For example. I joined LinkedIn four and a half years ago, and my first job was to support internal sales staff. I was lucky to join. There are only 500 people in the company, but I work alone to support 200 sales. These are the questions they ask me every day:
"Hello, Simon, which company should I call? Who is the decision-maker of this company? How should I contact this decision maker? There are so many of us, who will contact us? What kind of story are we going to tell when we get there? "
The background here is: LinkedIn had about 3 million company information at that time, which was extracted from the resume of each user, but as a salesperson, he could not call every company of these 3 million companies. Which company should he call most?
That is to say: first, which company should I call? How much is this company worth to LinkedIn? Because we are models; Customers pay a sum of money every year; The second question, who is the decision-maker of this company? For example, Google's 20,000 employees have to make 20,000 calls, or who should they call?
The third question, how to contact this person? You think, because LinkedIn is a professional social network, it still pays great attention to the relationship between people. We know that the right relationship and bridge can improve productivity. Step four, we have 200 salespeople on LinkedIn. Who should contact this company the most? The fifth question, we went, what story should we tell?
Now I use Linkedin data to answer these five questions one by one. As you may know, LinkedIn's biggest business is headhunting, accounting for about 60% of its total revenue today. First of all, can you use LinkedIn data to solve the problem of which company will spend how much?
First, we analyze each company and how many employees it has; Second, let's analyze how many people this company has recruited; Third, let's analyze how many people have lost in this company; Fourth, let's analyze where this company recruits people. What is the nature, type, title, position and function of his job? His position, his rank, step by step, these are all functions in our model.
Next, let's analyze how many HR employees, how many headhunters are in charge, their headhunting turnover rate, and how much work they do on Linkedin every day. Then when all this information is summarized, we make a seemingly simple but complex model, and the final result of this model is a number: US dollars.
That is: how much will this company spend on LinkedIn every year? With such a number, I said so much nonsense and finally gave it to the salesperson.
For example, at that time, Google predicted that it would spend 10 million on "headhunting", which was Google's figure last year. But I remember when I first came here, Google only spent 3 million a year. Then the salesman said, Simon, that's impossible. Then I said, your data analysis result should be this number, and Google will only spend more, not less.
Next, the second question: Who is the decision maker? At that time, we found the decision maker by analyzing Google's internal social network. Here, many people think that he should be VP or HR to buy this product, but we find this idea more reliable, but not the most reliable.
We finally found out that the people who really want to buy LinkedIn services are actually front-line product managers, people who use LinkedIn for curiosity, and people who really want to buy LinkedIn services. But the boss above them is contracted, so we are middle managers like Target, and he also uses people like Linkedin. This conversion rate tripled in an instant, just after this was discovered.
Next question: how to contact? By analyzing the relationship between our internal sales staff and this relative decision maker, we can find out who has the highest social influence on LinkedIn or has the closest social relationship with him. Then let's send this salesperson to contact him.
The fourth step is to analyze the relationship between all internal salespeople and the company, find the strongest salesperson, or find someone who can support him and help him build relationships in the team. Think about it, for example, knowing you is not my relationship, but my team. Help me introduce this "wall" relationship to know you. In this way, this social relationship will be upgraded again, further improving the conversion rate.
In other words, we shortened all these steps from about four to eight hours to 30 seconds to one minute today.
In the past, it took two months to find and prepare this information. But three years ago, it became a "button" on LinkedIn. As long as the salesperson clicks this "button", it can automatically answer these questions, and then when these questions are answered, the whole story comes out. What's the story? The story is the most important point. The story is: Why do Google or GE buy Linkedin services? Why?
The story is very simple, and it goes back to the question in my data just now, because we know its personnel flow, we know its company growth, we know we know we know, we know much more information than their own HR, and we also know its advantages and disadvantages in the labor market.
In this way, it is a completely relatively real data-driven "story", for example, it is not a fabricated story, but a story based on facts.
At that time, I remember a salesperson told me that he could close one customer a quarter, for example, after he joined this, he could close three customers a week. This is probably the middle of 20 1 1, which was a great victory for us at that time.
So today this "button" has disappeared, and we all push this information to our internal sales staff through mobile phones. Because everyone is running outside, no one has time to press this button. Now, let's push the right information to the right people at the right time and place.
Then why can you use information to push? Suppose that the HR senior director of a company leaves, we will immediately drive two messages internally: the first is to inform the account manager, for example, internally, you see, your high-level relationship may have left, and our competitors may have come in; The second message: This person left his job and joined a new company. We immediately sent this message to the sales manager who is in charge of the account. For example, a very big candidate has been transferred to your side. Do you need to fight for it after he settles down?
These are all cases of data-driven sales. Today, more than 3,500 people inside LinkedIn are using this system. Now the company has 6000 people and about 3000 salespeople. In other words, there are people outside the sales staff who are using it. If it is useless, no one will use it, so this thing is a valuable system.
Moreover, we can also iterate out new product lines from internal big data analysis. You know the three business models of LinkedIn: talent solution, marketing solution and paid subscription, which are our traditional three income pillars. But in fact, the fourth business model is called "sales solution", which was launched at the end of July this year.