How to optimize and adjust the data of e commerce operations?

To do a good job in the data optimization of e-commerce operations, the internal data dimension cannot be ignored, and the following directions should be done:

1. Visitor data: The operator should pay attention to and record the data focus every day. Understand the visitor's origin, regional distribution, access time, whether the visit is anonymous, and non-anonymous visitors should also actively record the visitor's membership information.

Sufficient visitor data will provide a reliable basis for subsequent promotion, function optimization, process improvement, and customer service tracking for different user groups.

2. Visiting pages: A good record of visiting pages requires excellent record identification and burial, and the operation not only needs to understand which pages the user visits, but also should understand which pages the user stays on for a long time, which pages stay for a short time, through what way to access, and the attributes of the visited pages. With this data, it can provide assistance for accurate content optimization and selling point refinement.

4. Order result: The efficiency of order processing will greatly affect the customer's purchasing experience. Operators should actively pay attention to abnormal orders, find out the causes of exceptions, and take the initiative to compensate or explain to customers.

5. Complaint feedback: We must attach great importance to the data generated by the user's active interaction. Record and reply at the first time, so that users have a sense of being valued.

After that, it is necessary to improve the content, optimize the process, and compensate and comfort the user as appropriate.

E-commerce operation optimization.

E-commerce operation, the rise in data shows that it is still relatively effective in operation. Therefore, for this kind of operation, it is necessary to increase the operational skills and data analysis capabilities on this basis.

The data analysis capabilities are as follows:

Including main image data, detail data, product data, customer data, market data, promotion data, SEO data, and so on.

For operations, it is necessary not only to clarify the exact meaning of these data, but also to clarify the application significance of these data. Because e-commerce operation includes store operation, any part of the data will reflect some problems.

What operations need to do is aggregate the observational data and draw conclusions based on the data to support the next optimization solution. For example, if you don't analyze the reasons for the rise and fall of traffic, it is very likely that you will make similar mistakes again, and you will not be able to improve the ability of operation and the skills and methods of e-commerce operation.

Also the data is best to observe for a period of time in a steady operation.

One. E-commerce data analysis architecture.

First of all, it needs to be admitted that the premise of the data analysis architecture model is that it needs to have a sufficient understanding of the daily work scenarios and needs of the business, and be able to put forward data analysis methods with suggestions to release the timeliness of business personnel in the data analysis process.

Two. Online store management analytics.

For users in a store, a complete purchase process: see the advertisement, enter the store, browse the product, consult the purchase, place an order and pay. How should store operators analyze and manage the traffic of users in each link?

In view of this, the following will be analyzed in detail from four aspects: traffic analysis, sales analysis, commodity analysis, cluster rent, and activity analysis.

Three. Offline store management analysis.

For e-commerce companies, in the past, they were mainly online stores, but with the expansion of their business, these companies are now expanding offline stores to make up for the lack of online user experience and integrate online and offline, so as to expand the scale of users. To this end, Changsui Yonghong consulting experts have designed an offline store management analysis system, which helps e-commerce companies choose the most suitable stores and achieve efficient management of stores through offline store expansion analysis and store location analysis.

What is Data Analytics Thinking?

Data analysis thinking, I think, is: turning behavior into data - acting backwards through data.

I'll give you an example:

You often come to my shop to buy auntie towels.

If you come over today to buy an aunt's towel, I know you're going to come to your aunt in about a week. Based on the quantity and specifications you buy, I can infer how long your aunt has been here and how much. Pull out your purchase time for half a year, and I can infer how often you have a great aunt is not stable.

If you haven't seen you buy an aunt's towel for two months... That must have been two months ago, when your boyfriend's raincoat broke.

Pull out your boyfriend's purchase history, and I know that the raincoat in this store may not be up to standard.

In order to verify whether he is unqualified, let's go and see if his repurchase rate within half a year is much lower than that of his peers.

Well, Hail Woo Mo just because you didn't buy an aunt towel, I suspect that the raincoat in this store is not up to standard.

This is the basic thinking of data analysis.

Learning the basic thinking of data analysis, it can only be said that you barely have the possibility of data analysis.

Then do the data analysis. There are a few things that need to be understood.

1. Data sample: If the data sample is not selected reasonably, then the result is completely wrong. For example, if I go to grab an aunt towel store that is located as a 40-year-old aunt, and ask for an aunt cycle for Chinese women, it is not scientific at all.

