How to improve conversion rate? High conversion rate tips!

"Why has the conversion rate decreased? I can't find the reason." The users are most likely to be changed by data fluctuations. If you check the data indicators every day without breaking down the users into different groups, you may never find the answer.

Essential perspective: User activity status

If you can clearly separate and analyze the user's active status, then about 70% of data analysis problems will be solved. What often "stuck" the analysis is this very basic but easily overlooked content.

Therefore, when you encounter any questions about data fluctuations, first segment the user activity status and clarify the activity status of the "users causing problems", which will definitely be of great help to you.

Number of new users + number of old users = number of active users

Let’s talk about the word “active” first. When we talk about active users, many people think that so-called active users are users who are very active in the product. To quantify it, for example, if a user uses it at least two or three days a week, he or she is considered an active user.

Sorry, that's not the case.

First of all, we need to clarify a concept. The so-called active, or active user, is generally defined in the industry as: a user who has opened a product within a selected time period is considered active, and is an active user.

So, activity defines a state, not a degree . Active users are divided into two categories: new users and old users.

I won’t explain the new users, but the so-called old users, that is, users who are not visiting the product for the first time, are all old users. So the relationship between these three concepts is that in the same time period, the number of new users + the number of old users = the number of active users.

For example, the new active data you see every day, such as: an average of 4,000 new users per day and 10,000 daily active users, means that there are an average of 6,000 old users visiting every day.

Lost users + silent users = inactive users

Well, since we are segmenting the user's active status, if there is an active stage, there must be an inactive stage. If you pay attention to inactive users, you may be slightly surprised that the number of inactive users is extremely large.

Inactive users are also divided into two parts: lost users and silent users.

The absolute majority of them are lost users. The so-called lost users are those who have used our products. However, the product has not been launched for a period of time, and this period is so long that we believe that the user has rejected or forgotten the product. In this case, we define such users as lost users. According to the business characteristics of different products, we generally divide them into 30 days, 60 days, or more than 90 days.

The other part is silent users. Similarly, silent users have used our products before, but have not launched the products for some time. However, this time period has a maximum and a minimum value. The maximum value cannot exceed the value that defines lost users, and the minimum value is generally one-third of the days that define lost users.

For example, a content community product with average reputation in the industry can be defined as follows: if the product has not been launched for more than 30 consecutive days, such users are considered to be lost users. Define the time period for silent users, which can be users who have not launched the product for 7 to 30 consecutive days.

Well, here is a key point that many people ask me: "How do you determine or define churned users?"

My answer is: "This threshold is defined based on our understanding of our own business and users and is gradually calibrated through data. There is no official formula."

Segment user activity status

As your product grows, the number of inactive users may be much larger than you think. It is very important to recall inactive users, and it is very effective if done properly.

Because of this, some users will become silent users or lost users and then be successfully recalled, becoming a very unique group among old users - returning users, or returning old users.

Why do we need to do such a segmentation?

Because the usage scenarios and experiences faced by a returning user are very similar to those of a new user, we also need to activate returning users and keep them active. However, they are not new users in essence, so they cannot enjoy preferential treatment such as financial products and novice labels.

Therefore, it is necessary to segment this type of users and provide exclusive operations and services.

For example, if your operation strategy is more detailed, you can use rules to give different rewards to returning users and continuously active old users to stimulate inactive users. First complete the return flow, then maintain continuous activity, and then receive rewards for continued activity, and finally turn him into a high-value user.

Changes in user activity status

First, when a user enters our product as a new user, there will be two directions:

1. If they are successfully activated and recognize the value of the product, new users will continue to visit and become active old users;
2. If a user does not access the product for a period of time after being added, he or she will become a silent user. When the user does not visit for a continuous period of time that reaches the standard of user churn, then the user is in a churn state.

So, how do active users and churned users continue to stay engaged on the platform? The construction and operation of the points mall is very important, so that users can enjoy value-added services in addition to product consumption.

At the same time, if a user who is in a silent or lost state sees our advertisement due to our recall strategy, or thinks of us when he has a need and visits our product again, such a user is in a reflux state. After the user returns, if he continues to visit, he will become an active old user.

Finally, if an active user, whether he is a new user, a returning user or an old user, he may become a silent user at any time. This is why data is needed to monitor the status of users in the product in real time so that strategies can be adjusted in a timely manner.

The impact of user activity status on business data

First, from the traffic dimension, we usually only focus on the number of new and active users; if we look at the change graph of user activity status, you will find:

• The newly added status is the starting point for any user.
• The silent state is the inevitable path for a user to go from active to lost.
• With any recall strategy, users will definitely experience a state called reflux.

Therefore, new addition, silence, and return are the three key nodes of the entire user status. Everyone pays enough attention to new users, but silent and returning users are often easily overlooked. Therefore, when we do traffic analysis, we must be able to accurately measure new user acquisition, activation and recall.

• The analysis of new users is to attract new users and promote their activation.
• The purpose of analyzing returning users is to promote their activation after recall.
• The purpose of analyzing silent users is to take preventive measures and recall them in time.

Secondly, from the conversion dimension, the focus of our analysis should be on the people who truly influence conversion.

Many companies will encounter the dilemma of "why the conversion rate has decreased, but I can't find the reason." In fact, we need to know that users are the ones most likely to be changed by data fluctuations. If the data indicators you check every day do not break down the user's active status, for example, once the quality of new users decreases, the conversion rates of all your key indicators will decrease.

Therefore, when looking at the key conversion rate, it is necessary to segment it and segment the conversion rates of different user statuses.

For example: the conversion rate of the first trigger; the conversion rate of repeated triggering by old users. The conversion rate will also be closely related to whether a membership points redemption system has been established for user points. If the user experience is done right, it will greatly help improve the conversion rate.

Third, from the retention dimension, although we always mention the retention rate, in fact, 90% of people have a very shallow analysis depth on retention.

If we want to expand on the content related to retention, it involves user life cycle calculation, cohort analysis, etc. My suggestion is: If you want to understand the value of segmented user status, then at least, in addition to retaining new users, you should also measure the retention of returning users and old users, so that you can clearly evaluate the operating results.

Activity is a state, not a degree. We need to segment users ’ activity status and meet the needs of users in different statuses to motivate them to complete conversions. Accurately measure the user's active status and formulate strategies for attracting new users, promoting activation, recalling users, etc., evaluate the user's value level, locate the people who truly influence conversion, and measure the retention indicators of active users at each stage. Only with such refined operations and improving the satisfaction of different users' active status can we ultimately achieve business growth.

The development of a user active points mall is essential in product operations. There are many free points mall systems now that can be directly connected to the points mall API to quickly help active users.

Related reading:

1. How to use coupon activities to increase user conversion rate?

2. Community operation, how to improve activity and conversion rate?

3. How to effectively improve your product conversion rate? 3 case studies!

4. Community operation: How to increase the conversion rate of fission communities by 10 times?

5.4 steps to increase conversion rate by 50%, revealing the underlying logic of traffic acquisition!

6. Promotion and marketing: What to do if traffic is high but conversion rate is low?

Author: Mai Le Points

Source: McLean Points