User Operation —— Analysis and Growth Model of Six Users

4. User health analysis User health is a core indicator based on comprehensive consideration of user behavior data, reflecting the operation of products and providing early warning for product development. Including three types of indicators: product basic indicators, flow quality indicators and product income indicators. They constitute a system for evaluating the health of products, but each has its own emphasis. (1) Basic product indicators: mainly evaluate the running state of the product itself, such as PV, UV, number of new users, etc. UV: uniquevisitor refers to a natural person who visits and browses the web through the internet. However, the definition of ultraviolet light is time-limited. General 1 day, the number of independent visitors to the product, if a user visits many times a day, it will be counted as 1 UVs. Ultraviolet is one of the most important indicators to measure the order of products. PV: page views. Record every 65,438+0 visits of users to each page in the website. Users visit the same page many times, and the number of visits is cumulative. So the PV value is generally greater than the UV value. Number of new users: refers to new users, generally defined as users who have registered for the first time but have not made the first payment. The transformation process from new users to old users can be divided into four image spaces: frequency, quantity, time and category; (2) Traffic quality indicators: mainly evaluate the quality of user traffic: bounce rate, per capita browsing times, per capita stay time, user retention rate and user return visit rate; BounceRate: Bounce rate is also called bounce rate: the number of exits/visits after browsing a single page = single access/entry visites. The number of times you exit after browsing a single page-simply put, you enter a page and leave without clicking on any page. Generally used to measure the quality of users' access, a high bounce rate usually means that the content is not targeted (attractive) to users. The bounce rate of a page = (5/ 10) * 100%. The exit rate of a page =(5+2/ 10+2)* 100% for each person to stay. User retention rate: retention refers to "how many users have stayed". Users who start using the application within a certain period of time and continue to use it after a certain period of time are regarded as retained users. Retention rate = number of new users/number of new users (general statistical period is days). The retention rate actually reflects a retention funnel of users, that is, the process of new users transforming into active users, stable users and loyal users. Through the macroscopic observation of the user's life process, we can grasp the channel quality from one level, such as payment, stickiness, value, CAC cost and so on. User return visit rate: users who start to use the application within a certain period of time and continue to log in after a certain period of time are regarded as return visits. For example, the proportion of users using the App again after using the App for n days/weeks/months is called the N-day/week/month return visit rate. The difference between retention and return visit lies in: the former is how many users are added and how many users are left; The latter is the number of apps and software that users use and access again in a certain period of time. (3) Product income indicators: mainly evaluating the profitability and sustainability of products: GMV, ARPU and order conversion rate; ARPU: Customer unit price = effective payment amount/number of paying users, which reflects the payment amount of an ordinary user. The higher the amount, the more profits will be brought to the enterprise. Therefore, it is a very good way to stimulate gross profit by increasing the unit price of customers, such as our common promotion methods: 10 yuan buys two pieces, and buys two pieces for free. Conversion rate: order conversion rate = number of effective order users /UV. Conversion rate is the key factor of transaction income. The higher the conversion rate, the more users will place orders on the target page. User Payment Amount (GMV): The payment amount is the running water of the product in a certain period of time. Whether the product income is good or not depends mainly on the payment flow. What is the profit model and whether there is a stable income-generating ability is the ultimate test of a product (excluding strategic burning money and circle users first). The product income index has an identity: sales = number of visitors × transaction conversion rate × customer unit price sales = exposure times × click rate × transaction conversion rate × customer unit price; 5. Analysis of User Portrait The official name of a user portrait is UserProfile, which refers to a tagged user model abstracted according to the user's attributes, user preferences, living habits, user behavior and other information. Generally speaking, it is to label users. Labeling is a highly refined feature recognition through the analysis of user information. By labeling, users can be described with some highly generalized and easy-to-understand features, which makes it easier for people to understand users and facilitate computer processing. In the early stage and development period of products, user portraits are often used to help product operators understand user needs and imagine the scenes used by users. Product design changed from making products for everyone to making products