how do start-ups get customers?

how do entrepreneurs get customers efficiently, how do they keep their topics, what users like and hate, and how can they keep them? Fortunately, with the development of science and technology, this door looks like? Only by guessing? The work can already be solved through science. The following is what I shared with J.L. How do start-ups get customers? Welcome to visit www.oh1.com/chuangye for more hot entrepreneurial projects.

Why do many companies' data analysis become a mere formality?

The main differences in data analysis are shown in three levels:

1. Cognition of value

Many companies are in a period of crazy growth, and the decisions made by everyone may have produced a lot of value. In this case, it is difficult for them to realize that data decision-making can produce greater value than violent growth.

2. Cognition of basic methodology

It means a core but simple methodology. At present, there is not much understanding of basic methodology in China, which may be because the domestic development time is still relatively short, while the United States has been developing it for decades.

3. Cognition of practical operation methods

Domestic front-line employees use data to guide their work and operation, such as products, customers, sales and other practical operation experience is relatively less. On the one hand, because of the short development time, on the other hand, the concept of data use has accumulated relatively little. In addition, the gap between technology and business is huge. Engineers are forced to build data systems, but he doesn't really understand the business side; The business end is not very familiar with the technology, which leads to many requirements that cannot be directly realized by existing technical means. The lack of mutual understanding further aggravates the slow use of data. When many companies start from scratch, a lot of time is spent in the process of building a technology platform.

firstly, the technical platform is very complicated, which requires various engineers; Second, many companies are groping from scratch, but the data analysis system needs a series of processes and talents, each of which cannot be too weak to really string together.

Many domestic entrepreneurs didn't realize the value of data at first. When he realizes the value of data, his expectations are often high. This big gap can't make the value really fall, or even make people produce it? Can this value really be realized? Question, lack of patience.

what kind of companies need to pay attention to data?

generally speaking, at present, domestic companies pay more attention to data, such as internet finance, e-commerce, trading platforms, SaaS and online travel companies. This kind of customers have a high unit price, and they don't completely fight for traffic, so entrepreneurs have the motivation to improve their transformation.

macroscopically speaking, entrepreneurs will go through 4-5 product and enterprise life cycles.

the first stage is called cold start. At this time, the company was particularly early, angel round or A round, and even the financing was not successful. Is it a false proposition for companies at this stage to be driven by big data? Because the number of customers is limited, the sample is insufficient. They need to know more about the needs of potential customers, go? Beg? Customers come to use this product.

the second stage? In the early stage of growth, the cold start is nearing completion. Experienced entrepreneurs will begin to lay out some core indicators related to growth, such as daily/monthly activity and retention. The purpose of these indicators is not to measure the current performance of products, but to have a comparable benchmark for future growth.

Moreover, these indicators can tell us when we should do growth. If the product itself has no viscosity, it will not really grow if it burns money to grow. Because the loss rate exceeds the growth rate. In the past, many companies that burned money were successful because the competition was not so fierce and users did not have so many choices. But today, if your products are poor, your retention is not high, and your reputation is not good, no matter how much money you burn, you can't get real core natural growth.

the third stage is the growth period. Can you see the great difference between a good startup and an ordinary startup at this stage? Efficiency. Both PR and activities need manpower and time costs. How to find the most efficient channel in the growth? I think this is the core competitiveness of PK between startups.

the fourth stage is the liquidation period. The realization of business requires a high user base. General Internet products, a small number of highly active and experienced users, will be converted into paying users. Similar to a funnel, it is constantly sifting, which is to fight for the efficiency of operation.

for example, the conversion funnel of e-commerce users is generally: access? Register? Search? Browse? Join the shopping cart? Payment, or return to the future.

