Ten thousand words analysis: How to explore user growth strategies?

A comprehensive business research and intelligence system is an important part of business growth. This article uses 10,000 words to analyze in detail how to dig out growth strategies. Let’s take a look at it~

• As a new project, without data and direction, how can we find a growth strategy?
• When you are in a fierce battle with your competitors, how can you monitor the movements of your competitors' products?
• As an investor, how can you discover projects as early as possible and find out their real data?

First, you need a comprehensive business investigation and intelligence system, which is also an important part of business growth.

This article will use 10,000 words to introduce a business research method in detail, hoping to help everyone answer these questions.

1. Why study corporate growth?

Alan and Yolo are two biology graduate students with a hacker spirit. They graduated from Imperial College London and are currently focusing on studying growth hacker cases and tools.

We strongly believe in deriving from axioms to theorems and then obtaining the truth through reasoning.

As science fiction fans, we realize that commercial society is like the dark forest in "The Three-Body Problem", where various civilizations in the universe are competing with each other for the power to survive and develop.

Competition is everywhere: competition at the cellular level has been going on for more than 4 billion years, competition at the species level has been going on for hundreds of millions of years, competition at the human level has been going on for tens of thousands of years, competition at the national level has been going on for thousands of years, global competition has been going on for hundreds of years, and commercial competition after the information explosion has been going on for only a few decades.

Let us extend the axioms of "cosmic sociology" in The Three-Body Problem to business, and first think carefully about these two original propositions:

• Original proposition 1: Survival is the first need of civilization.
• Original Proposition 2: Civilization continues to grow and expand, but the total amount of matter in the universe remains the same.

We can transform it and transfer it to business:

• New proposition 1: Growth is the first need of an enterprise.
• New Proposition 2: Business survival requires continuous growth, but the user attention and purchasing power that can be pursued are limited.

Whether these two new propositions can become axioms remains to be explored, but we find that companies that can continue to expand become stronger and stronger, constantly confirming the "Matthew Effect" and the "80/20 Theory."

This also makes us realize that successful people must have improved their success rate through replicable elements.

As one of the oldest post-90s, Alan resigned from his job with Yolo two and a half years ago and began to study and practice growth hacking. His goal was to understand how companies can rely on experiments and data to establish replicable and efficient growth models before the cold winter really comes.

We did not join any Internet company because we wanted to maintain independence and focus on research. Over the past two years, the two of us have completed a number of growth experiment projects and written more than 500,000 words of research reports. It is no exaggeration to say that we treat this topic as a doctoral thesis.

Why did you choose this direction?

People often start from their own situation, judge a trend, and then make a choice.

This method of deriving general rules from special cases is called induction .

When I was in college, apart from attending classes, I was devoted to playing games. It was not until I entered society and started working that I began to have a sense of crisis. For example, the following two signals acted like alarm bells and forced me to make changes:

Signal a:

I took the college entrance examination in Jiangsu in 2009. That was the college entrance examination with the largest number of participants in Jiangsu's history, with a total of 546,000 people. Then, the number of people taking the college entrance examination began to decline year by year. In 2016, the number of college entrance examination candidates in Jiangsu was only 360,000, which means that there are fewer and fewer young people. This trend also exists nationwide, but it is not as obvious. I only realized this problem when I started looking for a job in early 2015.

(College Entrance Examination Registration Guide, http://edu.sina.com.cn/zt_d/gkbm/)

Signal b:

After I started paying social security, I studied the mechanism of social security and found that my mother would retire this year, and my father would retire soon. As an only child, I have to support two elderly people, while my parents' generation only had to support 0.57 elderly people per person. This also means that my theoretical social responsibility is 3.5 times that of my parents.

(As a glorious only child, my mother can receive a one-time payment of 3,600 yuan after retirement. But when I entered society, I realized that the price of being eugenic is to take on double the social responsibilities.)

These two signals indicate that the golden age of my country's baby boom generation has passed, which means that the "old babies" of our parents' generation have also reached retirement age. Moreover, this wave of "old babies" has a serious shortage of children due to the family planning policy. Every "second-generation baby" like me needs to be N times more efficient than their parents in order to keep society functioning as it is.

