Lei feng's network: author zhangxi dream, founder of GrowingIO, the former Director of LinkedIn. Cath Kidston iPhone 6s plus cases
Today and we may have heard the news that LinkedIn was $ 26.2 billion acquisition by Microsoft, a nearly 50% premium offer, the world's largest professional social network, is the world's second-largest SaaS (software as a service) vendors into Microsoft's fast-growing business cloud strategy. Many of my friends ask why LinkedIn has had such high valuations in the past, why Microsoft would buy LinkedIn premium 50%? Many of my friends asked me, "a social network worth? "" The price is high, or low? "
Core is often very simple in fact, mergers and acquisitions, valuation, origin of the premium is "growth". Microsoft to buy LinkedIn in a way that, to achieve further growth through mergers and acquisitions. It is important, and LinkedIn over the past 6 years from about 70 million years of corporate revenues, grew to $ 3 billion turnover business, five-year growth of more than 40 times. The growth in business services, which is amazing.
6 years ago, my first LinkedIn company meeting to hear the words of Peter Drucker, he says: If a thing if you can't measure it, you can't grow it. The core idea of this sentence out LinkedIn corporate values. Growth data analysis, data-driven realisation, liquidation to further promote growth. And this culture reflects the inside spread of the lean startup culture in Silicon Valley, startups must do three things to--Build (build), Measure (measure) and Learn (learn). This sentence in the past 6 years have been verified through a variety of ways in the product, operations, sales, marketing and other areas are a large practice.
A lot of people have doubted LinkedIn's valuations are too high, in fact Wall Street valuation of LinkedIn, based on a lot of very basic indicators. One important formula is cost (CAC) and the user lifetime value (LTV) relationship between, LinkedIn Gets the SaaS enterprise customer costs far lower than ordinary competitors. We used to create sales leads data system as a whole, for example, customer success analysis systems, marketing and data analysis system, product analysis systems allow different sectors completely data driven. Here is as a direct result, LinkedIn comparison with ordinary well-run SaaS company, her CAC/LTV ratio, and only about half its rival. The total cost of sales and marketing, 1 time lower than competitors or similar companies more than double. This makes the whole of equal resources in support of growing several times faster.
(Source S1 analysis of self-www.tomtunguz.com to LinkedIn)
Because a lot of customers are enterprise-level customers, LinkedIn, enterprise-class customer efficiency is one of the best companies in the industry. Realisation of the data drive the entire team (sales, marketing, operations, product) with super fast speed to obtain the customers, most efficient reduces the user's loss in the flat period, existing customers on the realisation of efficiency and growth. This is the core reason LinkedIn has been given high valuations on Wall Street.
|LinkedIn realisation of the early war
Data is LinkedIn growth strategy which is a very important aspect, in terms of product design, business operations, and data is a very important part. LinkedIn is set up in late 2002, 2003 operational framework on the basic design, established early it will have user data and realisation framework clearly.
Is the overall strategy of three circles, the first is the growth of users, use and activity, and the second is to produce a large amount of data, then data, business liquidations, and growth, again promoting users, data, liquidations, and growth data.
Havermann (LinkedIn founder &CEO) design LinkedIn strategy when he collected a large number of user information three ways-- Cath Kidston iPhone plus case
The first, variable by user's basic information, such as companies post jobs.
Second, the number of users grows to a certain level of time, B2B companies advertising.
Third, when there are a lot of people's information, company recruiters will use this platform to find candidates.
Liquidated the way he wanted to very clearly, but not to do on the first day, is the users experience and use of core concern to him, is the growth of overall growth produces large amounts of data, he learn from data and did the same in the future.
LinkedIn began at only 10,000 users with data-driven business. Early days, founder of the first user gets completely on cold starts, all joint founder and early incarnations of the 10 employees, everyone needs to pull 500 friends in, and that's why he gets the first 10,000 users.
Zhihou, attracted the first product manager, start the next round of growth from 10,000 to 25,000, and this time they went to the observation of two channels, an e-mail, a search:
LinkedIn came out founders all have rings, so there will be users to search LinkedIn, or search for people. We found data, users who come in from the SEO channels than e-mail invitation came in about the number of people, but in the product 3 times times more activity on the platform. This is not thought of before, and made a decision: If you want to get the same number of users, they are more willing to invest the resources in the use of the frequency higher, more willing to spend time on here, so give up low-active users, focus on active users.
I think this is his strategy execution which first thing the right thing to do.
| Should entrepreneurs from when to begin to focus on data?
Greylock's investors, product manager of the former Pinterest. Growing very fast at that time, they were times of growth each year, he summed up a framework throughout the product life cycle, when founder should be sensitive data.
At the earliest, and does not require too much data, by virtue of the founders ' intuition, intuition, product manager, making decisions accounted for a large proportion. But later, data management is becoming more and more important, a person can not always win at the Casino, a team grows, not all employees have intuitive decision making power, data driven decision-making to ensure efficiency in the future.
