How to build a community O2O user operation system from 3 aspects

The o2o company I work for is a one-stop community sharing service platform in China that is the first to implement the new retail concept and connect online and offline life scenarios. Using APP as an online tool and a three-kilometer radius of the community, various community services are provided to residents through offline stores.

In just over a year, we have covered 12,173 communities and the number of users has grown explosively. The question now is - how to build a feasible user operation system?

This system not only ensures the activity and retention of platform users, but also effectively promotes the operational connection between city companies, stores and channels.

After taking over the user operation project, my team and I mainly thought about the difficulties of the project:

1. Different from general APP products that use the AARRR user model to establish an operation system for the pure online user life cycle of user acquisition, activation, retention, revenue, and recommendation, our operation scenario is relatively much more complicated. The user life path extends to offline, that is, user acquisition is mainly completed offline, and needs to be guided to online consumption, and then to offline services. Each path requires operation and maintenance of users.
2. The second is the complexity of the company's business. The platform's product categories basically cover all aspects of community life, from daily fresh fruits and vegetables to laundry, home repairs, housekeeping, etc. The basic business logic is that users locate their own community, and the store closest to the user will deliver the goods. Due to differences in service communities, product displays in each store vary greatly. This results in a certain bias in store category sales, with only a few popular items with high demand often selling well, resulting in many services and products on the platform being unknown to users. How to better help stores match products and services to users in need is a big challenge.
3. After a year of explosive growth, the rate of new user additions has slowed down significantly. Due to the lack of an effective user growth system, the community user penetration rate has not reached saturation. How to ensure that users can continue to grow is an extremely important operational task.

In response to difficult issues, I will explain how to build a community O2O user operation system from the following aspects?

1. Ways to increase community o2o users

When it comes to growth, the general idea is the operation of traffic channels , including ASO optimization and paid promotion of various application stores, as well as new media channels, local promotion , etc. This kind of thinking is centered on CAC, maximizing the optimization of CAC to the minimum and finding the best channel for traffic generation.

With the disappearance of traffic dividends, more and more companies have found in channel operations that no matter how correct the channel system is, during the implementation process, the operation strategy guided by the KPI of new user registrations has resulted in the online user retention rate being unable to increase, and the CAC remaining high. Many products have fallen into a growth black hole after a period of growth explosion, and growth has stagnated.

However, in the process of building the growth system, we found that the indicator of new registrations in community o2o operations is not the most important. The two most important indicators are the new user's first order conversion rate and the next month's repurchase rate.

New users are guided by the store to register, and whether they place their first order and repurchase the next month are important factors that affect whether the user can stay on the platform. Blindly pursuing growth in the number of registered users in the early stages will instead bring a large number of zombie users or one-time consumer users to the platform, which is not conducive to the health of the platform's overall users.

Guided by these two core indicators, we use stores as traffic centers and community users within three kilometers of the stores as new customer acquisition targets. We divide the communities into sections and assign each person to be responsible for an area without overlap. We set an operational goal for each delivery person to develop 1,000 consumer users, and establish a community with delivery personnel as the smallest operating unit.

• On the one hand, maximize the penetration rate of community consumer users;
• On the other hand, we maintain users through the community and increase the consumption frequency of each consumer.

In terms of operations management, we have an offline delivery team of about 4,000 people. Everyone’s main job is delivery. During the delivery process, we need to use fragmented time such as return trips to attract new customers. In addition to the normal incentive mechanism, we need to know the community coverage of each store, the distribution of districts in the community, the penetration of consumer users in each community, and the actual completion status of each delivery staff in attracting new customers.

After submitting the requirements to the data development department, the data department developed an offline user data operation platform, which can intuitively monitor offline new user data and community user penetration rate, concentrate firepower on ground promotion in communities that are difficult to penetrate, and have delivery personnel collect portrait data for each community and user to form a complete community portrait model and user portrait model.

Under the guidance of this growth system, we even completely gave up the use of online promotion channels in the later stage, and users achieved free self-growth. The monthly repurchase rate of users in some stores can reach up to 80%. Although the overall user growth rate of the platform has slowed down, the quality of users has achieved a qualitative improvement.

2. The way to fine-tune the operation of community o2o users

Refined operations are inseparable from the stratification and grouping of users. In the early stage, we also built a user tag management platform, but found that most user tags are useless.

Why doesn't it work?

Because it is out of business context.

