Preface
First of all, I apologize to everyone. I have been taking part in the written test and interview of the autumn recruitment recently. I have never had time to write a blog. As we all know, blogging is time-consuming. In order to ensure the quality of the article, take the time to prepare well. Some time to write. Please rest assured that no matter how busy you are, at least four articles will be posted a month, which means that at least one article per week is guaranteed. And will not be perfunctory, will ensure the absolute quality of the article. As much as possible to satisfy everyone when reading the article will have a certain gain.
Following the content shared last time , we then introduce the business knowledge in data analysis. As we all know, as a core data analyst, the thinking and business of data analysis are particularly important. Its importance is much more important than using mysql, Excel, python and other tools. As a member of data analysis, we should first have the awareness of data analysis and continue to cultivate our business capabilities. Only with these Basic literacy, we have some python, mysql, Excel and other technologies will be even more icing on the cake. Therefore, let me introduce you to the relevant content of the business.
1. Common indicators in specific scenarios
Those who have done data analysis know that only when we understand the business requirements can we effectively establish the corresponding business data model. When building a data model, indicators are a very important measure. When we analyze the business, we try to establish indicators first, and then build models based on indicators after we have indicators. We use a picture to show some common data analysis processes of e-commerce: After
introducing so many, what are the indicators? An indicator refers to a single dimension element that can be measured by total or ratio. In data analysis thinking , we have introduced data analysis methods, including structuring, formulating, and businessization. In fact, the content of these analyses still needs indicators to be finally implemented. Just like when we analyze a certain event in our daily life, we often use the appearance we see to predict the future development through some quantifiable indicators. The specific analysis process is as follows:
In fact, when performing data analysis, we should analyze through core indicators. A good indicator should be the ratio. Through this indicator, we can explain the corresponding problem intuitively, that is to say, a good indicator should Can bring significant effects. In addition, we need to pay attention to: a good indicator should not be complicated, and it should not be vanity.
Next, I will introduce some common indicator scenarios: mainly including marketing indicators, product operation indicators, user behavior indicators, e-commerce indicators, and traffic indicators. In addition, we will also introduce how we should generate some indicators. First, I will introduce marketing indicators to everyone.
1. Marketing indicators
Generally, when we analyze marketing, the first thing we need to pay attention to is the life cycle of customers/users : this is the cycle of the entire business relationship between enterprises/products and consumers. Of course, the stages of different business divisions are different. In traditional marketing, it is divided into potential users, interested users, new customers, old/acquainted customers, and lost customers. Secondly, we should pay attention to user value : In fact, in a large number of business fields, should we most effectively define users? We introduce the index method here and analyze the indicators that we consider the most important. Common calculations are as follows:
User contribution = output/input *100%
user value = (contribution 1+contribution 2+……)
Just as we often dabble in the financial industry, we use the formula of deposit + loan + credit card + annual fee...-risk-churn to predict the value of users. In addition, I will introduce the RFM model to everyone . In the user life cycle, we use the cube model to measure customer value. Using R's last consumption time, M total consumption amount, and F consumption frequency, users are divided into multiple groups. Next, I will introduce user grouping and marketing camps . User grouping is a common strategy in marketing. It extracts several core dimensions of users. We will commonly use the quadrant method to generalize and classify them.
The specific expression is as follows:
2. Product operation indicators
I have introduced some commonly used indicators in marketing, including customer/user life cycle, user value, RFM model, user grouping, and marketing indicators. Next, I will introduce AARRR , including Acquisition user acquisition and Activation user Active, Retention user retention, Revenue, Refer spread. These indicators are as follows from the bottom up:
- User acquisition
channel reach : commonly known as exposure. That is, how many people have seen the
conversion rate of the lead channel related to product promotion : how many users are interested in Cost Per because of exposure, including CPM, CPC, CPS, CPD, CPT, etc.
Channel ROI : familiar KPI for promotion and marketing, return on investment, profit/investment*100%.
Daily application downloads : the number of app downloads, here refers to click to download, it does not mean that the download is complete.
Number of new users per day : based on user registration and submission of information.