(In my calculation formula, N = "half of the number of brothers and sisters of your parents' generation" divided by "the number of you and your brothers and sisters". For example, my parents have 7 brothers and sisters in total, and I am an only child, so my N is half of 7 divided by 1, which is 3.5)

The overlapping area of ​​the ellipses in the figure below is the starting point of the decline of the golden generation, which was around 2015-2016. This was also an opportunity for us to start our own business, because my intuition told me that if I didn’t change, I would be waiting for death.

(I couldn’t find the original source of the picture, but it was said to be Wind Data.)

Similarly, a company is a collection of people and will inevitably face the same problems. If you want to achieve higher labor efficiency, you must change the status quo. I personally think there are several directions:

1. Try every possible way to improve labor efficiency, streamline operations, and use automated tools rather than manpower to expand scale.
2. Abandon the thinking of existing stocks, explore the incremental market, and then attack the existing market by reducing the dimensionality.
3. Develop the lower-tier markets or go overseas to markets with demographic dividends or where competition is less fierce.

In order to work towards these three directions, we studied domestic and international cases, tools and methodologies of growth hacking. After two years of hard work, I finally got the hang of it.

2. Growth Engineering v1.0

In our research, we found that business growth is never a fancy trick that can be achieved overnight. Instead, efficient growth engines are carefully designed systematic projects. There must be engineering principles behind it, so we named it "Growth Engineering".

Growth engineering is not something we created behind closed doors. Historically, this term appeared earlier than growth hacking, dating back to 2005.

The term growth hacker can be easily misunderstood and may be considered a means of taking shortcuts, so we tend to emphasize systematic engineering. For example: We’ve written before about some cases where growth hacking was misused. The article is long, so read carefully: Want to learn growth hacking? You must avoid these 6 misunderstandings (with 12 cases)

The purpose of growth engineering is to integrate information data, available resources, and technical capabilities to build a systematic and efficient growth engine for the enterprise.

In practice, we simply divide growth engineering into two steps:

1. Business Reverse Engineering: Deconstruct business cases, reverse growth strategies and data, and find growth levers.
2. Growth Forward Engineering: Use lean startup thinking to polish your product, then apply growth levers to yourself and build a growth engine.

Since it is a systematic project, the core is still engineers.

Whether in Silicon Valley or China, leading companies have established corresponding positions: growth engineers (Facebook, Toutiao, etc. have similar positions).

It’s worth noting that we don’t consider growth engineer a technical position. His first priority is to understand the logic of business, followed by understanding the boundaries of technology, and writing code is the icing on the cake.

(What is a growth engineer? Source: SMASHING BOXES)

We think so because we don’t have a technical background. We just know a little bit about Python and PHP, and have some research on front-end code, which can be mastered quickly.

But this does not prevent us from accomplishing tasks such as crawling and analyzing data, building websites, and automating marketing processes. This is all thanks to the mature third-party tools and open source codes on the market, so we don’t have to reinvent the wheel. Moreover, we believe that only a few leading companies have the ability and need to develop their own marketing tools. For most companies, developing tools is unprofessional and unwise.

Chiefmartec, an American company, summarizes a blueprint of the TOP5000 Marketing Technology (MarTech) every year. Each color block represents a type of marketing technology with certain functions, and each small logo represents a marketing technology. I estimate that the total amount of commercialized MarTech in the world is over 10,000.

(Martech landscape, Source: Chiefmartec)

During the two years of research, we tried about 300 representative marketing technologies (it cost a lot of money, but I think we earned it back).

We found that these tools can also be categorized according to the two aspects of growth engineering:

1. The first type is tools that provide intelligence gathering (Spying) and competitor data (Competitor Analysis), accounting for about 15%. We regard this type of tool as an entry-level weapon for commercial reverse engineering.
2. The second type is Marketing Automation tools, accounting for about 85%. They can help companies achieve data-driven and experiment-driven processes more easily, while expanding business scale through automation. These tools can help you get more results with less effort when building your own growth engine.

At the same time, these MarTechs can also be connected to each other using APIs. What’s even more interesting is that you don’t need to know how to write code. You just need to visually call modules and design the connection logic. For example, you can connect hundreds of tools on the Zapier platform, which we use when we publish our Growth Daily.

These tools have been mentioned in our previous articles. The article is very long, so read carefully:

How to use growth hacking thinking to create a public account from 0 to 1?

How to build an e-commerce website with zero code and achieve automated marketing?

Of course, the pitfall here is that you need a lot of time to do debugging, and you need great patience to familiarize yourself with each tool and establish a process. Very few people are as free as we are to study these tools and methodologies, which is probably why these tools have not been popularized.

But many giants are using it this way. Even the old luxury brand Gucci has installed dozens of MarTech on its website, including tools for email subscriptions, online chats, personalized recommendations, etc. They even hired full-time digital marketers to try out various marketing tools on the market.

You can even use tools like Similartech or Builtwith to monitor what MarTech your competitors used and when. This shows that MarTech has become a very important link in Europe and the United States.

3. Commercial reverse engineering

After introducing the general concept of "growth engineering", let's start with the first step of growth engineering - "business reverse engineering".

Simply put, it means to thoroughly study competitors and the industry, and to combine the strengths of all parties to become a tactical master.

After writing more than 500,000 words of growth case analysis, we increasingly feel that the logic of exploring growth strategies and solving locked-room murder cases are exactly the same. We boldly redefine growth: the essence of company growth is to "murder" users' attention and purchasing power through products, and the "criminal method" is marketing that leverages human nature.

Why do we think this way? Because products, marketing and users interact with each other, if you separate them and look at them separately, you will only see a small part of the picture and will not be able to understand the overall situation.

Marketing and products generally test your understanding of human nature and needs. The specific forms of expression are ever-changing, and it is easy to fall into superficiality if you only look at one or two tricks. In the end, the core indicators did not increase, but only a bunch of vanity metrics were added, and it was still a mess.

So, in order to get a more complete picture, we started studying books on intelligence and criminal investigation and building research models, hoping to comprehensively monitor growth cases in the market.

3.1 The First Law of Commercial Reverse Engineering: “Rocard’s Law of Material Exchange”

In order to crack the "secret room crime" in the market, we referred to the "Locard Exchange Principle" proposed by the famous French investigator Edmond Locard in his "Course of Criminal Investigation".

The criminal's crime inevitably leads to three kinds of material exchange:

1. Trace materials: criminals will leave fingerprints, footprints and other traces at the scene;
2. Physical substances: criminals will leave traces of blood or hair on their bodies;
3. Impression material: Criminals will have an indelible memory of the facts of the crime, thereby subconsciously disclosing information.

Similarly, we can boldly assume that company growth will inevitably lead to three types of information exchange:

1. Trace substances (marketing traces): traces of promotion and publicity left by the company in the market;
2. Physical substance (product change): The company leaves an iterative mark on the product;
3. Impression material (information disclosure): The company and related personnel may intentionally or unintentionally disclose company-related information.

We call this principle the "Lunfang exchange principle".

This name is created by us. Lunfang is the abbreviation of Alan and Yolo. You will definitely not find it on Baidu. This is also regarded by us as the first law of commercial reverse engineering.

Therefore, when we need to interpret new business cases, we will inevitably pay attention to these three types of information: marketing channels, product changes and our own information disclosure.

At the same time, the intensity of these three types of information exchange varies for companies at different stages. As the company continues to mature, its marketing intensity continues to increase, its product anti-reconnaissance capabilities continue to increase, and its level of information disclosure also continues to increase. Therefore, we need to prescribe the right medicine to find a breakthrough in the growth strategy.

1. For early-stage companies: Most companies will spend their energy on polishing their products, with only a small amount of marketing actions and unreliable PR information disclosure. At this time, the product form is relatively early, iterations are frequent, and the anti-crawler capabilities are weak. It is often rewarding to start from product data.
2. For companies in the development stage: all three types of information exchange will gradually strengthen, especially marketing methods will become more abundant. At this time, using tools to find all their marketing channels can help you figure out their tactical intentions, conversion data and even strategic direction.
3. For companies after listing: information disclosure reaches its peak, financial reports and investor reports come one after another, and most short-selling institutions also start investigating a company from the financial reports. At the same time, many growth strategies can be clearly seen from the financial reports.

So how do we obtain these three types of information?

Of course it is legal to use public data, such as search engines, crawlers, third-party tools, etc. According to research by the US intelligence system, approximately 90% of the world's intelligence is public intelligence, and only 10% of intelligence needs to be obtained through secret channels.

