Many people start the analysis based on data such as PV/UV/number of users, but after the analysis, what measures should be taken? What kind of goal drives it?

I have also done such analysis. Most of these routine data are just tied to a person's KPI, reflecting the person's work performance. It is not a complete data analysis.

Here I would like to summarize the operating system of Internet companies and lean data analysis from a more comprehensive perspective.

The purpose of data analysis should be for the development of the company, or to put it bluntly: for the company's profitability and sustainable profitability.

The Internet has different profit models and data indicators, which can be roughly divided into three types:

• The first is to sell goods or services to users, represented by e-commerce, social networking and O2O platforms;
• The second is to make profits through advertising, such as Google, Baidu and other platform-based Internet companies;
• The third is to charge users directly, major game companies.

Taking the Internet e-commerce company with the most complex analysis system as an example, let’s break it down one by one and see which data needs to be analyzed? How to analyze? What is the value of analysis?

The revenue of e-commerce companies is generated by orders piled up one by one, when users purchase related products or services.

It can be said that users and goods or services are the two basic elements of an order. The company's revenue decline, growth, and anomalies can ultimately be traced back to these two elements: users and goods.

In this way, we break down revenue-related data into three categories: users, products, and orders.

1. Operation Module

From the perspective of the user's consumption process, it can be divided into traffic diversion-conversion-consumption-retention.

We generally divide users into new and old users. Regardless of new or old users, we will focus on two areas: one is traffic generation (attracting new users), and the other is conversion; ultimately it is reflected in the form of data, namely traffic and conversion rate.

1. Drainage

The quality of traffic is measured by analyzing data such as PV, UV, number of visits, average visit depth, and bounce rate.

The purpose is to ensure the stability of flow and try to increase flow through adjustments.

All charts are generated by data reports built by FineReport

Furthermore, according to the traffic structure, it can also be divided into channel structure, business structure and regional structure.

In the channel, traffic can come from independent visits, search engines, Taobao payment, JD payment, etc.

According to the device, it can be divided into PC channel and APP channel, and according to whether it is paid or not, it can be divided into free traffic and paid traffic.

Some people will analyze the quality of each channel based on the proportion of channel traffic.

The line chart below can track the traffic situation of each channel, analyze whether the unreasonable proportion is short-term or long-term, and assist in the analysis of the problem. Measuring quality based solely on traffic is incomplete and needs to be combined with conversion rate and ROI.

This is easy to understand when divided by region.

According to the business structure, the most typical example is when holding an event, such as Double Eleven, you must track the traffic of the event, observe the changes before, during and after the event, and evaluate the effectiveness of the event.

2. Conversion

After completing the traffic generation work, the next step is to consider conversion, which requires browsing the page - registering as a user - logging in - adding to the shopping cart - placing an order - paying - completing the transaction.

There will be user loss in every link, and improving the conversion rate of each link is the core of this work - the increase in conversion rate means lower costs and higher profits.

Analysis of conversion:

1. Observe the conversion rate of each link, analyze its rationality, and make adjustments to links with abnormal conversion rates;
2. Track changes in conversion rates to locate anomalies and verify the effectiveness of strategy adjustments;
3. Observe the conversion of each channel, define the channel value, and adjust the operation strategy accordingly;
4. Analyze the conversion cycle of each link, analyze user habits, and provide a basis for formulating operation strategies.

The most direct analysis result is the conversion funnel.

3. Retention

Users are attracted through various channels or activities, but after a period of time some users will leave. Of course, some users will stay, and these users are called retained users.

Regarding retention, what we need to focus on here is daily activity and retention rate.

When it comes to retention, it’s all about:

1. Daily activity monitoring, observing user activity data and analyzing the health of daily activity;
2. Observe retention patterns, identify retention stages, and assist in marketing activities and market strategy positioning;
3. Compare the retention of different users and product features, analyze product value, and assist in product adjustments.

4. Repeat purchase

Survey data shows that a satisfied user will bring in 8 potential businesses, and an unsatisfied user may affect the purchasing intention of 25 people. This shows how important repeat customers are.

