by this pricing model
May not be incidental, it may be for pricing itself
So the assumptions of the two calculations above are:
1. The creator believes that he has provided scarcity value to the shares and can take action in advance, such as acting as a market maker for himself
2. Each share holder is a scarce evaluator and market maker, and together they determine the scarcity price.
Scarcity lies in the core value of non-replicability, real-time nature, creator’s creativity at that time, and empowerment intention.
So a person’s life of ups and downs, scarcity, moments of glory, and top prices will be reflected in the fluctuating price curve.
Looking back at the beginning of the text
NFT’s price=floor price
x*y=k tells us that
Technically, as long as the depth is deep enough, slippage can be smoothed out
It can better reflect the value of depth at the value level, which is the value of the consensus network
Price=floor price tells us that
The depth can be easily undermined by a floor price
So the value of this system comes more from the price system itself
Similar to luxury goods and identity recognition
Applying liquidity mining on this formula
Price=S^2/16000 tells us that
The pricing model of the top price, higher prices, and limited networks all point to the “core scarce pricing system” of people.
If the denominator is larger than 16,000, it refers to a “price-flattened subscription system”; if it is smaller than 16,000, it refers to a more volatile and sensitive “super VIP customized service system.”
As for this simple formula, the steep curve it brings in the early stages and the great arbitrage opportunities it presents may be seen as a problem by some, but the wise see it as an opportunity.
After all, simplicity represents a more resilient, fair, and prosperous system, and arbitrage opportunities will be resolved by the market itself.
The simpler and more elegant the pricing model, the closer it can approach the highly efficient price discovery of blockchain. Only by discovering the price can another thing that blockchain is good at be done: valuation.
However, the pricing model itself also determines the value and growth direction of the product.
But now the pricing is a completely different logic.
Let’s turn around and try to find this kind of price pattern.
1. Does it have anything to do with the value provided by the creator?
2. Does it have anything to do with the number of fans the creator has?
Yes, but it cannot be quantitatively calculated. It follows that the more fans there are, the higher the average price, which can be used as a reference.
Directly quoting the data compiled by foresightnews
In the figure below, there is still a significant difference in the number of top-level Twitter followers, with even hundreds or thousands of followers included.
It can be seen that
1. It is related to the creator’s ability to mobilize fans, but cannot be quantified
2. There are behaviors such as self-purchasing, self-brushing, self-promotion, and self-realization included
So let’s try a simple calculation to test the price range
Consider some factors
- Revenue: Transaction tax revenue + Expected income from point brushing
- Cost: Cost of self-purchase
1. Creator’s transaction tax revenue
It can be found that the earning/price ratio K has a simple range
The higher the price, the higher the frequency of attracting transactions and the higher the earning
It is related to price ranking, active guidance by the creator, etc., which are currently homogeneous conditions.
The first place, cobie’s K is close to 30
The K value of the top 4 is between 20-30
The K value of the 5th-50th place is between 10-20
The K value of the 51st-400th place is between 5-10
So we will use K=30\20\10 for calculation later
Analyze using cobie’s transaction data
We can see that cobie’s transaction volume fluctuates greatly, with 2/3 of the trading volume coming from the past week.
