What are the user operation strategies?

The author is a community o2o user operation and summarizes four strategy systems. Growth framework: user growth team+core growth channels+growth tools. User modeling: user model construction, including label portrait, user value model, user preference identification model, user churn early warning model, user activity model, etc. Scenario layering: 12 operation scenarios, each scenario is layered and grouped based on user tags and modeling tools, and corresponding precise marketing means are formulated for operation. Data operation: core operation index system+data analysis model. Under the growth framework, the marketing department is responsible for channel operation, new media is responsible for content output of social channels, and user groups are responsible for user activation and promotion. At each operation node of AARRR, the growth index is defined. Channel operation is mainly assessed in acquisition nodes: new users, acquisition cost (CAC) and new user retention rate. The main assessment of products at activation and retention nodes: registration conversion rate and functional retention rate; Activity operation is mainly assessed in activation and retention nodes: DAU, MAU, DAU/MAU；; ; User operation is mainly assessed in revenue and recommendation nodes: user conversion rate and K factor. Products need to have their own good core growth channels and growth tools, such as coupons such as social sharing. User models include label portrait model, user value model, user preference identification model, user loss early warning model, activity model and so on. The value of labels lies in helping operators to achieve scene stratification for users based on their services and design targeted marketing activities. The value of portraits lies in helping operators understand the characteristics of each group; User value model can identify high-value user groups; Preference identification model helps operators to push products in a targeted manner; The early warning model of churn can retain users before churn, and the activity model can wake up and promote activities in a targeted way. The establishment of the model needs a special data product team to complete. When marketing based on user model, operators need to pay attention to marketing effect analysis and iterative optimization of marketing scheme. Platform-based business can derive several operation scenarios, and each scenario needs to operate different user groups, which come from tag models and various user models. The core index system can monitor the development trend of users' operation and know the basic information such as user activity and health in real time. User data analysis system can help operators locate problems in time and optimize products. The first is the construction of core index system, which must be closely combined with product objectives. For example, the goal of bicycle products is to obtain rental income, and its core indicators should be around paying users. The goal of information products is to generate traffic from users' reading, and its core indicators should be based on DAU, browsing depth and duration. At the same time, people at different levels in the enterprise pay different attention to the core index data, and the leadership level pays attention to the volume, cost and income of large-scale users; The operational level focuses on user activity, retention and transformation; In the construction of index system products, we construct the core indicators of consumer users from four dimensions: new customer acquisition ability, health, preference and purchase behavior. 1. Analysis of the growth potential of new customers: cities, stores and promoters understand the overall development and development potential of users in regions, business districts and communities; User source channel analysis: every channel wants to know the current push channels, and which channels do users mainly come from? Which channels are of high quality, so as to optimize the channel strategy; Analysis of new products: stores and places want to know which products in the region contribute the most to new products, and the products that customers place orders for the first time are defined as new products; Analysis of novelty-seeking preferences in various communities: Stores and promoters want to know the preferences of new users in various communities in the region, such as: A community prefers electronic products and B community prefers fresh food, so as to promote novelty-seeking in various communities. 2. User Health User Value Analysis: The channel wants to know who its loyal users are and can find these high-quality users to let them participate in the activities. Similarly, promoters can invite these users to the store to participate in activities online; User churn index: the channel wants to know which different groups of users will be churned out and how to prevent them from being churned out; Community users' contribution: stores and promoters want to know the GMV contribution rate of each district where the promoters are located, and the distribution in the district should have weekly and monthly trend charts. 3. User preference category preference: stores, promoters and channels want to know which community/region is more inclined to consume what kind of goods (cross relationship between buyer's location and category); Activity preference: stores, promoters and channels want to know which community/region prefers what kind of activities (the cross relationship between the buyer's location and activities); Price preference: the channel wants to know what prices users of different categories prefer, so as to push various price segments to the corresponding users (the cross relationship between categories and prices); Contact preference: stores, promoters and channels all want to know which channels users of different categories prefer to buy (the cross relationship between category and contact). 4. Repurchase rate of different user groups: The channel wants to know the repurchase rate of new and old users and find out high repurchase products, adjust the operation strategies of new and old users in time and do a good job in commodity operation, and monitor them on a monthly basis. User path analysis: the channel wants to know the user participation from the channel home page to the active page, and in which link the user is lost, so as to do a good job in page operation. Secondly, the data analysis system needs to build a series of analysis model tools to help operators locate the problems in the operation process. Model tools include funnel analysis model, attribution analysis model, micro-transformation analysis model and queue analysis model. How to attribute common analysis scenarios such as DAU fading? How to attribute the low registration conversion rate? How to attribute the low retention rate of new users? Take the low registration conversion rate as an example to briefly describe the analysis method: step one: disassemble the influence dimension; Step 2: Disassemble the subdivision indicators under the dimension; Step 3: Locate the problem. Registration conversion rate can be divided into two dimensions: channel and product. Decompose the segmentation indicators under each dimension, and the channel segmentation indicators include media delivery, advertising types, advertising content and keywords; Products include registration logic, product design, input method, product stability, etc. To locate the problem, we need to look at the breakdown indicators one by one, and find abnormal data, for example, look at the conversion rate of each link through the funnel, and focus on the links with low conversion rate. If it is a channel problem, optimize the media, AB test the advertising content, and accurately locate the keywords; If it is a product problem, optimize the registration logic and interface to improve the stability of the APP. Summarizing the four strategic systems, we can find that user operation is no longer a simple job of finding a few operators to do a good job of group operation, nor is it a job that a few user models can quickly enhance the user value of enterprises, but an operation system that enterprises need to invest manpower, energy and material resources for a long time. The significance of user operation to enterprises is self-evident. The growth of the overall performance of an enterprise is inseparable from the expansion of the scale of high-quality users and the improvement of the life cycle value of users. Establish a user churn warning system 1, define churn 2, select appropriate data to represent churn 3, define forecast time window 4, establish a model to make exploratory analysis on feature data and churn fields, check whether there is strong correlation between each feature dimension and churn, keep strong correlation and delete weak correlation. The most common method to establish user churn rules and predict other users churn is to use decision tree algorithm to generate user churn rules. 5. Construct the operation strategy of lost users: segmentation strategy or scoring strategy. In addition, if you want to learn user operation systematically, then this course may be suitable for you: