Stock, increment and retention in product operation!

Some people interpret data as just data, without providing any support for the business, or when leaders ask about some business indicators, they cannot answer fluently. They have clearly read the data, but they just can't answer.

In fact, the fundamental problem is that there is no internal data system, which leads to unsatisfactory data produced by technology, or product and operation interpretations are based on their own experience, resulting in data errors.

The reason why we cannot answer the leaders’ questions is mostly because we are unable to convert user data and behavioral data into business data.

This article is mainly based on the summary of common data types, which is convenient for forming a data system within the company and eliminating errors in data extraction or interpretation. It also makes it easier for you to convert user data and behavioral data into business data and answer your boss's questions fluently.

Common data can be divided into three categories: user data, behavioral data and business data. User data generally includes stock, increment, retention and source; behavioral data generally includes PV, UV, visit depth, conversion rate, duration, bounce rate; business data generally includes total amount: GMV, total playback time, total reading time, etc.; per capita: ARPU, ARPPU, average playback time per person, average reading time per person; number of people: number of paying users, number of viewers, number of readers; others: payment rate, viewing rate, reading rate, etc.

The indicators extracted from business data may vary depending on the business you are doing. You only need to express your own business indicators.

In fact, this is a series of articles on how common data indicators in product operations are defined. This article is (Part 1). This article mainly discusses four aspects: user data stock, increment, retention and channel quality measurement.

1. User Data

1.1. Inventory

The existing data is generally counted using DAU (Daily Active User) or MAU (monthly active users), usually daily active users and monthly active users, as well as some other common indicators such as pcu, dnu, wau, acu, etc. When defining DAU and MAU, they are defined based on key user spontaneous behaviors, that is, active. The active indicators are different in different businesses.

I would like to emphasize here that it must be defined by the user's spontaneous behavior. Common ones include logging in, making a request, reading an article, watching a video, and even placing an order as an active action. Any of these will do as long as it is a spontaneous action by the user. Some people define push as daily active users. This definition is generally meaningless because it is not a spontaneous behavior of users.

The calculation method of DAU is generally not prone to errors. You only need to calculate the data for the day. If it is a global business across time zones, the definition of indicators generally uses 24 hours instead of the concept of day.

Some ways of calculating MAU may lead to misunderstandings. For example, if there are 100,000 registered users and 3,000 DAU, the calculated MAU is 3,000*30=9,000. This method of calculation is not wrong. The correct method of calculation is to add up the daily active users of this month and remove duplicates. Similarly, the calculation method of WAU is the same. Remember that all cumulative active users must be removed by removing duplicates.

1.2. Increment

Incremental users refer to the users who are newly added within a certain period of time. The controversy about incremental users usually exists in the new indicators, that is, the number of newly added registered users or the number of newly added devices.

Generally speaking, if you do not have an account system, you can only recognize the number of newly added devices. If you have an account system, and it is not helpful for the business if the user does not log in, then you can log in to the account. If it is helpful, then you can log in to the number of devices.

Users who have not logged in for a long time cannot be counted as new users and can only be counted as woken-up users.

This is generally used for internal additions. There may be another algorithm for external channel additions. It is best to confirm the new indicators with the channel, otherwise there will be serious disputes later.

In the eyes of the channel, downloading is considered a new addition, regardless of whether the user has installed it or deleted it after installation. Therefore, you must define indicators with the channel in the early stage, otherwise the channel’s business will be really annoying.

If your company is wealthy enough, you can define the new additions to the registration. If you only look for channels occasionally and are still hesitant, then wait for the channel-customized additions.

1.3 Retention

Retention data is generally calculated using X-day retention. In most cases, 7-day retention is used to measure user acceptance of the product or the quality of the channel.

The concept of first-day retention is also introduced in some products. In fact, first-day retention and second-day retention are the same concepts. In first-day retention, the starting date is calculated from day 0, and in second-day retention, the starting date is calculated from day 1.

First-day retention is suitable for products with obvious periodicity. For example, the programmer product I came into contact with recently is only used from Monday to Friday most of the time. The product usage rate on Saturdays and Sundays is particularly low. This is the time when it is particularly suitable to introduce the first-day retention calculation process.

The advantage of doing this is that the first day is day0, and the seventh day is actually day7, which is 8 days, and is the same day in the dimension of the week.

Products that experience significant ups and downs within a week and the impact of different days of the week are excluded.

The method for calculating retention on the Xth day is simply to use Xth day/first day*100%. This method is generally used to measure channel quality.

The calculation method for retention within X days is (second day +... + Xth day) deduplication/first day * 100%. This is generally used to measure product popularity.

Whether you use the first-day retention calculation or the second-day retention calculation, as long as there is consensus internally and with the channel parties, just explain it clearly.

The word count is almost enough, I will describe the behavioral data and business data later.

Author: Growth Hacker Yibenhei

Source: Growth Hacker ReadMeditation