For example, taking gender as an example, 40% of users are labeled female, 35% of users are labeled male, and the rest are of unknown gender. But after stratifying users by gender, what can operations do?

You may imagine promoting commonly used products for women to the female group, but this will have the same effect as promoting it to all users on the platform, because even if they are all women, everyone's consumption preferences and needs are different, which requires a user labeling system with business scenarios as the core.

What is a business scenario?

Business scenarios must be based on business and be able to guide business.

• For example: Our platform has 38 channels, and each channel needs consumer users. How can we find potential consumer users for each channel from the platform users? This is a scene.
• For example: the consumption time window of each channel is different. How to divide the existing consumer users of each channel into low-frequency, medium-frequency, and high-frequency users to guide the activity operation of each channel is also a scenario.

After communicating with various channels and collecting requirements, we sorted out 13 business scenarios and built a corresponding user label system for each scenario, which can be used to guide the business. There are 11 specific categories of labels, each of which has several subcategories, and each subcategory has a varying number of labels. The same requirements are still met by the data department, which develops a label management platform through data modeling and other forms, and realizes real-time updating of labels.

How to use specific tags?

For example: As we mentioned earlier, the stores only have a few best-selling products that are selling well, but many people who have demand for many products and services are not aware of them, so they need to actively recommend them to users.

How to recommend it then?

Traditional user analysis is based on user historical behavior, and recommends products to users based on what they have purchased, but it has been found that the effect is not ideal. The reason is: simply looking at the user's historical behavior cannot guide the business. Historical behavior has a lag. Except for some high-frequency products, the user's needs have been met after purchase. Pushing such product information to users is actually a disturbing behavior.

At this time, a type of label is needed, that is, interest prediction label. Interest prediction not only looks at the user's historical behavior, but also comprehensively considers dimensions such as the similarity of platform products and time decay factor to label users with XX categories and products. Then we grouped users by tags, planned corresponding product activities, and pushed activity information to the grouped users. After a period of optimization of the tag model, we found that the turnover rate of slow-moving products increased significantly.

This is the essence of refined user operations - that is, labels must be based on business scenarios, and do not pursue a large number of seemingly rich but useless labels.

For example, some companies make 360-degree portraits and label users’ interests as reading, listening to music, etc. Without mentioning whether the content filled in by the users themselves is their true thoughts, there are reliability issues in labeling users in this way.

However, the label that users like to read books is really useless, because the operator does not know whether the users like to read literature or economics, so there is no way to plan targeted activities. In addition, user data operation is also a very important part of user operation. I will not give a detailed introduction here, and I will write a special article to sort it out later.

3. Community o2o community operation

In terms of community, we are still in the exploratory stage, but the effect of community operation has been exerted. 30% of the orders of some stores come from the community, and community users contribute about half of the repurchase rate of the stores.

Our community mainly solves several major problems:

1. User service issues. The community is formed by offline delivery personnel. Each delivery personnel serves a fixed area. After a period of door-to-door delivery and providing convenient services to users, the delivery personnel have a certain offline connection with the users. Then when users encounter problems, they post them to the group, and the group owner actively responds and answers the questions, which to a certain extent has successfully shared most of the customer service pressure.
2. Problems with user ordering. Why can the community help us contribute half of the repurchase rate? The reason is that the main group of community users we serve is almost entirely middle-aged and elderly users. An obvious characteristic of these users is their poor learning ability and inability to use our APP. After they enter the community, they perfectly solve the problem of placing an order. Just say what you want to buy in the group, and the group owner will respond immediately and place an order on behalf of the user. Within an hour, the group owner will deliver the goods to the customer's door. We call this experience community butler service.
3. User activity issues. Our store often holds in-store activities that require users to participate. We just need to send an event notification in the group, and many old users will come to support us. Some online promotional activities can also be spread through social networks.

Therefore, the operation of the community is based on the ability to solve problems for users. Imagine: some community operators use various means to attract users to the group, but the purpose is just to promote the product. The means of daily activity is to send red envelopes. After a while, you will find that everyone only pops up when sending red envelopes, and usually they all turn on the do not disturb mode. The effect of this kind of community operation is basically zero.

The above is a summary of my experience in the actual user operation process. In a nutshell: to build a user operation system, you must find a growth model that suits your own company and build a sophisticated operation system to guide operations. Offline, you can use the community to maintain users and increase the user's life cycle value.