Data will tell you a lot of signals, those signals so that you have a standard, and the space available for growth, you assume that quickly verified. We are now in continuous optimization, conversion rate 20% us today.
Sensitivity and judgment can be passed to your data over time develop.
LinkedIn's CEO is 5:30, got up at six in the morning every day, send large amounts of mail, why increased search efficiency, why advertising revenue is so yesterday, the product manager will follow up, company-wide data analysis will follow up, everyone in the company will follow up. Afterwards, we said our top analysts who is CEO, all data if he were in charge. In 2014, I invited him to do a share in our group, we ask him, you don't see so many documents not annoying? He said that is not a report to him, like a heat seeking to, as he saw it, will know where the problem is. And later data has become his feeling, intuition and in-depth use of products for data, so he quickly navigate to where the problem lies. This is why Net Income of LinkedIn, SaaS enterprise than many losses in the financial statements on a cause of much better. This again for the LinkedIn valuations improved.
(Source S1 analysis of self-www.tomtunguz.com to LinkedIn)
| Do only one thing if a company-wide, what is this thing?
LinkedIn annually repeated a question to be asked is: If there is only one thing to do the whole company, what is it? Use numbers to prove: a week added to 5 contacts the user, their retention/frequency/retention time is not added to the user 5 contact three times to five times, this is the magic number they find driving growth.
But very, very few such people, and their entrances to increase social relationships. LinkedIn also has an upload address book feature, also recommend who you might know, and put these functions in the entrance to the individual product pages.
LinkedIn doesn't know why when the first increase social relationships will be retained, we have at least two hundred or three hundred different indicators, and no single index can tell us, it is because of this reason.
But after weighting the results are these users spend a lot of time, indirect realisation becomes possible. But the product manager to simplify very complex problems, let all things pay attention to this point: pay close attention to the magic number, so that more users add to 5 contacts in the first week. So, when growth was very fast.
Should become a data-driven culture
Data-driven primary is the 1th, CEO to realize the value of it; 2nd, we need a basic framework and methodology, the framework is very simple, that is, three, an idea quickly landed, authenticate into next loop; 3rd, must become a data culture.
LinkedIn company has a data culture:
Product: today there are 400 million users, but starting from 10,000 to 25,000 users, with data analysis. Channels to the user activity was discovered in 2004, for example are not the same, decided to do more active users.
Customer service: the user use the data to determine which customers will churn. Decline such as the use of customer loss, customer service the daily observation of each customer company index, to follow up on the contacts increased customer retention.
Sales Department: 95% more sales every week in user behavior data to determine which companies have the possibility of purchasing services. They applied to each customer data rankings, according to the of high use, frequency of use, last sort of proximity and other factors, targeted interactive sales team customer service team. Predict customer churn, customer needs, sales staff, customer service staff for assistance.
Marketing Department: every week optimizing advertising with data, price changes, email marketing, offline activities measures to promote sales and to enhance the effect of it.
For many years, in the United States's life is very comfortable, I came away from LinkedIn, is because we have experienced the power of data-driven. 2010 we do the sales analysis, according to company rankings, make sales at the most active and least active five users, brings more than 200% to LinkedIn's growth.
Data-driven variable number important?
Business, first you have to have a good idea, so he quickly landed and we use data to prove it is not efficient. Now flow more and more expensive, so we need rapid cycle method, use data to prove that we do is effective, this effect can be quickly stacking and stacking to form future growth, this is the core of the lean startup.
For example, Web site user registration, everyone else is doing, but there are a lot of holes. LinkedIn is optimized for several years, very little change, you can multiply the changes brought about.
GrowingIO user registration step is three pages, there was a time, our final registration conversion rate is 7.7% and sounds 8% and 15% can do? But a lot depends on the details, we registered conversion rate through the browser did a group, found that Chrome success rate of registration is 12%, IE success rate of registration is 1%.
Because we use a new Java framework, in the old version of Windows is not supported by your browser. So, we just increase the success rate of registration of IE, you can raise the overall success rate.
Before the data analysis, we looked at the many references, just a SaaS software, basically from visitors to the final success of the register should be about 5%, we thought 7% was all right, but a lot of people want to come in and want to buy something and can't buy, because he was stuck in the middle.
Why did I leave LinkedIn to build mobile data analysis company GrowingIO?
Over the past more than 10 years of work experience, see, hand-practical data analysis on a number of projects. See value in data analysis in a variety of large enterprises, which is more than many people imagine. This data-driven value to show in various enterprises. But we also saw that many companies did not do the simplest of three things, missed opportunities for using data-driven growth.
1. There is no recognized the enormous value of data analysis.
2. do not have very simple methodology and framework for analyzing data, enterprises do not have enough talent to this framework.
3. does not use the correct, suitable for the modern trend of the analysis tools to do more with less.
This is why our business GrowingIO. GrowingIO is good for many businesses, he is not just for big Internet companies, in fact, small enterprises don't have the resources and time, needs tools. Today is the era, how to use tools to quickly realize the value, is the embodiment of a core competitiveness.
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