Three aspects tell you how to implement scenario marketing in user operations.

When doing user operation, making clear the scene can make us familiar with user behavior and set up effective marketing strategies. So what should we pay attention to when implementing scene marketing? The word "scene" must be familiar to everyone. We should pay attention to the scene when doing activities, and we should also pay attention to the scene when doing O2O. For example, going to the store is a scene, and going home is a scene. So many times we understand this scene, in fact, it is more limited to a concept, but it is a little confused when it is really used. When I started user operation, my understanding of user operation was to plan an activity, push it to existing users, and then see how many orders were converted from specific activities, which was 1.0 operated by users. Later, the user operation was understood as the need to establish a membership system and a points plan, and to allocate different rights and interests to different levels of members, so that users are expected to grow according to the growth route we designed. Now I think this should be 2.0 run by users. Later, the whole user operation has been systematized, such as life cycle management, label portrait system and automatic intelligent marketing system, which can do some more refined operation work, which is user operation 3.0. In the 3.0 period, we will give more consideration to the users themselves. Because in the period of 1.0 and 2.0, the focus of our thinking is from the perspective of operation itself, what kind of activities we can do is activity thinking. What kind of membership rights level can promote the repurchase of members, which is operational thinking. Considering users is a kind of user thinking, so we need to know what our users are like. Only by knowing the users can we have corresponding user operation strategies. How can we deeply understand our own user characteristics? From a technical point of view, what we need to do is to collect user portrait fields and mark user behaviors, so as to improve user portraits and help us understand the characteristics of a group of users in depth, and then carry out targeted marketing or maintenance; From the marketing point of view, what we need to do is to know under what circumstances users will buy. And analyze the motives and reasons of users' purchase. 0 1 Why do you want scene marketing? Take buying cakes as an example. There are at least four scenarios for eating a cake: birthday, Valentine's Day, wedding and party. Birthday scenes are very common. In this scenario, users usually buy cakes for their children, parents, lovers and relatives instead of their birthdays. Therefore, the birthday gifts we make now are often very unsuccessful marketing, such as birthday cake coupons, and the conversion rate is less than 3%. Why is it an unsuccessful marketing? It is because when we ask users to apply for membership cards, they will fill in some basic field information, including birthdays, but at this time, shop assistants often tell customers to fill in their own birthday information. In this scenario, although the customer's specific birthday is known, the cake business has no better transformation effect, that is, the specific scene is ignored in the user's operation, and the marketing often fails to achieve the expected goal. In view of this scenario, we need to do some thinking on user portraits to improve the conversion rate of cakes. Of course, everyone will think of the birthday housekeeper strategy, let the user fill in all the birthday information of his family in our APP and give him a cake coupon when his family has a birthday, but this is a wishful thinking strategy, because at the moment when personal information is extremely sensitive, few people are willing to disclose the real birthday information of their family to businesses. In addition, we can have a better strategy based on store initiative and big data operation: when introducing customers to receive membership cards, the clerk will add that if you don't often buy birthday cakes for yourself, you can fill in your birthday as your family's, so that your family can get our cake coupons in advance when they celebrate their birthdays. In terms of big data, we judge the frequency of users buying cakes through modeling. Based on the frequency data, we analyze which month users often buy cakes, so as to label users with corresponding cake buying opportunities. Combined with the automatic intelligent marketing system, when the system judges that the user buys the cake, it automatically pushes the coupon to the customer's member account seven days in advance, and informs the customer to check it by SMS. Through this case, it can help us to understand the value of user data operation more deeply. I understand that some enterprises often rely on the collection of portrait information when making user portraits, such as encouraging users to improve their basic information through rewards, collecting user information through shop assistants, and even collecting family information with the help of delivery staff, including whether the residential area is upscale, how many people and children are there in a family, and so on. Is this information available? I can tell you that 90% of the information is wrong, because users don't want to reveal their true information, and they will fill in some fake birthday data to avoid the harassment of merchants. Shop assistants and deliverymen are not so conscious. They just want to complete the tasks assigned by the headquarters, and the authenticity of the data cannot be verified by the headquarters. User behavior data is in our database, which is completely real transaction data and can't be faked. We put a series of marketing value labels on users through modeling and analysis, which is the value of scene+data! In fact, when we are talking about what is scene marketing, we have demonstrated this necessity, that is, scene marketing makes user operations more refined and precise. We should not only be familiar with the specific scenarios of user