Reasonable use of public data and intelligence can achieve results beyond your imagination.

3.2 Three major methods of commercial reverse engineering

In order to dig deeper into the project, we referred to the common methods used by European and American intelligence agencies and even short-selling agencies, and roughly understood three types:

1. Open Source Intelligence (OSINT): collecting intelligence from open sources;
2. Reverse Engineering: Reverse design principles from finished products;
3. Social Engineering: Infiltrating and obtaining information through interpersonal communication.

All three methods are full of hacker spirit and are considered compulsory courses by intelligence and security personnel. They can be seen in intelligence competitions at the national and even war levels.

Below we will introduce and give examples one by one:

Method a: OSINT is the abbreviation for Open Source Intelligence. After the 9/11 terrorist attacks in the United States, the CIA established a dedicated OSINT department in 2005.

At the same time, in the era of digital marketing, digital business public intelligence is also growing exponentially. Commonly used means may be search engines, crawlers and various spying tools. These public intelligence are enough to reveal almost all the details of any company. We will list the public information sources and tools in detail later.

(CIA emblem and creed)

Method b. Reverse engineering is a methodology that uses products to infer design principles. It was first used for military or commercial products, and is also applicable to products in the Internet era. Any product change is made with a purpose. When we combine the change style with market intelligence, there is a high probability that we can infer the project party's tactical means and strategic intentions.

For example, WayBack Machine is a special tool used to view past versions of a website, which records information on 435 billion web pages since 1996.

In the recent short-selling report on Pinduoduo released by Blue Orca, the WayBack Machine's web backup was cited. It pointed out that Pinduoduo's website in December 2017 showed that the group to which Pinduoduo belongs currently has more than 5,000 employees, but Pinduoduo only acknowledged 1,129 employees in its prospectus. Regardless of how many people Pinduoduo actually has, at least this change on the website directly exposes their intention to hide the number of employees. This gave the short sellers a handle on the company, and it was believed that the company was concealing labor costs and inflating labor productivity.

(Pinduoduo short-selling report, source: blue orca)

Method c. Social engineering is a method of penetration targeting people, known as The Art of Human Hacking. It is used to dig out information disclosure related to people. It is a basic theory for intelligence security personnel. If you are interested, you can buy this book and study it.

For example, if you want to investigate the structure of Facebook's growth team, you can find relevant people at Facebook through Linkedin. However, you need 2nd or 3rd degree connections on Linkedin to access detailed information of relevant people. At this time, you need to establish Linkedin friends with 1-2 people from the Facebook growth team. If you pretend to be an intern and successfully add them as your friend and scrape the Facebook Growth team's information, that would be a social engineering penetration.

(Facebook’s headquarters is at 1 Hacker Road, no kidding)

You may think that the above methods are not very common or rarely mentioned, but in fact, people just don’t call them that.

Let's review the video of Jobs ' interview in 1995 (you can search it on Bilibili) and feel the textbook hacker spirit:

1. At the age of 12 (1967), Jobs demonstrated strong social engineering skills, that is, the ability to infiltrate and obtain information through interpersonal communication. At that time, he wanted to assemble a calculator but was missing some parts, so he found the phone number of Bill Hewlett, the co-founder of HP, through the phone book information and made a cold call. It took him only 20 minutes to successfully obtain the parts he wanted for a frequency calculator, and he got the opportunity to intern at HP during the summer vacation. Jobs said this incident changed his life.
2. When 15-year-old Jobs saw someone in Esquire magazine saying there was a way to make free phone calls, he didn't believe it and thought the author was bragging. He sneaked into the Stanford Linear Accelerator's Science and Technology Library and looked through all the books. On the last row of bookshelves, he found a technical manual from AT&T, and discovered a major vulnerability in the US telecommunications network. He went home and spent three weeks with Woz making a "Blue box" that could make free calls around the world using analog telephone signals. They even tried to call the Pope at the Vatican pretending to be US Secretary of State Henry Kissinger, although the prank did not succeed. They found vulnerabilities in huge systems through public intelligence and exploited them, which falls under the category of open source intelligence (OSINT).
3. When developing the MAC, Jobs made drastic improvements to the production line. He visited about 80 automated factories in Japan and returned to California to build the world's first automated production line for computers. Here, he demonstrated his strong reverse engineering ability, that is, the ability to obtain design principles and reuse them by deconstructing finished products. Not only that, I am even more curious about why 80 factories allowed him to visit. This is probably the legendary "Reality Distortion Field" of Jobs.