Customer acquisition cost : the cost of acquiring a user
. Percentage of users in one session : refers to a new user who has downloaded the App, opened the product only once, and the duration of use is within 2 minutes.- User active
daily/weekly/monthly active user application downloads : The active standard is that the user has used the product. In a broad sense, web browsing content is counted as "use", and orders placed on the official account are counted as "use", not limited to opening the APP.
Proportion of active users : The proportion of active users in the total number of users, which measures the health of the product. The
number of sessions of the user : the entire cycle from when users open and use the product until they exit the product. If there is no operation within 5 minutes, the default session operation ends.
User visit time : the duration of a session.
Average number of visits by users: The average number of sessions generated by users in a period of time.- Users keep
users who use the product for a certain period of time and continue to use it after a period of time. Let’s use a small case to illustrate: assuming that the product has 1,000 new users one day and 350 users who are still active the next day, then the next day’s retention rate is said to be 35%, and if the seventh day there are 100 users who are still active One, then the seven-day retention rate is 10%.- Revenue
paid users : the
proportion of paid users who spent money: the ratio of daily paying users to the number of active users, and the ratio of total paying users to the total number of users can also be calculated.
ARPU : Average revenue per user in a certain period of time
ARPPU : Average income of each paying user in a certain period of time, excluding unpaid
customer unit price : the average amount of goods each user sells. Total sales/total customers
LTV : User lifetime value, which is close to the customer value of marketing, and is often used in the operation of game operators and e-commerce. In addition: LTV = ARPU * 1/churn rate
propagation
K factor : each user can bring in several new users; namely: K factor = number of users X average number of inviters X invitation conversion rate;
user sharing rate : a certain function/page , The number of sharing users accounted for the proportion of page views.
Activity/Invitation Exposure : The number of times the page was viewed by people in online communication activities. Generally refers to a circle of smiling friends.
3. User behavior indicators
The indicators for marketing and product operations were given above. Next, I will introduce you to the indicators related to user behavior. In fact, data analysis of user behavior is a very broad subject, and user behavior analysis is different in different business backgrounds.
- Function usage
function usage rate/penetration rate : the ratio of users who use a function to the total number of active users. For example, like, comment, favorite, follow, search, and add friends can all be counted as functions. These indicators all have a role in a specific business.- User session
session: also called session, is the entire process from start to end during a visit. On the web page, if there is no operation within 30 minutes, the default session operation ends. The whole conversation process is as follows:
- User path
diagram : The user's browsing track inside the product during a session. Through this, the critical path conversion rate can be processed. The specific path diagram is shown in the figure:
4. E-commerce indicators
Next, I will introduce the e-commerce indicators, which mainly include shopping basket analysis , repurchase rate and repurchase rate .
- Shopping basket analysis
pen unit price : the amount the user pays for each purchase, that is, the expenditure for each order. Corresponds to the customer unit price.
Unit price : the average price of the product.
Transaction rate : The percentage of users who successfully paid in the total passenger flow.
Basket Coefficient : How many commodities are sold in an average order. The shopping basket coefficient is the more the better, and it is also related to the commodity association rules.- Repurchase rate and repurchase rate Repurchase rate
is the proportion of users who have made multiple purchases in a period of time to the total number of consumer users. For example, in April, there are 1,000 users who consume, and 500 of them consume more than twice, the re-sale rate is 50%.
The return rate is the percentage of users who have spent a certain period of time and still consume in the next period of time. Taking the example just mentioned, the number of consumer users in April is 1,000, and 600 of them continue to consume in May, and the return rate is 60%.
5. Flow index
Many companies now value traffic. Many people use their spare time to watch some meaningful videos, which will generate traffic. Next, I will introduce some commonly used indicators in traffic analysis:
- Page views and visitor volume
PV : the number of views. The statistical indicators of the Internet’s early rise, a user’s request for access to a webpage can be regarded as a PV, and the PV is 10 if the user has viewed ten webpages.
UV : It is the number of visitors to the webpage within a certain period of time, and the number of unique visitors with official names. In the same day, no matter how many pages a user visits, he is only considered as an independent visitor. It should be noted here: Technically, UV will be measured by cookie or IP.
Percentage of visitor behavior: new and old visitors : measure the vitality of the website.
Visitor time : measure the quality of content, not the UV of the content, but the visit time of the content.