And the daily trading volume during the calm period is only 1-2 ETH
Prior to August 14th, the highest price was 2.4 ETH, with a cycle income of 35 ETH, K=15
After August 14th, the highest price was 3.2 ETH, with a cycle income of 53 ETH, K=16.5
And the daily trading volume during the calm period is only 1-2 ETH. We assume that the calm period will bring the creator a maximum of 30% incremental income
So we take K values as K=(30;20;10)(1/2)(4/3)
Substitute into the subsequent calculations
2. Expected income from point farming
In this part, we do not include intentional point farming income
We only include the airdrop income that users passively receive in buying and selling, or do not calculate income
We assume that the airdrop rate is 20%, and 20% of the chips are used for airdrops
Among them, PE may be between 10-30, and we tend to use a lower PE of 10 during periods of excessive traffic
Calculations show that the income from airdrop farming is about 3 times the transaction tax cost paid
For subsequent calculations, since it is impossible to estimate this part of the income, we consider the trading volume of buying S shares and then selling them all as one transaction volume, with a coefficient of F
We tend to set F=0.5 when users passively receive airdrop farming income
Or completely ignore it with F=0
We will make a decision on which data is more accurate for calculation later
Then, the trading volume for point farming is defined as
Below we can roughly list the formulas for calculation:
creator transaction tax revenue + (point accumulation income – point accumulation cost) >= self-purchase Sshares cost
The self-purchase cost is not necessarily obtained purely through self-purchase,
Here, only passive self-market making with a cost of 0.5
There are three unknowns,
Short position coefficient F, taking values of 0, 0.5, and 1 respectively
Transaction tax coefficient K, taking values of 7, 14, and 20 respectively
Valuation PE, under the condition of high short-term data, try to take a small value of 10
The calculated price is basically in line with the current market price, which proves the speculated price composition factors behind the formula:
1. The majority of the income comes from creator’s transaction tax revenue, indicating that the main indicator is the trading volume of buying and selling, which reflects the activity of fans and the enthusiasm of creators.
2. A small part of the income comes from passive airdrop income. Our calculation results show that the coefficient F seems to be between 0.5 and 1, indicating that the market does not include a large proportion of future airdrop income in the price.
3. The price reflects a portion of the market making cost, which is approximately the coefficient we chose, 0.5.
So how do we apply this conclusion?
Let’s take Cobie’s data chart as an example.
1. Calculate the daily trading volume directly as an indicator. The two high points on the right are 3.2 and 2.5, respectively.
The corresponding trading volumes for August 19th and August 21st are 270 ETH and 130 ETH.
Previously, we calculated the price of top-level creators as earning/price=15 for a single cycle.
First of all, the value of 15 corresponds to different levels and needs to be considered.
Secondly, we need to determine a value that is not particularly clear,
That is, based on the current cycle and activity, determine how much proportion of the trading volume on this day may account for the active cycle.
Determine that the first peak belongs to the first peak after the quiet period, and judge that there is a 4-fold trading volume on that day in this cycle.
That is, price=270*4*0.05/15=3.6 ETH
Close to 3.2
Determine that the second peak belongs to the second round of the cycle, and judge that the total trading volume of the cycle is 6 times that of the day.
That is, price=130*6*0.05/15=2.6 ETH
Close to 2.5
The difficulty lies in how to determine the proportion of trading volume.
In addition, different influencers, enthusiasm of spreading, and the popularity of the project itself will affect the fluctuation of K value.
This method may have some difficulty in speculating the price fluctuations of the same target, but it can be used to find some undervalued targets.
2. As the certainty of the project increases, the airdrop yield coefficient may also be partially included. This needs to be observed, whether it will raise the price to F=1K=20 when the price is 5.6?
Now observe the fee indicator. Whether it’s the top-ranked target or the overall protocol, they have all declined from the short-term high point. It is not suitable to chase high prices. You can only observe fee data in real time, perhaps there are individual counter-trend targets.
What else can be done?
1. Bot+social is a good direction. In the early stages, using rules and utilizing bots, or becoming a human bot, can make money and develop bot projects. According to 21.co analyst TomWan’s monitoring, more than 113 Bot contracts have obtained over 20,000 keys (shares) on friend.tech and made profits exceeding 2 million US dollars. The most profitable Bot contract is 0xcc218bbd21e14944fcc121d161c9b9ae71b9cc85, with an income of 569,000 US dollars.
2. Users with many followers can do self-purchasing market-making and refer to the formula calculated in our article.
3. The scarcity price discovery system can be improved, such as creating a more user-friendly third-party dashboard?
To display: market-making situation, buying and selling behavior indicators, timely information summary by creators, all of which are extremely needed.
Pricing each person’s share may not be the top technical issue, so it requires more joint efforts from various products and resource injections from various parties. It is difficult for a single winner to take all, and the value of third-party data analysis platforms can be highlighted.
Even third-party data platforms can play a guiding role in a specific track.