This is the difference between adolescence and menopause (this example shows that Lin Mubai also has a knowledge of **, and welcomes the majority of unmarried women of appropriate age to write to their friends for consultation).

An example that is often made in actual combat is: a single product with a good conversion rate of flat sales does not sell well in Juhuasuan. Some items with a poor conversion rate will be sold out instead?

Why? Think about it, don't ask me, think for yourself. If you don't understand, don't try to do e-commerce data analysis.

2. Data selection: In fact, we will encounter a lot of data, but some data is not necessarily what we want. It's like we will meet a lot of good girls in our lives, but it's hard for us to understand who can better accompany us through this life.

I can't give an example of this matter, so I'll give you a test question here:

Now our store needs to make coupons**, the purpose is to increase the unit value.

Okay, you tell me to do 100 yuan off 100.

Well, very good, then you tell me now, why is it full 100 instead of full 110, why is it 10 yuan minus instead of 20. Take out your data.

Well, don't ask me how to get it. And don't wonder if I can really analyze it, I really can.

3. Dynamic change: The most commonly used thing is to analyze what problems or changes may occur through changes between data. However, when the amount of data changes, the other data will change as well.

So we need to be clear about what data is positively correlated with each other, what is negative correlation, how they relate to each other, and under what circumstances is true. For example, the proportion of normal favorites is positively correlated with conversion rate, but these days they are inversely correlated. The lower the conversion rate, the higher the collection rate is likely to be.

If you want to run an e-commerce business well, it is important to know how to analyze, which data indicators are more critical?

The e-commerce economic indicator system has four dimensions and 12 indexes, first of all, the data is collected and summarized, and there is a special statistical platform - industrial economic monitoring, ** and policy simulation platform.

The specific copy definition of the second type of e-commerce refers to mobile information such as Bai Toutiao, Guangdiantong, and Kuaishou today

On the information flow platform, ZHI, merchants who rely on high-quality advertising traffic to do single product sales DAO, and the transaction forms are mainly free shipping and cash on delivery.

The key to the second type of e-commerce is product selection, delivery, and logistics. Therefore, for individuals or small businesses, it is necessary to understand the heart of Jiawei, 894 in the middle, 984 in the middle, and 622 in the middle.

First: Clicks

In these two figures, the number of keywords and the delivery area, the delivery time are the same, but the bids are different, Figure 1 has a low bid, resulting in a small number of clicks, so the number of orders that can be brought is also small, and Figure 2 raises the bid, the product ranks high, the number of clicks has increased a lot, and the number of orders has made a significant breakthrough. At this time, as long as our input-output ratio is in line with expectations, we should release traffic in exchange for more orders, paid promotion can be profitable, and the transactions brought by organic search traffic will earn more.

Second: click-through rate

As the first rate of e-commerce, click-through rate is an important indicator to measure whether the product is popular with users.

If you don't know how to optimize click-through rates, you may wish to start from several aspects such as creativity, copywriting, accurate keywords, and accurate people. Today, because it is not the optimization problem of sharing click-through rate, I will not repeat it, in short, there is no highest click-through rate, only higher.

Third: Average click cost (PPC).

This should be a headache for many drivers or operators, if you want to reduce the average click cost, we can start from the deduction formula, and search for the deduction formula: actual deduction = (next bidder, next quality score) their own quality score + yuan. From this formula, it is not difficult to see that there are only two ways to reduce the deduction, either lower the bid or increase the quality score.

Lowering bids will inevitably lead to keyword rankings, making it more difficult to get ** and clicks, and once the number of clicks is insufficient, it may overturn. Therefore, you can only improve the quality score of keywords, and the optimization of quality score can start from several aspects such as attribute relevance, category relevance, click-through rate, click feedback (conversion rate) and so on.

Fourth: Click conversion rate

If you want to get a relatively high ROI, that is, the input-output ratio, you should find a way to retain every traffic as much as possible. The optimization direction can be improved from the completeness of the detail page, preferential activity policies, buyer evaluation, after-sales protection, customer service inquiry conversion, etc., which improves the click conversion rate, can also reduce the promotion cost, and maximize the traffic value.

Regarding what core data e-commerce needs to optimize, Qingteng will share it with you here today. If you have a keen interest in internet marketing, hopefully this article can help you. If you want to know more about advertising and marketing copywriting, copywriting optimization methods and materials, you can click on other articles on this site to learn.