Average number of pages visited by visitors : measure the attractiveness of the website to visitors and the depth of the visit.
Source : Where do visitors come from? Technically, by extracting the parameters of the source website, you can distinguish
user behaviors such as SEM, SEO, or external links. Conversion rate : The proportion of users who have performed corresponding operations on the website in the total number of visitors.
Percentage of homepage visitors : The percentage of users who only viewed the homepage in the total number of visitors.- Exit rate and bounce rate
Exit rate : the number of page visits exiting from this page/the number of visits entering this page.
Bounce rate : the number of launches/visits after browsing a single page. It should be noted here that the bounce rate generally measures the landing pages, marketing pages and other pages. The exit rate is more product-oriented, and any page has an exit rate.
Second, the way to generate indicators
We have introduced you to the commonly used indicators in various scenarios above, and then we will introduce how to generate your own indicators and build a data indicator system. In fact, when we build a data indicator system, we need to understand the data indicators first. A qualified indicator requires a clear and complete definition. It is necessary to clarify the calculation rules and to understand the specific business meaning, that is, to meet the following three rules:
a. Significance of the indicator : How to use the vernacular to say this indicator, and what is its purpose
b. Statistical time : The indicator is time-sensitive, and it is necessary to clarify what time period the data is
. c. Calculation rules : Is it the proportion or the total number, who compares to whom? Or add another
Next, we take P2P products as an example to introduce the construction of data indicators.
1. Collecting data indicators
First of all, the ultimate goal of P2P products is to maximize the scale of investment and borrowing. So reflected in the data indicators, the two most direct indicators are the amount of investment and the amount of borrowing. The second is. The user of any product will have a life cycle, that is, a process from contacting the product to abandoning the product. And this process can be divided into multiple stages. As long as we think clearly about how to enable users at each stage to achieve the ultimate goal of the product, we can collect most of the data needed for the entire product. We divide the user base life cycle of P2P products into five major stages in natural order:
Of course, the investment user stage is the stage to achieve the ultimate goal of the product, so what we have to do is to make users in other stages enter the investment user stage. Therefore, in the eyes of the product, it is actually hoped that the life cycle of all users will evolve in this way.
Among them, the visitor stage, registration-investment, and loss recall stage are three relatively independent stages, and the corresponding data indicators can be obtained after specific details.
According to a complete user life cycle or business process, basically all relevant indicators of the product can be sorted out completely. When collecting data indicators, each business link can be considered in accordance with the main data evaluation purposes of scale, quality, conversion rate, and utilization rate/proportion. For example the first investment link:
Scale indicators : the number of people, the scale of investment amounts, the scale of investment orders, the scale of investment times, etc.;
quality indicators : registration/real name-investment cycle, per capita investment amount, etc.;
conversion rate indicators : registration / real name / recharge-investment conversion rate;
utilization rate /Proportion indicator : the proportion of initial investment in total investment users, etc.
2. Data index disassembly
After collecting the indicators involved in the product, you will find that there are many indicators. However, in a specific business, different business stages may focus on different indicators. For example, channel promotion focuses on customer acquisition costs and conversion effects, while investment focuses on investment amount and number of investors, and so on. Therefore, for different business stages, we need to select the core indicators of that stage, and then disassemble them, and then focus on them according to the dismantling indicators. For example, in the pull-out phase mentioned earlier, we are most concerned about the growth of new investment users, so we can split the new user growth data indicators into:
New investment user growth = browsing UV/APP activation × registration conversion rate × real-name conversion rate × investment conversion rate
At the investment stage, we are most concerned about the growth of the investment amount, so we can split the investment amount data index into: investment amount growth = number of new investment users × number of investments × per capita investment amount each time + old investment users × return investment Rate × number of investments × per capita investment amount each time. In this way, we know which core data we need to focus on and analyze at different stages. Only the subdivision indicators that maximize the effect of the core indicators are the most worthy of our attention.
In addition, different departments focus on different data. For example, marketing focuses on channel promotion data, the operation department focuses on business growth data, and the technical department focuses on product stability and performance data. The product department focuses on function usage data and user profile data. The finance department focuses on transaction data.
