Startups need to clarify these 6 issues before they can carry out data-driven operations!

The era of "traffic is king" is over, and Internet companies are transforming towards lean operations . Implementing lean operations requires a large amount of data to support decision-making, which poses a huge challenge to the company's data collection and data analysis capabilities.

There is a large gap between China and the United States in data analysis. Data analysis is only given high attention in some particularly large domestic companies, such as BAT; of course, this is due to their long-term accumulation and better integration of data and operations. This is my overall feeling after returning to China. Domestic companies' understanding of data itself and the value that data can provide is not as deep as that of the United States, and the difference is quite large.

Question 1: What kind of companies need to pay attention to data? What are the differences between the different stages?

Generally speaking, the companies that pay more attention to data in China are those with high average order value and focus on conversion, such as Internet finance, e-commerce , trading platforms, SaaS, and online travel companies. This type of customer has a high average order value and does not rely entirely on traffic, so entrepreneurs will have the motivation to increase conversions.

Generally speaking, entrepreneurs go through four stages of a product’s life cycle.

• The first stage is called cold start. At this time, the company is in a very early stage, and has not even successfully completed the angel round or A round of financing. For companies at this stage, using big data as a driver is a false proposition because the number of customers is limited and the sample size is insufficient. They need to understand more about the needs of potential customers and "beg" customers to use this product.
• The second stage is the early stage of growth. The cold start is almost complete. Experienced entrepreneurs will start to plan some core indicators related to growth, such as daily/monthly activity and retention. The purpose of these metrics is not to measure the current performance of the product, but to provide a comparable benchmark for future growth.
• The third stage is the growth period. At this stage, the huge difference between good startups and ordinary startups can be seen - efficiency. Whether it is PR or organizing events, it requires manpower and time costs. How to find the most efficient channel during growth? I think this is the core competitiveness among startups. If you don't do data-driven things and just rely on intuition, it may work once or twice, but no one can go to a casino and win 10,000 times in a row. Therefore, intuition needs to be combined with data so that companies can quickly optimize various channels to improve conversion efficiency per unit time.
• The fourth stage is the monetization period. Business monetization requires a very large user base. For general Internet products, a small number of highly active users with good experiences will be converted into paying users. It is like a funnel that continuously screens, and the efficiency of operations is what matters here. For example, the conversion funnel of e-commerce users is generally: visit - register - search - browse - add to shopping cart - payment, or return in the future. This is a very, very long funnel. To truly implement data-driven operations, we must continuously track every link in the funnel.

A good company, especially one that plans to generate revenue in the future, must pay attention to the conversion efficiency of each department and each link. This conversion efficiency can be achieved through marketing methods, product improvement methods, and even customer operation methods. A small improvement in each link together will result in an exponential improvement. It is difficult for people who have not worked in data-driven operations to understand how huge this kind of multiplication will be.

Question 2: What should good data analysis look like?

Good data analysis can benefit everyone in the company. It is not a privilege, nor is it only for one or two people in the company, but it can directly benefit all operating departments in the company, especially those fighting on the front line.

Normally, we only talk about strategy and general direction, and only show it to the CEO, VP or operations - this is not enough. It needs to be given to frontline employees so that they can use it. I think this is a big difference between a data-driven enterprise and a non-data-driven enterprise. Efficiency improvement means improvement for everyone, not just for one or two people.

If a company wants to build a complete data analysis mechanism, it should start with the business. All data analysis operations or data systems should start with the business and the customers. This data analysis system should not only solve one or two very narrow problems, but also need to have a system and a big picture. Then, in fact, the most difficult part of data analysis is data collection and data organization. This process is the most time-consuming, probably because the initial plan was not thorough enough. Therefore, we should pay great attention to data collection and data organization in a planned manner.

In the end, data analysis cannot just stay on the basis of reporting, the value is still not enough. Ultimately, once those numbers come out, it’s right and effective to tell others what they should do. There is a lot of profound knowledge involved here, and it requires strong operational capabilities.