In essence, all of the above techniques pave the way for "borrowing".

Many people may say: I also copy secretly, but it seems to have no effect. That only means that your awareness is not high enough. Can the things done by scholars be considered stealing?

Let’s take a look at how Jobs explains his understanding of “borrowing” at the end of the video.

When the host asked Jobs: "How do you know which direction is right?"

Jobs pointed out: No matter what you do, you need to be familiar with the outstanding achievements of mankind in various fields and try to apply them to your work. I never think it is shameful to borrow other people's ideas. Ultimately it is up to your taste to decide.

At the same time, he quoted Picasso's famous words:

“Good artists copy, great artists steal.”

Of course, Jobs himself has always adhered to this principle. For example, he really likes the design of German Braun electrical appliances.

Below we have listed some public intelligence sources and some commercial tools for marketing, products and information disclosure for your simple reference.

The core is these three methods: open source intelligence method (OSINT), reverse engineering, and social engineering.

3.3 Commercial Reverse Engineering Process

After collecting a large number of clues, the detective's task is to clear the fog and restore the truth of the incident; similarly, the task of the "growth detective" is to sort out the growth model from the clues and find the levers for growth.

Therefore, we referred to the criminal investigation system established by the US FBI: Criminal Profiling, and evolved it into Growth Profiling.

(From the investigation manual on the FBI official website, Source: https://vault.fbi.gov/Criminal%20Profiling)

Let us first introduce criminal profiling. Its principle is to infer the psychological state of a criminal based on his behavior, and thus analyze his personality, living environment, occupation, growth background, etc. This is a method of reasoning from the specific to the general, which is an application of the induction method I mentioned earlier.

There is also a school of investigation called "Deduction", the typical representative of which is Sherlock Holmes. My partner Yolo is very good at this kind of reasoning, but I won’t go into details here.

Here are the six steps of criminal profiling summarized by the FBI. If you are interested, you can read the paper yourself.

The content of their manuals is all in slanted text, which is not very friendly to people with OCD.

After studying the investigation flow chart, we adapted it into a business reverse engineering process:

a. Clue collection: Refer to 4.1 "Law of Information Exchange between Ethics Parties", and focus on clues about marketing promotion, product changes and information disclosure. Refer to 4.2 for clue collection methods.

b. Diagnostic model: It mainly uses the seven-question analysis method (5W2H) first developed by the U.S. Army Ordnance Department to find the core problem. As the saying goes: Writing down the problem clearly is half the solution.

c. Growth strategy evaluation: In the FBI profile, it is mentioned that the murderer's criminal methods usually meet two characteristics:

• One is behavioral consistency, which means that the same murderer will use the same crime method;
• The second is homology, which means that in the same type of cases, the murderer's characteristics and methods have many similarities. For example, murderers like to wet the bed, set fires and abuse small animals.

Similarly, there is not much new in the business world, and no matter how unique something seems, it must have a historical reference.

This includes: the same operator's operating methods in different growth cases are usually consistent; second, in cases with similar business models, the growth strategies used will also have many similarities, because the operator himself will definitely learn from competitors.

So, here we need to build a broad strategy library and be familiar with the various ways of playing in business history and the effects they can bring.

d. Build a growth model: This step requires placing the growth strategy into the growth model to ensure that there are no omissions, and start to derive the conversion rate of each step to find the growth lever.

e. In-depth investigation: When there is insufficient evidence, further investigation is needed and new clues need to be submitted for cross-verification.

f. Implementation of “capture”: I like to take the research report and talk directly with the project’s operator.

3.4 Interpretation of some cases of commercial reverse engineering

We have only disclosed a portion of the interpretation cases before, and there are still many that we have not disclosed, otherwise the PR people will cause trouble for you. But I can understand that if a project in China shows some signs of improvement, there will be N teams following up the next day.

We have written some cases before, which are quite long, so read with caution:

We crawled 200,000 data points and dug deep into the popular "Xiangwushuo"

Grab 50,000 data and decipher the second-hand book applet of "buy at original price + sell at 15% discount"

4. Importance of Commercial Reverse Engineering

The purpose of business reverse engineering is to extract intelligence through data and then reversely explore the growth principle of a project. The key word in the middle is naturally intelligence .

In English, intelligence and intelligence are the same word. So no matter how people talk about artificial intelligence or business intelligence, don’t forget the origin: being able to extract intelligence from data and guide people to make decisions is a manifestation of intelligence.

So let’s go back and think about the question at the beginning of the article: Therefore, even cold-start companies have piles of public data to mine. The difficulty lies in extracting actionable business intelligence from the redundant big data.

I personally think that in the era of lean entrepreneurship, every company needs to master a system that converts public data into business intelligence, so as to establish an important decision-making engine for the company and avoid wasting resources on inefficient attempts.

Here we would like to give examples of intelligence work of Chinese and foreign companies to tell you the importance and trends of intelligence work.

4.1 Meituan’s Competitive Intelligence Department

In the fiercely competitive market, one of the sciences that players must learn if they want to survive is Competitive Intelligence.

I once came across an article that said Meituan has a special department that keeps an eye on the data of competitors in various industries and will enter the market and prepare for battle when the time is right. I can’t find the source of the article, but that doesn’t stop us from verifying: Does Meituan have such a team?

Here we just need to use the search engine to study the clues of information disclosure. The key words are "Meituan + competitive intelligence".

We can find three recruitment clues on the first page:

Clue 1 : In July 2013, Meituan recruited development engineers in the field of competitive intelligence on Douban.

Clue 2 : In September 2013, Meituan released a similar “competitive intelligence engineer” on the Shuimu Tsinghua Community. This information disclosure contained richer content. First of all, we can speculate that it must be difficult to recruit such talents, and popular channels such as Douban may not be very accurate. It is very likely that no one has been recruited in two months, so I will try the forum of Wang Xing’s alma mater, Tsinghua University.

At the same time, in order to increase the appeal of this post, the poster used an impassioned tone and began to speak from the heart.

The information revealed can be summarized as follows: competition in the group buying business is fierce, intelligence is crucial, and everyone from Wang Xing to Xiao Bing attaches great importance to intelligence work. Although our intelligence team has only been established for a short time, we are all very awesome.

Clue three : In 2015, Meituan started recruiting competitive intelligence engineers for its hotel division.

At the same time, let's collect some public reports about Meituan, which involve the time when Meituan's business was launched:

Meituan Waimai was launched in November 2013

In July 2015, Meituan Hotel and Travel Division was officially established

In October 2015, Meituan-Dianping merged

Therefore, it can be roughly seen that the competitive intelligence personnel recruited in 2013 and 2015 seem to be related to the development of new business lines in those years.

At the same time, we found another piece of personnel information that can be used for cross-verification. It was reported that in 2016, Lv Heng, a technical expert who built a complete intelligence system for Meituan, resigned and founded the human resources SaaS "Xinren Xinshi", raising US$50 million.

Finally, we also searched and found an article on Jianshu called "Meituan Dianping Dictionary", which records all of Meituan's internal terms.

One of the entries is Competitive Intelligence System (CIS). The links all jump to Meituan’s own employee backend, and you need to log in to have permission.

Just a simple search, isn’t it quite rewarding? You can sort it out again according to the 5W2H method, I believe it will be clearer.

You can recall again: Meituan always strikes back in the market and manages to defeat so many competitors. I believe the importance of their intelligence work is self-evident as they were able to successfully break through and go public this year.

(BTW, I am also a heavy user of Meituan. I feel that there is something wrong with the business logic of train tickets. I often think that I have bought the train ticket, and then find out that I didn’t buy the ticket when I arrived at the train station.)

Finally, I would like to share with you a post from Wang Xing’s Fanfou account. He forwarded a status at 1:43 am on June 19, 2015. It should be someone else’s quote, which is somewhat similar to the “Law of Information Exchange between Ethics” that we have been working on behind closed doors:

Being a competent CEO requires great knowledge: knowledge of products, the people, the market and the competition.

Although I haven't found the source of this sentence, I have to say it makes a lot of sense: the CEO's job is to connect products, markets and people to win the competition.