The repurchase rate can be divided into "user repurchase rate" and "order repurchase rate". In addition, the "user repurchase rate" has a similar meaning to the repurchase rate and is also within this range.

• User repurchase rate = number of users who purchased twice or more per unit time / total number of users who purchased;
• Order repurchase rate = number of orders purchased for the second time or above / total number of orders within a unit of time;
• User repurchase rate = number of old users who have purchased in unit time / total number of users who have purchased.

The purpose of analyzing repurchase rate:

1. Comprehensive indicator display, analysis of user stickiness, assistance in identifying repurchase rate issues, and formulation of operational strategies.
2. Compare and analyze horizontal dimensions (products, users, channels) to refine repurchase rates and assist in problem location.

5. Churn

Loss is inevitable, but it can be retained.

Loss can be divided into:

• Rigid churn: can be further divided into new users’ acclimatization type and old users’ interest shift type. This part of lost users cannot be retained, and the relationship ends here. No matter how much money you spend, it will be useless.
• Experience loss: It may be application experience, service experience, transaction experience, product experience, etc. In short, it is when you feel a little unhappy during the use of product services. As the saying goes, if you don’t agree, you will lose experience.
• Competitive loss: that is, the user has become a fan. It may be that the competitor’s experience is better, or that the competitor has launched some preferential policies. We also need to grasp the dynamics of the industry and take corresponding actions against our competitors' fan-grabbing behaviors.

Different companies have different definitions of churn, which may mean no login behavior within 7 days or no transaction behavior within several months.

Return rate = number of people who return after leaving within a certain period of time / number of people who leave within a certain period of time

Routine data monitoring of loss is usually carried out together with retention, and the two are inseparable.

For churn alone, the most common monitoring you see is as follows:

Furthermore, the churn rate combined with the retention rate can also evaluate the value of the channel.

2. Sales Module

Indicator tracking: There are a large number of indicators in the sales module, including year-on-year growth, completion rate, sales ranking, proportion of key commodities, proportion of platforms, etc., which can be analyzed and tracked from the three perspectives of people, goods, and places.

Store analysis: For small B-level users or those who have settled in the platform, it is necessary to analyze the operating indicators of each store, including each store's efficiency indicators, completion rate indicators, performance indicators, average customer spending, etc., to achieve store value assessment and analysis.

Sales activity management: Activities are a very important part of online sales, and closed-loop analysis of sales activities is achieved from three levels: before, during, and after the sales activity.

These include:

• Prior investment analysis and target forecasting;
• User engagement, customer flow analysis, and sales order analysis during the event;
• Post-goal completion status, activity comparison, cost-sales ratio, activity attenuation, activity explosiveness, etc.

3. Product Module

• Procurement management: including supplier data analysis, procurement matching analysis, etc.
• Supply chain management: supply chain service situation analysis (response cycle, delivery timeliness rate, order execution rate), management indicator analysis (material cost ratio, customer complaint rate, etc.).
• Inventory management: analysis of data such as commodity inventory days, inventory-to-sales ratio, effective inventory ratio, and inventory turnover rate.
• Analysis of important indicators: Analysis includes indicators such as age of goods, turnover rate, out-of-stock rate, structural indicators, price system, correlation analysis, and hot and cold sales to judge the value of goods and assist in adjusting product strategies.
• Analysis of abnormal products: including analysis of return rate, damage rate, abnormal products and other data, and timely handling of abnormal products.

4. User Module

• Analysis of key indicators: including the number of new users, growth rate, churn rate, percentage of effective members, retention rate, etc.
• User value analysis: Based on the RMF model and incorporating other personalized parameters, users are divided into different value categories and further analyzed for users at each level.
• User portrait: Add labels and weights to users based on inherent attributes, behavioral attributes, transaction attributes, interests and hobbies, design user portraits, and provide reference for precise marketing.

Author: Li Qifang

Source: Data analysis is not a big deal