3. Determine the data dimension
After determining the data indicators that need to be focused, you need to subdivide the dimensions of the data indicators, for example:
Time dimension : second, minute, hour, day, week, month, quarter, year.
Channel dimension : promotion registration, natural registration, event registration.
By user type : new and old users, high and low net worth users, active/churned users
by terminal type : WeChat official account, PC official website, Android APP, iOS APP
by region : province, city, etc.
So far, a preliminary data indicator system has been completely constructed. However, it needs to be continuously adjusted and optimized according to the actual situation in the application. After all, even for the same product, the data that is focused on at different stages is different.
4. Summary
After constructing a data indicator system, I have a more personal understanding of the famous saying of Peter Drucker, the father of modern management, "If you can't measure it, you can't grow it effectively." Data analysis is a kind of thinking ability, not only a weapon for business growth, but also an effective way to solve problems.
3. Methods of using data analysis index system
Before we introduced the establishment of the data indicator system structure, we will then analyze the indicator system through data.
First look at the main indicator + judgment standard . For example, the main indicator is: sales amount. First, see whether the standard is met this month, and how much difference is below the standard. Let's look at whether the cumulative annual target is met, and how many deficits/surpluses. This is easy to see: know what the problem is and how big it is.
Looking at the classification dimension , which areas have not done well, which areas have done well, are they barely completed or continue to rise. Who has the ability, who is behind is clear at a glance.
Finally, look at the sub-indicators/process indicators . Which link is not done well? Is it too little promotion, too high a cost, too few users, or too low a payment rate.
4. Problems in the establishment of the data index system
After we have built the data indicator system, there will be some problems. The specific problems mainly include: the selection of indicators has no practical guiding significance and no judgment standard.
1. The selection of indicators has no practical guiding significance
New media all pursue the number of WeChat official accounts. If you rely on the number of readings for advertising, then the number of readings is meaningful. If you rely on graphics to sell products, then you should pay more attention to the conversion rate and product sales. After all, an exaggerated title can bring High reading volume, reading volume at this time is a vanity indicator.
Special attention should be paid to: The vanity index mentioned here mainly refers to the meaningless index, which is often very good-looking and can whitewash the performance of operations and products, but we should avoid using it.
2. There is no criterion
The index system is not a stacked index, but it is necessary to analyze the quality of the product through the index. An indicator is either good as long as it rises, or bad if it falls. It needs to have criteria for judgment.
5. Construction of business analysis framework
I have introduced you to the establishment, analysis and possible problems of the data analysis index system. Next, I will introduce you to the construction ideas of the business analysis framework. In fact, the whole construction ideas mainly include the establishment of the core thinking and indicators we mentioned earlier. , The relationship between thinking skills and indicators, and the business and indicators we mentioned today. Therefore, when we construct business indicators, we mainly start from the following three perspectives, namely:
- From the point of view of indicators
- From a business perspective
- From a process perspective
Next, we use the previously introduced marketing indicators, product operation indicators, user behavior indicators, e-commerce indicators, and traffic indicators as scenarios to make reference models for everyone.
1. Marketing model
According to the common analysis indicators given by the aforementioned marketing, we build its model as follows:
2. AARRR model
3. User behavior model
4. E-commerce model
5. Flow model
to sum up
The first two articles respectively introduced the summary of data analysis and some of the thinking methods of data analysis that we often use in data analysis . This article mainly introduces the business in data analysis, mainly including the commonly used indicators in specific scenarios in data analysis. These scenarios mainly include marketing indicators, product operation indicators, user behavior indicators, e-commerce indicators, and traffic indicators. The other is the way to generate indicators, the way to use the data analysis indicator system, and the problems in these ways. Finally, I introduce the construction of the business analysis framework. The content of the business direction has been introduced. The next article will introduce you to the use of tools, including core knowledge points related to Excel, python, power bi and mysql. Life is endless and struggle is endless. We work hard every day, study hard, constantly improve our abilities, and believe that we will learn something. Come on! ! !
Tomorrow is 1024, I hope everyone has a happy holiday. At the same time, I hope that readers who read my article will learn something.
references
[1] How to build a data analysis index system
[2] Data analysis method: 4 steps to build a data index system