When searching for Meituan, I also noticed Toutiao, owned by Zhang Yiming, a fellow Longyan native of Wang Xing.

They also recruit intelligence analysts, but they are assigned to the risk control link, and their main responsibility is to continuously compete with cheating users. Judging from the description, the purpose of Toutiao’s intelligence work is not to launch wars abroad, but to fight against cheating internally.

Seeing this, I must introduce to you Palantir, the most mysterious B2B big data intelligence company in Silicon Valley, which is said to be valued at 41 billion US dollars.

4.2 Palantir, Silicon Valley’s Most Mysterious Intelligence Unicorn

This company was created by Peter Thiel, the Paypal gangster, the godfather of Silicon Valley venture capital, and the author of "Zero to One". Palantir is the crystal ball in "The Lord of the Rings" that can travel through time and space and see everything.

Before Palantir was founded, Peter Thiel's company, Paypal, was plagued by fraud problems. In order to prevent criminals from using Paypal to launder money, Paypal engineers had to develop a set of software to deal with suspicious fund transfers, and then analysts checked the screened transactions one by one.

But as transaction volumes increase, manual methods can no longer keep up with the criminals’ ever-changing tactics. After that, Paypal developed new tools again to find suspicious accounts and freeze them by matching users' past transaction records with current fund transfers, thereby avoiding tens of millions of dollars in losses. After Paypal was acquired by eBay, Thiel thought that PayPal's anti-fraud technology could provide services to the government.

——Excerpt from Sogou Encyclopedia, Palantir entry

Remember what I just mentioned: the CIA established an Open Source Intelligence Analysis (OSINT) team around 2005, after 9/11?

Coincidentally, the CIA helped Peter Thiel establish Palantir in 2004 through venture capital.

Therefore, we have good reasons to suspect that Palantir plays an important role in the CIA's OSINT system.

During Palantir's nearly 14 years of development, US intelligence agencies have gradually become its main customers, such as the CIA, FBI, DIA (Defense Intelligence Agency), the Navy, Army, Air Force, and police departments, etc. Palantir's users are mainly concentrated in Washington, with government business accounting for 70%. Palantir did not enter the financial field until around 2010, when it brought JPMorgan in. (Source: Sogou Encyclopedia)

The company's client success stories are also amazing, for example: it once helped the US government track down al-Qaeda leader Osama bin Laden; and helped JPMorgan avoid the financial fraud of former Nasdaq chairman Madoff.

(Peter Thiel and Trump seem to be a close partnership)

Recommended readings on Palantir:

1. Zhihu search "The most mysterious big data company Palantir (Part 3): The Dark Knight"
2. How Peter Thiel's Palantir Helped The NSA Spy On The Whole World.

I put this case here to illustrate that big data is not a gimmick. We hope that more people can return to their essence and use it instead of blowing bubbles and engaging in data trading. At the same time, big data is also the core weapon of national intelligence agencies. Intelligence agencies led by the United States attach great importance to this aspect.

A few days ago, when I was typing an article, I saw the incident involving Professor Zhang Shousheng and went to learn about it. I listened to his speech all night while typing, and I felt heavy-hearted as I listened. Professor Zhang is truly a role model for scientific researchers. Although science has no borders, the facts are always cruel.

The Office of the United States Trade Representative (USTR) did name Danhua Capital and Digital Horizon Capital in its 301 Report updated in November 2018. Both of these companies were nurtured by Professor Zhang.

301 Report address: https://ustr.gov/sites/default/files/enforcement/301Investigations/301%20Report%20Update.pdf

This updated version of the 301 Report, which is more than 50 pages long, mentions China nearly 500 times, directly targeting China. At the same time, China and the United States still have important business talks to complete in the next 90 days, and we hope they will go smoothly.

Most of the theoretical systems in this article are also based on research by US intelligence agencies and short-selling agencies. We also hope to "learn from foreigners' strengths." This article should also be translated into English and posted in the English world afterwards to see what everyone reacts. (The above sentence is probably not translated by mastering the skills of the foreigners...)

5. Conclusion

All you can see here is true love. First of all, I have to thank you for your time.

This article only introduces the first part of growth engineering, and there are still many things to be improved later. Anyone who is interested in this system is welcome to come to us for discussion.

Source: Growth Black Box