The biggest difference between Chinese and American companies in data analysis: the practice of growth hacking

1. Spending money ≠ growth, data-driven is a capability

In the past 10 years, China's Internet has developed very rapidly. Previously, the Internet economy was driven by traffic. As labor costs continue to rise and competition intensifies, it is no longer sustainable to rely solely on traffic, budget, and burning money to acquire customers and markets. If your customer acquisition costs are high, slow, and expensive, resulting in the business value not covering the costs, you will ultimately be unable to make a profit or realize cash.

The cost of acquiring a purchasing customer in China may be five times that of the United States. No vertical field in China is a vacuum. There will always be competitors with the same valuation or financing amount as you. If you spend money more efficiently than others, it will be easy to stand out and even kill your competitors. The prerequisite for spending money efficiently is to have data support and spend money based on data analysis .

Therefore, only by improving efficiency and helping a business grow at a relatively fast speed and lower cost can a company's core competitiveness be achieved.

The first step to achieving growth is to make good products so that users can stay in your app, website and services. In the past, when flow was king, it was like a leaky bucket. Because the amount of water coming in was so large, even if it was leaking violently, your bucket would slowly fill up.

But today, the water coming in is getting less and less, and the rate of leakage cannot keep up with the rate of water inflow, so it is impossible for this business to have any substantial growth.

Therefore, companies need to make their products have very high viscosity, a product that provides a very good user experience and people are willing to use it every day, which is like a bucket that does not leak. Then we can efficiently optimize various channels and bring in new users, so as to achieve explosive growth.

2. Four major differences between Chinese and American companies

In fact, this theory has been applied in the United States for many years. This is also a core difference we saw after returning to China. In addition, there are four very big differences in the entire enterprise market between China and the United States:

1. Do you have data-driven awareness?

Many companies have not yet realized the huge value that data-driven can bring to the enterprise, or only a few super-large companies have realized this. This makes the founders' decision-making and business perception far more important than data-driven. This is my first impression.

2. Whether to conduct data analysis practice

In the United States, data analysis, both as a product and a methodology, has been around for many years. Many Chinese companies are developing rapidly, but their development time is relatively short, and there is a certain difference in practical operational capabilities between them and the United States. The ability to conduct advanced data analysis is basically concentrated in a few leading Internet or large companies, among which Internet companies are more capable, while most companies do not have this operational experience and ability.

3. Do you use data to make decisions?

We found that the proportion of people who use data to make decisions in Chinese companies is relatively low compared to that in American companies. For example, at LinkedIn, where I used to work, I can’t say 100%, but close to 80-90% of people use data to make decisions and optimizations every day and every week. In China, through our understanding of our customers, including many paying customers, we know that relatively few people within their organization make decisions in this way.

4. Whether tools are used to replace manpower

The United States has evolved to an era where manpower is no longer relied upon to solve operational efficiency problems. They have fully entered the era of toolization, productization, and scale. Many companies in China are still at the stage of preparing to hire a large number of people, including senior data engineers and analysts, and even building the entire data system. The difference between them and the United States is quite large.

These four differences also determine the form of our products in China today:

First, companies are not used to using data analysis tools and think they are worthless;

Second, data analysis practice is not very large-scale and is only practiced in some core Internet companies;

Third, internal staff do not have enough experience using data analysis tools;

Fourth, data analysis tools are not yet fully developed.

3. Implementation and realization of growth hacking

Sean Ellis first proposed the term "Growth Hacker" and helped many Silicon Valley companies achieve rapid product growth, many of which have already gone public, the most famous of which is Dropbox. At that time, Sean was responsible for user growth at Dropbox. He spent one year increasing the user base and usage frequency by 500%.

A growth hacker is a combination of three roles: a marketer , a product developer, and a data analyst. The core of growth hacking is to obtain massive growth in the fastest way, at the lowest cost and with the most efficient means.

The popular Pirate Rule "A AR RR" model implements "Growth Hacker" into five executable steps, namely: user acquisition (Acquisition), user activation (Activation), user retention (Retention), user monetization (Revenue) and referral (Referal).

Here are some examples of well-known foreign Internet companies that implemented growth hacking in the early stages, covering the five aspects of AARRR.

1. Get users: Hotmail cleverly implemented the first wave of viral marketing by appending "Get your free email at hotmail." to every email sent by Hotmail;

2. Stimulate activity: LinkedIn sends specific emails to customers about classmates’ graduations, promotions, etc. to increase activity;

3. Improve retention: Facebook optimizes its products to adapt to the African network environment. In the early days, the African network speed was very slow and user retention was very low.

4. Increase revenue: Various discounts and value-added activities for Amazon Prime subscribers: such as the "$70 free shipping" activity, which increased sales by 150%.

5. Spread the word: Introducing Dropbox to new users can create room for growth.

Behind all these practices are data analysis, such as conversion analysis, retention analysis, channel analysis, A/B testing, etc. It is these value-oriented data analyses that implement growth hacking and continuously drive business and customer growth.

4. Value-oriented data analysis has a long way to go

The growth hacker framework is relatively universal, and is especially needed by Chinese entrepreneurs . In China's big data ecosystem, technology precedes the theoretical system, while in the United States, the theoretical system slightly precedes technology. For example, the growth framework is not a product practice framework, but a business management methodology framework. With this framework, it becomes executable by supplementing it with various products and tools.

Big data has been popular in China for three or four years, but many companies have not yet found a way to implement and monetize it. This growth methodology has been proven to be valuable by many companies, including LinkedIn, Facebook, and Airbnb, and can be applied six months after the company is established. This methodology is in great demand in the country, and can only create value for the enterprise when combined with internal operations.

We also want to correct a misunderstanding through products and practice. For many Chinese Internet companies, they believe that as long as they connect to your tools, they can immediately see efficiency. In fact, this is not the case. It is necessary to integrate the data-driven operation methodology into daily operations and use data analysis tools proficiently. This is a process of continuous cycle and continuous improvement. At LinkedIn, we did not achieve 50% growth through a single project, but rather through many small projects that evolved and iterated continuously, ultimately resulting in geometric growth.