operation, but also know how to operate the users of the platform in each scenario. Let's talk about the second scene of eating cake, which is Valentine's Day. The idea of general activity operation is to choose a Valentine's Day cake, choose a theme, and then design corresponding activities, such as discounts, buying gifts, and half-price for the second time. And then do a good job of online and offline promotion to attract customers in need to buy. From the perspective of user operation, this activity must first have the reasons to impress users and the motives for users to buy, such as attractive activity intensity. Of course, these two points are still within the scope of activity operation, and the most important is the third point. We hope to find convertible users who meet this scenario. There is a very important cost here-if we have an APP, we can push the activity to all users through the APP message, but there may be a single group of your users, and pushing this activity to them will achieve the opposite effect, not only without conversion, but also with a high unloading rate. If there is no APP, users can only be notified by SMS. If you have 5 million members, I believe the boss will definitely not approve the budget of this SMS, because the ROI of your activities will be very low, and even this marketing will become a loss-making marketing. In this case, you need to find out which users in the user pool will respond to the activity. We need to build a marketing response model. In this model, we will screen out the previous Valentine's Day activities and similar activities, and then screen out a group of sample users to find out whether these users have participated in these activities through the database. With the modeling data, we can train a model, which can help us judge the responsiveness of users, and we can screen out users with higher responsiveness to reach them, thus effectively improving the conversion rate and ROI. In fact, the case of this scene provides you with a user operation idea. I talked about a five-step method of user operation: first, we must clarify the scene, and then we must establish a model to find the target user group we want to market through the model. Derivative analysis is carried out on the target group, and in-depth analysis is carried out on the group characteristics based on the user portrait. Finally, according to the characteristics of the group, the corresponding marketing strategy is planned and automatically pushed. How to implement scene marketing has three important supports: data support, analysis support and trigger support. 1. The data support in the two scenarios mentioned above is inseparable from the modeling of user behavior data. Through the model, we find a more suitable way to establish user behavior labels, determine the frequency, early warning value, sensitivity and other results, and gradually establish a stable operation plan and operation plan. Some fixed operation files can be solidified on a certain day or even a certain time of the week to form a fixed operation plan. 2. Analysis and support are particularly important for us to do user operation analysis. There are many solidified analysis models in user analysis, such as peer group analysis of users, which is a very important analysis method. The most commonly used tools for queue analysis are user retention tables, such as product iterative optimization of APP, queue analysis of initial users (people who open the application for the first time) 1 week, 2 weeks and 3 weeks later. You can clearly know the impact of product changes on an indicator of a specific group of people. It is also a key indicator to measure the effect of product iteration, market and operation promotion. 3. Intelligent support user operation is about automation and intelligent marketing. A company contacted before has a very primitive user operation method-the operator does activities first, then sends an email to the data department to let the data personnel extract user data, and then analyzes which user groups are divided after the data comes out, and then sends an email to the leader to ask him to approve the technology of extracting the user's mobile phone number. After the user's mobile phone number is put forward, he also sends an email to the technology saying that he wants to push or send a message. After this long process, all departments are working, including leaders. It sounds like everyone's work is saturated, but the final result is that the operation is crying-from the start of the event to the push of the event, one week is slow, and one week is two weeks. In the process of such a long approval time, on the one hand, it is easy to miss the opportunity of push activities, on the other hand, the user group has already undergone qualitative changes, and the user group finally pushed has long been irrelevant to the original user group. Efficient user operation is: we set up an automatic intelligent marketing process directly in the management background. After this process is realized, there is no need for manual intervention. The system will run a preset target user group once a day, initially label users, then push preset activities or coupons, and automatically push push or text messages. After a period of time, the system process will execute the analysis node and label the users who participate in the activity with the effect tag. It is very important to compare the number of users under the initial tag and the effect tag. We will explain to you what is scene marketing through two small cases, so that we can discuss three questions and I will knock them for you together. Everyone can speak freely in the comment area: What is your industry? What scenarios will you have when you are doing user operations? How do these scenarios do user operations? Welcome everyone to leave a message in the comment area! Author: Zhao Wenbiao, official account of WeChat: User Operation Observation (ID: Yunyingguancha), user operation manager of a community o2o platform, with 7 years of experience in Internet operation, willing to share dry goods, welcome to exchange and learn.