This is a very, very long funnel. To do a good job in digital operation, we should keep track of every link of the funnel. Why? Because it can't be measured, it is difficult to do growth.

a good enterprise, especially one that wants to make revenue in the future, must pay attention to the conversion efficiency of all departments and links. This conversion efficiency can be achieved by means of marketing, product improvement and even customer operation. And each link is slightly improved, and together it is a multiple improvement. This kind of multiplication, if you have not done digital operation, it is difficult to understand how big it will be.

every industry has its own KPI. For example, in SaaS industry, it is a simple question whether user registration can be successful, but many enterprises may ignore it; After the user registered successfully, did you locate your core product function point, and did this user use your core function? What core product features can make users stay? What functions can't? These should be recorded in the product analysis, but if there is no data, how to analyze it? How to measure it?

Many American companies have summarized these things and have been using them for more than ten years. These experiences can be imitated and learned by many domestic enterprises, and there is no need to go blindfolded again, which is a waste of time and resources.

one more thing, enterprises should be operational. What concept? That is to say, data analysis is not a sport, but a daily affair? Every day, every week, every month, every quarter, we are watching these things. It is a very important process to constantly optimize, learn and promote. But habit cultivation is quite painful, because many entrepreneurs are very busy, so there is no time to see those things.

what should a good data analysis look like?

Good data analysis can benefit everyone in the company. It is not a privilege, not only for one or two people in the company, but for all operating departments in the company, especially those fighting at the front line, to directly benefit. General only talks about strategy, only talks about the general direction, only shows it to CEO, only shows it to VP or operation? This is not enough. It needs to be given to employees who work in the front line and let them use it. I think this is a big difference between a data-driven enterprise and a non-data-driven enterprise. Efficiency is improved by everyone, not by one or two people.

Frequently Asked Questions for Startups

Especially early-stage companies, their concerns are very standardized. For example, they want to know about new users, retained users, strong channels, and the functions of products used by new users.

retention is the core and most urgent problem for a startup to succeed. With the retention rate, there is basically a growth rate. The core users who were brought in in the early days generally have a high retention rate; The relative viscosity of users brought in later period is relatively low. The relatively successful Internet products generally pay attention to the core users in the early stage, meet the needs of the core users, and then spread downwards through this. Therefore, retention should be paid more attention.

at the same time, the retained users need to be decomposed. The remaining users, some new users and some old users, seem to be measured by the same time, but in fact they are different. Many startups sometimes don't separate it: for example, among the retained users, how many are new users and how many are old users; What is the retention rate of old users and new users?

after dismantling, it can be operated for each different type of' users. For example, it can analyze which functions users use more than five days a week.

in the early stage of the product, we should keep the product well before adding it, so that the founder's energy will be more focused. Because if you do both innovation and retention at the same time, you will be divided into two places, and you will not be able to take care of it. With high retention, it will also be helpful to Laxin, find the access channels for high retention users, and then continue to replicate operations.

second, after you have a good retention, you can expand quickly. Because after the expansion, users will stay and your growth rate will accelerate.

I think this basic thinking method is needed after the cold start of the product. In the growth period, you need extreme concentration.

early depends on intuition, and later depends on science.

The earlier you do some data preparation, the better it will be for a company. It is a process of continuous iteration and accumulation. However, don't put the cart before the horse, and don't do AB test as soon as you can start, which is unnecessary, because you haven't accumulated enough users, and the data thus analyzed is not representative.

finally, let me briefly summarize the five stages of data analysis:

? The first stage, there is nothing;

　? In the second stage, the company needs to go back to history: knowing what is happening to its products is the most basic and original stage;

　? In the third stage, people who do products, operations and marketing internally need to ask why: this stage is to predict, that is, to predict what a certain group of people will do next, so that they can develop products in a targeted and better way;

　? The fourth stage is to have a solution: that is, I predict that this group of people will do this, so I will give it a better solution, so that it can be better transformed and retained, bringing better new effects;

　? The fifth stage is optimization. How can diverse product lines find the best balance point? There is a balance point in terms of price, marketing, product design and sales. This balance point is the point of maximizing the interests of entrepreneurs and the point that users like this product best.

It takes time to accumulate these five stages. Don't jump. Jumping often fails. Start from the basics. ;