Lei Fengnet (search "Lei Feng Net" public concern) : Author Zhang Ximeng, Founder and CEO of GrowingIO ; Former Director of Business Analysis for LinkedIn, personally established LinkedIn's business data for more than 90 people Analysis team. The original text was sent to the GrowingIO technology blog and public number, authorizing Lei Feng.
Everyone says that data is important. Then, when should entrepreneurs start to pay attention to data? Is the company established? I think, of course not. In the early days of the product, data drive is a false proposition. Only when the real growth period is reached can the data burst into force.
Data analysis flows in formI have done many years of data analysis on LinkedIn and found that the difference between China and the United States in data-driven is still very big. Data analysis can be valued by some of the most important companies in the country. However, in the United States, data has become an important engine driving the growth of many companies.
Why do many domestic companies put a lot of emphasis on data analysis on the surface, but they end up in the form?
Many companies are in a period of frenzied growth, and the decisions that people make in their minds may have already yielded a lot of value. In this case, it is difficult for them to realize the great value that data decisions can produce. At the same time, they do not have much cognition of basic methodologies, and technologies and businesses do not understand each other, which further exacerbates the slow use of data and cannot see the realization of value. In the end, it becomes a decision based on feelings, rather than making decisions through data operations.
LinkedIn's data drive But we look at the United States. Take LinkedIn as an example. In the past 6 years, from a company with an annual revenue of about 70 million revenues, all of a sudden it has grown to a company with a turnover of 3 billion US dollars. This growth rate is amazing in the corporate service sector. . More than 6 years ago, I first heard Drucker's statement at the company's regular meeting at LinkedIn: One thing, if you can't measure it, you can't grow it . This sentence has precipitated LinkedIn's corporate values: growth drives data analysis, data drives realisation, and realisation further promotes growth.
In the early days, LinkedIn had a clear data frame of only 10,000 users, and it was data-driven to repeatedly ask questions that could be proved with data.
LinkedIn was established at the end of 2002, and it has clearly stated the user data and the framework for realizing it early in its establishment. Data is an important part of both product design and business operations. Haverman (Founder & CEO of LinkedIn) collects a large amount of user information and thinks of three ways of realizing:
First, through the user's basic information to realize, for example, the company issued positions;
2. When the number of users grows to a certain extent, B2B companies will vote for advertisements;
Third, when there are a lot of people's information, the company's headhunters will use this platform to find candidates.
The way to cash out he thought very clearly, but did not do it on the first day, his core concern is the user experience and usage, the overall growth, growth generates a lot of data, he learned from the data, the future Make money.
LinkedIn started data-driven business with only 10,000 users. This time to observe two channels, one is e-mail, one is search. From the data, it is found that the number of users coming in from the search engine channel is almost the same as the number of people invited by email, but the activity on the product platform is 3 times higher.
This was not thought of before, so a decision was made: if you want to get the same number of users, they are more willing to invest resources in people who use more frequently and are more willing to spend time here, so give up the less active users. , focus on active users .
One of the questions that LinkedIn repeatedly asks each year is: If there is only one thing that all companies need to do, what is it? Have to use numbers to prove?
Users added to 5 contacts within a week, their retention, frequency of use, and stay time are three to five times those of users who have not been added to 5 contacts. This is the number of magic numbers they have found to drive growth. . But at the time, such people were very, very few, so they increased social relations at all entrances to the product.
At the earliest time, LinkedIn did not know why increasing social relationships would generate such a large degree of retention. We analyzed at least two or three hundred different indicators, and at the end there was no single indicator that could tell us that it was for this reason. However, the result of weighting is that these users spent a lot of time on it, and indirectly become possible. The product manager simplifies the very complex issues and keeps everything focused on this one point: Let more users add to 5 contacts in the first week. As a result, rapid growth.
How do startups pay attention to data?The focus of each phase is different, and the growth period is the key period for data-driven. Although data is important, when should entrepreneurs start to pay attention to data? Since the establishment of the company began? no. In general, entrepreneurs will experience 4 to 5 products and their life cycle.
The first stage, cold start.
At this time, the company was particularly early, and driving it with big data was a false proposition - because of the limited number of customers and insufficient sample. They need to learn more about the needs of potential customers and "pursue" customers to use this product.
The second phase, the earlier period of growth.
Cold start is nearing completion.
Experienced entrepreneurs will begin to lay out some core indicators related to growth, such as day/month activity and retention.
The purpose of these indicators is not to measure the current performance of the product, but to have a comparable benchmark for future growth. And these indicators can tell us when we should do growth. If the product itself is not viscous, then to burn money and grow, it will not really grow, because the loss rate exceeds the growth rate. In the past, many companies that burned money were able to succeed because competition was less intense and users did not have many options. But today if your product is poor, retention is not high, word of mouth is not good, and no amount of money can be burned to obtain true core natural growth.
The third stage is the period of growth.
This stage can be seen as a great difference between a good startup company and an ordinary startup company—efficiency.
No matter the PR or the activity, it needs labor and time cost. How to find the most efficient channel in growth? This, I think, is the core competitiveness of PK among startup companies. If you do not do data-driven, rely on intuition, once and twice, but no one can enter the casino to win 10,000 times. Therefore, intuition needs to be combined with data, so that companies can quickly optimize all channels to improve the conversion efficiency per unit of time. Through the improvement and superposition of transformation efficiency, it becomes the company's core competitiveness. A data-driven company and a data-driven company, assuming an operational strategy, have similar capital reserves and customers, and the latter will surely win.
The fourth stage is the realization period.
The realization of business requires a high user base. Highly active and experienced users in general Internet products will be converted into paying users. Like a funnel, constantly sifting, which is to fight the efficiency of the operation. For example, the e-commerce user's conversion funnel is generally: access - registration - search - browse - join the shopping cart - payment, or return to the future. This is a very, very long funnel. To truly do a good job in data management, it is necessary to continuously track every aspect of the funnel. why? Because it cannot be measured, it is very difficult to grow.
A good company, especially a company that wants to earn revenue in the future, must pay attention to the efficiency of transformation in all sectors. This conversion efficiency, the means to be achieved, can be achieved through marketing methods, product improvement methods, and even customer operations methods. Each of these links is slightly increased, adding up to a multiple of improvement. This kind of multiplication will make it difficult to realize how large it will be if you have not done data management operations. For example, in the past when we were doing data-driven conversions on LinkedIn, we wanted to push an EDM, which was also distributed to 100,000 people, and the brain-turning decision was 0.01%, but after the data-driven department made a simple data model, it was also pushed. The conversion rate has increased to 0.3%, which is 30 times higher. If you do it every week, this conversion effect is still very impressive.
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