How should maternal and infant e-commerce platforms attract new customers?

As a data analyst, it is very important and valuable to calculate the accounts for new business and achieve data-driven growth. The author of this article uses maternal and infant e-commerce as an example to explain how to do a good job of attracting new business from the perspective of a data analyst. It is recommended for you who are concerned about data analysis and are responsible for attracting new business.

1. Introduction

For Internet businesses, attracting new customers has always been a top priority. Only by getting more people to use the product can we achieve economies of scale and gain more profits. Most Internet companies will set up user operation positions, and special people will be responsible for the company's new customer acquisition business. So what can a data analyst contribute to the company's new customer acquisition business?

2. The core of attracting new customers

As the Internet enters the second half, it becomes increasingly difficult to attract new users. Users are basically monopolized by giants, and the cost of users switching platforms becomes higher. Therefore, a core proposition of attracting new users is to "calculate the accounts clearly." Many people still stick to the concept of the early days of the Internet, that is, we attract as many new users as possible, without caring about how much money these users cost or how much value they can generate.

Therefore, the most important understanding of new user acquisition at this stage is what kind of users should be acquired at what cost, which is also the most valuable thing that data analysts can do to help the company's new user acquisition business. It is generally difficult for operations staff to calculate how much value the new users have generated for the company. At this time, data analysts are needed to intervene to help the new user acquisition business calculate the accounts clearly, truly achieving data-driven growth.

3. User Value Assessment

For most companies, they know how much money they spent to attract these new users, because these data can be produced with a simple data infrastructure.

Most of the advertising for attracting new users by Internet companies is done on Internet giants, so all the expenses can be clearly presented in a visual way. In addition, methods such as ground promotion are generally tracked and recorded, so most Internet companies can clearly calculate how much money they spent and how many new users they attracted.

But how much value do these new users generate, and is it worth spending so much money to bring them in? This is a confusing account for many companies.

Because in order to calculate these accounts clearly, a relatively complete data infrastructure is needed. It is necessary to mark the channel source of each new user and record the data of the company's commercial business. The commercial business model mainly consists of two categories: advertising to help advertisers sell goods and selling goods by oneself. That is, it is necessary to record the data performance of each user in all commercial businesses, and then associate the data of the two, and finally analyze it clearly through data analysts. This requires a relatively complete data infrastructure. Generally, only medium and large companies can achieve such a level of data infrastructure construction.

With these basic data, data analysts can start to show their skills. We can calculate the ROI of each channel. For example, the advertising of Douyin channel may be 10 times that of Baidu, so we should increase the advertising of Douyin; even some channels have negative ROI, where it costs 10 yuan to attract each user, but the value generated is only 8 yuan, so we should stop advertising on this channel.

In addition, as a data analyst, you can also do more in-depth analysis to see what value different types of users generate. This requires the company to build a user portrait system so that we can classify users according to their user portrait labels and determine which type of users are more valuable to the company. When conducting reverse push operations to attract new users, you should prefer users who can generate high value for the company. In the long run, the ROI of each channel can be achieved.

Of course, the calculation process will be more complicated. Data analysts need to constantly promote this concept and its logic to operations, so that operations can deeply understand its meaning. Only then can every company operator have this ROI accounting thinking in mind, and the company's new business can be better and better. Then the core value of data analysts is to help operations calculate how much money it is worth to attract each type of user. This is using data to help the business make decisions and play the real role of data.

4. Practice of attracting new customers in maternal and infant e-commerce

Take maternal and infant e-commerce as an example. From the perspective of a data analyst, how to attract new business? Our company's business is quite special. We can have information about the age of the user's baby. The age of the baby is a key information about how much the user is willing to spend. The older the baby is, the less concerned the parents will be about it, and the consumption for it will also decrease. Therefore, the age of the baby is a key dimension for users to judge the value of the business.

Therefore, we can divide the user value based on the baby's age. From the historical new user data, we can find that the value of pregnant users: 0-1 years old users, 1-2 years old users, 2-3 years old users, and users over 3 years old have relatively large differences. Basically, the younger the user, the higher the commercial value they generate. For this reason, we should invest different costs in different users and not treat them all equally.

In addition, the new customer acquisition goal of e-commerce cannot be simply the number of new customers, because this is often an indicator of false prosperity. It is very likely that the company will lose more money if it attracts more users. We can consider maximizing the user value in the next 12 months as the new customer acquisition goal, so as to guide the business in the right direction of attracting new customers.

But there is a problem here. It takes 12 months to evaluate the effectiveness of a strategy. As a business manager, this time period is definitely unbearable. Therefore, as a data analyst, you have to find a way to shorten this time period.

For e-commerce businesses, the value generated by users is the gross profit generated by their purchase orders, so how can we predict the gross profit value generated by attracting new users in the next 12 months? In this case, we can generally predict the behavior of users in a longer period of time based on their behavior in a short period of time.

Then we can predict the gross profit value within one year based on the users' repurchase gross profit generated within 30 days, 60 days, 90 days, and 180 days.

It should be noted here that the gross profit data of the first day of attracting new users must be excluded. The gross profit data on the first day cannot reflect the value of the user. This is affected by the strategy. Regardless of the quality of the user, the gross profit is a fixed value of the strategy's concession level. Therefore, excluding the first day's repurchase data in the future can reflect the quality of the user.

Then we can make predictions based on linear regression. After analysis, we found that the repurchase gross profit generated within 30 days can predict the gross profit in the next year with an accuracy of 70%, and the repurchase gross profit generated within 60 days can predict the gross profit in the next year with an accuracy of 85%. There will be no significant difference after that.

Based on this analysis result, we can evaluate its gross profit value for the next year earlier, and then calculate its corresponding ROI of attracting new customers based on the cost of each strategy. In the end, we can evaluate the ROI of different strategies on the same level, keep the strategy with higher ROI, and iterate the strategy with negative ROI. Finally, we can maximize the profit of attracting new customers.

After we can evaluate the value of each strategy, we need to stratify users and invest in strategies of different costs for users of different values. Generally, we first implement a lower-priced strategy for new users without losing money on the overall gross profit. Then, we implement a higher-cost strategy for users who cannot be converted by this strategy. In this case, the gross profit of the strategy is basically negative, so we must be cautious in implementing it. We must choose users with higher user value to implement this higher-cost strategy. Only in this way can we recover our investment and generate positive value. If this strategy is implemented for all users, the overall ROI is likely to be negative.

By following this logic, maternal and infant e-commerce operations to attract new customers can basically create a positive cycle. Attracting more users can help the company generate more value. After the company makes more money, it can have more money to invest in the cost of attracting new customers, ultimately maximizing the company's profits.

Based on the above methodology for attracting new customers, we can sort out a more appropriate strategy framework for attracting new customers. Of course, data analysts can only do this. The details of each strategy need to be polished by operations, and some creative ideas and strategies need to be brainstormed.

In addition, data analysts need to help new customer acquisition operations establish a complete data indicator evaluation system, help operations verify the effectiveness of each strategy and evaluate the funnel conversion effect of each link step, and continuously optimize each copy picture and other user experience details, ultimately achieving vigorous development of the overall new customer acquisition business. Our data analysts have also contributed indelible value in this.

Data-based operation architecture for attracting new customers in maternal and infant e-commerce

(Photo source: Zhihu @王向君)

Author: A Kun