with certain labels for 3-5 people, which indirectly reduced the complexity. The data content of the user portrait includes but is not limited to: (1) demographic attributes: including basic information such as gender and age; (2) Interest characteristics: browsing content, collecting content, reading consultation, purchasing preference, etc. (3) Location characteristics: the city where the user lives, the residential area, the user's moving track, etc. (4) Equipment attributes: terminal characteristics used, etc. (5) Behavior data: log data of users' behaviors on the website, such as access time, browsing path, etc. (6) Social data: social related data of users; The user portrait uses a three-dimensional space map of the scene, and the X axis represents the dimension of the business scene; The y-axis represents the dimension of the user tag; The z axis represents the service hierarchy dimension. First of all, user portrait business scenarios can be divided into user segmentation, product optimization, channel expansion, application promotion, risk control and so on. User tags have different definitions based on each business scenario. For example, the business of user segmentation scenarios is mainly the basic attributes of users, including gender, age and region. In the risk control business scenario, there are mainly user risk control labels, including scalper labels and abnormal score labels. Firstly, user tags are processed for user groups, and personalized recommendations are made according to different tags, and then decision-making operations are made at the operational level. Interlocking, so the core of user portrait is the establishment of labels. The user portrait analysis case explains: "He is a post-80s male white-collar worker who lives in Hangzhou with regular life time, likes cars and sports, and prefers Mercedes-Benz and Porsche." This passage is used to describe a user, not a class of users. So the essence of what we call UserProfile is that any user can be described by tags and data. From this, we can get the labels of such users and label them, and divide the users with such labels into a group or a class of users, so as to consider the later activities and user operations according to the characteristics of users. Of course, this kind of labeling can't completely guide the operation. For user operation, the classic model of user portrait guiding operation is RFM model. 6. Funnel model analysis Funnel model analysis, the essence of which is decomposition and quantification, refers to the transformation form and conversion rate of the whole process from the very beginning (acquiring users) to the final purchase, which is quantified by data indicators, and finally achieves the purpose of improving the overall purchase conversion rate. A classic application of traffic funnel model in product application is AARRR model, which comes from the book Growing Hacker. AARRR model is a practical model that combines the characteristics of the product itself and the position of the product life cycle, so as to pay attention to different data indicators and finally formulate different operating strategies. AARRR model: acquisition: how do users find and come to your product? Activation (browsing layer) Activation: How was the user's first experience? (Click/Participate) Keep: Will users return to the product? (Return visit/retention) Revenue retention: How do products make money through users? (Paid) Dissemination Retention: Are users willing to tell other users? Funnel model is very common in practical operation, and we can abstract three factors that determine the shape of funnel: time, node and flow. (1) time: the transition period, that is, the collection of time required to complete each layer of funnel. Generally speaking, the shorter the conversion period of a funnel, the better. (2) Nodes: Each layer of the funnel is a node. For nodes, the core index is the conversion rate, and the conversion rate = the flow from one layer to the next/to this layer. (3) Flow: the numerical size of each link, that is, the number of people. Traffic Funnel Model Hypothetical case description (the data are all virtual) We made a marketing campaign, and the traffic funnel model of the activity page is as follows: the user's traffic path is as follows: click on the main venue page → enter the product details page → place an order to buy → deliver goods (account number); Comparing the data of the traffic funnel in the main venue of e-commerce with the average map of the traffic funnel in normal stores, we can see that the jump rate of users in the step of "activity page → entering the product details page" is only 40%. Assuming that it is far below the average of 45%, we can think about why users don't click on products after entering the main venue. Generally speaking, the low jump rate is mainly due to the following reasons: (1) bugs in page development: mobile phone model adaptation problem, unable to click, empty page window, wrong link, etc. (2) The content does not match the drainage users: the drainage users are not interested in the products/content, and the BI recommendation is inaccurate; (3) Page operation problems: the points of interest correspond to the commodity commitment, the commodity profit is not enough, and the copy content does not match the landing page; After eliminating the problems one by one, you can initially lock the problem points and solve them in a targeted manner. To sum up simply, the funnel model is applicable.