3 tips for refined operation strategies!

As the cost of acquiring traffic gradually increases, the overlap between users of various products is getting higher and higher. At the same time, the emergence of Taobao's "Thousands of Faces for Thousands of People" has allowed everyone to see the possibility of refined operations targeting different groups of people. Carry out refined operations for each group of people, ultimately improve the efficiency of traffic utilization and turn traffic into real retention.

1. What is refined operation?

The opposite of refined operation is extensive operation, which is the operation mode that most people will adopt in the early days. What everyone sees is the same, and the operational strategy is fed back based on the final experimental results of the public.

On the other hand, refined operations are relatively more detailed. Different content is displayed to users in different life cycles, different types of users in the same life cycle, or even each user, and different operation strategies are adopted to complete the final conversion. If the early operation strategy was a one-to-many model, then refined operations are a many-to-many correspondence.

Under the refined operation strategy, each type of user or even each user is labeled with multiple labels. In this case, the priority of the user's refined operation strategy emerges. 20% of users generate 80% of revenue. Only by focusing on user management and maintenance of core users can we create greater value. Although the user data of 80% of users looks very good, it is difficult to create greater value. When time and energy are limited, focus your core energy on the things that can produce 80% of the benefits.

2. The key to refined operations

We briefly talked about what refined operations are earlier, and now we have a general understanding and outline of refined operations. Next, let’s talk about how we should carry out refined operations in our daily operations. The following content is purely my own views and opinions and is for reference only.

1. User stratification is fundamental

If refined operations are the technique, then user segmentation is the way. This is why user stratification is the basis of user operation. I have written articles about user stratification before. You can click on the article “A Brief Discussion on User Stratification in User Operation”.

Users can be stratified into various categories based on their basic information, behavioral information, and consumption information.

However, the key to how to perform stratification is to see what goals need to be achieved in the end. Carry out user segmentation around the goal. In the early stage, carry out preliminary user stratification and segmentation. When it is running smoothly in the later stage, if there are some obvious differences in user behaviors among this layer of users and there is room for further improvement, you can carry out user stratification one more step.

User segmentation is like cutting a whole cake into small pieces of different shapes and giving these small pieces to the most suitable person.

Taking Taobao as an example, users’ basic information can be divided into: gender, region, age, general occupation, and average monthly spending amount.

At the same time, based on user behavior, the user’s identity attribute labels can be inferred: marriage stage, whether there is a house, occupation and other very detailed information.

User behavior can be further divided into: daily shopping time, shopping preferences, shopping frequency, single consumption amount, coupon preference, shopping habits (search shopping or browsing shopping) and other information.

One thing that needs to be pointed out here is that the gender, age, and occupation labels of many products are not very accurate. These labels either come from manual filling in by users (users may fill in the labels randomly) or from manual labeling of the products. (Divided according to certain behaviors that users may have).

The reason why Taobao has so many user tags is that the information of many products is connected and combined, and then gradually optimized to form a combination of multiple tags for one user. Another core point is that from the act of shopping, we can analyze many user behaviors and user scenarios, and then use Taobao's data to conduct repeated proofreading and optimization.

I remember a case I saw before: a shopping mall found a group of people who bought sanitary napkins every month, but in the following months there were purchase records, but no behavior of buying sanitary napkins. So the shopping mall pushed promotional information of infant and child products to users who had been in contact with them before and had shopping behavior but had not bought sanitary napkins recently. This push brought a lot of additional sales to the mall.

Based on the user behavior stratification, user insights are also key. Why do users behave in this way and why do these behaviors disappear? There must be some reason behind it.

I have read an article written by Mr. Liang Ning before, about the question of which of the three major fan platforms is more accurate:

Taobao’s recommendations are much better than JD.com’s, but still not as good as Pinduoduo’s. You can search for "Wuxi spare ribs" on Taobao yourself, and you will see that Taobao's related recommendations are still "Wuxi spare ribs" and "Sanfengqiao", which are matches for keywords like this.

When I searched for "Wuxi spare ribs" on Pinduoduo, in addition to associating "Wuxi spare ribs" and "Sanfengqiao", Pinduoduo also recommended "Four Happiness Meatballs" to me. What does this mean? This means that Taobao is doing keyword association, while Pinduoduo is doing user understanding. It tries to understand my search motivations and preferences.

There is a core purpose behind user behavior. Making related recommendations based on search motivation can better adapt to the scenarios users want than keyword association and promote user conversion. What lies behind this is user insights and motivation analysis through user behavior.

2. User Behavior Maps Are Key

User segmentation can be said to be the basis of refined operations or even user operations. On the basis of user stratification, customized recommendations and operations must be made according to user behavior maps.

On this basis, user behavior is divided into several key points of user behavior according to the attributes of the product, so as to recommend the most suitable products to users at the key points of user behavior and maximize user conversion.

For example, in user behaviors such as check-in and welfare pages, most of them are users who are greedy for small profits. The characteristic of this type of users is that they have a lot of time, but it is not easy for them to convert to paying. Therefore, products that push coupons or incentive videos to these users have a higher conversion rate.

The user's Aha moment is the climax of the user experience of the product. If you can recommend some paid products for the first time to the user at this time, it will easily lead to user payment conversion.

At a certain moment and for a certain purpose, a user comes to a certain page. Based on the user's purpose and user attributes, the most suitable product is recommended to the user.

I saw that Zebra did a pretty good job in its refined operations before. In the information flow advertising, if the user fails to pay, Zebra will directly pop up a QR code to follow the official account to get a 1 yuan discount course (original price 69 yuan), promoting the conversion of potential users at a preferential price. This way, the conversion rate of converted users is very high. At the same time, recommending products of appropriate prices to users based on their current behavior makes conversion easier.

3. Data analysis is the foundation for a successful strategy

Fine-grained operations are a very rational thing. Every investment and every change in strategy is adjusted based on the monetization of data. Whether the final strategy is successful also requires analysis and comparison of data.

The way to ensure that the final data conclusion is more accurate is to conduct AB testing , forming an experimental group with a new strategy, while the control group remains unchanged. The only variable is the change of one of the variables. After controlling the variables, it is possible to more accurately determine whether the experiment is successful. Finally, the data of the experimental group and the control group are compared to see whether some data indicators of the experimental group are better than those of the control group.

Sometimes when we do experiments, we find that after a certain data indicator rises, another data indicator falls. At this time, it involves designing the overall goals and sub-goals of the experiment.

For example, we want to increase the conversion rate, but we may find that the arpu value decreases while the conversion rate increases. At this time, we will look at whether the overall revenue indicators have increased. If the overall revenue increases, it means that the strategy is successful. However, although the conversion rate has increased, the revenue from the increased conversion rate cannot make up for the gap caused by the decreased arpu value. In this case, the strategy may still need further adjustment and optimization.

At the same time, it is also necessary to see whether the sample size of the experiment meets the standard and whether there is enough sample size to support and ensure that the final result is a significant feature.

Generally speaking, if the significance characteristic is greater than 95%, we say that there is a significant difference between AB. Here I would like to recommend a platform for calculating significant features: Yunyan. The lift rate can be calculated by directly entering the data.

Generally speaking, the results of AB testing are the most accurate. When conducting comparative analysis from AB testing, it is also necessary to distinguish the primary from the secondary so as to better implement subsequent strategies.

The above are some thoughts and feelings about refined operations. Everyone is welcome to leave a message in the comment section.

Author: Creative Gorilla Factory

Source: Creative Factory