Therefore, an enterprise must have a broad perspective and also focus on feasibility. I suggest that if general enterprises want to build their own, they should first break through from a single point, find a transformation point, see the value, and then learn the method for the next practice through this practice. This is also a learning process. Don’t start by building a huge system or combining 50 data circles together in an attempt to build a data science framework. I think if you do this generally, unless you have a lot of resources, you will definitely fail.

Question 3: What stages can enterprise data analysis be divided into?

• In the first stage, there is nothing.
• The second stage requires the company to be able to trace back history and know what is happening with its products. This is the most basic and original stage.
• In the third stage, people who work on products, operations, and marketing need to ask why: This stage is about prediction, that is, predicting what a certain group of people will do next, so that they can develop products in a targeted and better manner;
• The fourth stage is to have a solution: I predict that this group of people will do this, so I give them a better solution to achieve better conversion and retention, and bring better new customer acquisition results;
• The fifth stage is optimization, how to find the best balance point for diversified product lines: there is a balance point in price, marketing, product design, and sales. This balance point is the point where the entrepreneur’s interests are maximized and it is also the point where users like this product the most.

These five stages require time and continuous accumulation. Don't skip, as skipping often leads to failure. Start from the basics.

Question 4: Why is data analysis in many companies just a formality?

This is mainly because many companies lack awareness on three levels: the value of data, data analysis methodology and actual operation methods.

1. Value Perception

Many companies are in a period of crazy growth, and the decisions made by everyone on the spur of the moment may have generated a lot of value; in this case, it is difficult for them to realize that data decisions can generate greater value than violent growth.

2. Understanding of basic methodology

Meaning a core but simple methodology. At present, there is not much knowledge about the basic methodology in China, probably because the domestic development time is relatively short, while the United States has been developing it for decades.

3. Understanding of practical operation methods

Domestic front-line employees use data to guide work operations, but have relatively less practical experience in areas such as products, customers, and sales. On the one hand, because of the short development time, on the other hand, the accumulation of data usage concepts is relatively small.

However, domestic companies have been rapidly improving this awareness. However, this cognition is a step-by-step, gradual process. In the United States, cognition and methodology have gradually been well unified - technology and business are integrated with data.

Many domestic entrepreneurs do not realize the value of data at first; when they realize the value of data, their expectations are often very high. This huge gap also makes it impossible for value to be truly realized, and even makes people question whether "this value can really be realized" and lack patience.

Question 5: What common myths do Chinese companies have about data?

I think domestic companies have two extreme understandings of data analysis: one believes that it is pure technology, while the other is more superstitious, believing that as long as they engage in big data, they will become high-end companies. I think there are certain misunderstandings in both approaches.

At its core, I think whether what you have made is valuable and effective? The most direct way to measure is by effect. Some other companies want to build their own platforms and set up large teams, but their efficiency and output are relatively low. I advise everyone to be cautious about this. As the ecosystem continues to develop, many tools are now very useful, and you have to learn how to use them. These are some great aids for entrepreneurs to succeed - you can't say that because you know how to use the tools, you will succeed in starting a business; but good entrepreneurs will definitely be able to use these various tools to achieve their goals.

Question 6: How to break the vicious circle of data not being fully utilized?

In the past few months, we have been dealing with customers and found that some companies use our products very well, while others are just average. Usually, companies that have someone in-house who is responsible for data use it very well; some companies do not have a core person to follow up on this matter, and their performance is average.

Therefore, there must be at least one person in the operations department who has a certain understanding of data analysis. It's like if we move a set of advanced surgical equipment into the company, it will be useless if no one knows how to operate it.

I think the best way to acquire knowledge is through practical operation. The prerequisite for actual operation is to have someone who knows a little bit and can guide you to do it a few times. Then start to apply and learn. This is the fastest and most effective way to acquire data analysis knowledge. I don't think just reading books or textbooks, or looking at some external big data guidance books, can have this effect.

With this person, we can get methodological support from people who understand this area and the company's products, and this learning mechanism will be established. This is quite important. Otherwise, even if the system is powerful, it cannot be fully utilized if no one knows how to operate it.