What does a data operation involve?

When we get a product online, it’s time to bring it to market and users. There is a lot of data generated in mass promotion, and by running this data we can get feedback to iterate and optimize the product.

How does data operation actually do? Especially with simple design thinking, how do you do that? Data operation consists of four parts, as follows.

▲ The working dimension of data operation

Traditional data operations include data collection, data collection, data analysis, and data recommendations. Data collection and data collection should actually be divided into two parts

Data collection mainly refers to systematic data acquisition, such as data collection through burying points, logs, crawlers, and preset rules. Compared with data collection, data collection also includes data demand collection and manual acquisition.

▲ Data collection and data collection relationship

Manual acquisition a collection of existing data to be collected in the future in order to achieve a goal or solve a requirement.

For example, in the e-commerce operation, the operation needs to see the number of orders and revenue under the operation activities, and the data collection demand for a single activity is different from the daily data collection. Time and date need to be selected separately.

Just need to: 1.

So we divided the data into two categories

They are product function data and business data respectively. Collectively called the core data under just need.

2. Easy to understand:

Around rigid demand, we define each indicator as simple and easy to calculate. The core data under its product functions are

Page views

The total number of times a page has been viewed, such as the total number of times a site or page has been viewed.

Unique visitors

UV a browser cookie indicates that all can be reprocessed according to the number of statistics

Number of unique page visits

UPV refers to the unique number of times a page is accessed. For example, the page is accessed multiple times within a visit. The UPV only records the page once

Jump out of the number of

Bounces, the number of times a page is visited after entering a site, with no subsequent visits

Leave the number

Also known as push counts, the number of times a visitor enters the site from the current page and does not follow up is recorded as a bounce.

Average depth of access

Average number of pages viewed per visit, page views/visits

Average page duration

Average time on page, the average visit duration of each page, total stay time of the site/total page views of the site.

Valid number of visits

Valid times = number of visits – number of jumps

Dropout rates

Quit times/visits *100%

Bounce rate

Number of jumps/visits *100%

New users

Users who start applications for the first time in history need to be de-duplicated by device ID

Start times

Number of times a user opens an application in a specified period. “One startup” means that the user starts the APP and exits the APP. Multiple pages may be viewed during a single startup

Daily weekly monthly annual active users

The number of users who have started the application in a specified time range needs to be de-duplicated based on the device id. Active users refer to the number of active users defined by the platform

active

Active Users/Total Users x 100%

Retained users

Users who are added in a specified period (T1) and still use the program after a period (T2), where T2-T1 is a period of time

Next-day retention

Based on the number of retained users added one day before the statistical date)/(Number of retained users added one day before the statistical date x 100%), the 7-day and 15-day retention rates are also calculated

New registered users

The number of users registered through the application

Single use duration

The duration of a user’s application is divided into average duration and single duration. The average duration is the average of all access duration of all users in a certain period of time.

Average service life

Daily usage duration of all users/total active users; It also has the total number of users divided by the total number of users

Using interval

Interval between two applications started by the same user.

Average depth of access

We consider the user’s depth of access as the cumulative number of pages a user reaches in a single app launch.

3. Service data indicators

Business metrics will relate to money, and of course there will be data on business attributes. For example, PMTalk has signed authors, content number, membership number, activity registration number, which are determined by the company’s business scope.

Is common business data needed in any industry or business

Membership increase rate

Number of new members/Total number of members *100%

Membership repurchase rate

Number of successful member repurchase orders/total number of members

Membership turnover rate

Number of members not renewed within a certain period of time after expiration/Total number of members *100%

Member retention

1- Membership turnover rate

Active member

Member login to use products in line with active users/total members

Percentage of SKu sales

Quantity sold per item/Total quantity sold *100%

Sales conversion index

Single product order quantity/total number of users *100%

Introduce order conversion

Introduce amplitude order 2 volume /UV *100%

Introduce order amount conversion

Import order amount /UV*100%

UV value

Amount after valid order discount /UV

The cost of data acquisition is also extremely low. There is no need for complex development techniques and hardware requirements, as long as the developer buried the previous calculation rules based on the above indicators can output this kind of data.

Data analysis method

Through data collection and data collection, we then start to analyze the data, and provide effective decision-making suggestions through the rules and results reflected by the data.

Here are seven analysis methods

1. Traffic marks UTM

It is used in advertising and external promotion scenarios.

UTM has five parameters, three mandatory and two optional, as follows:

Utm_source, UTM_medium, and UTM_campaign are mandatory parameters, while UTM_TERM and UTM_Content are optional parameters.

Source: Tag search engines and other sources

Medium: indicates the specific media, such as email or click

Utm: User-paid search, display advertising keywords

2. Multi-dimensional analysis

Combined with time cycle, region, different channels to screen users to refine the problem. Comparing a single indicator is meaningless and requires multiple dimensions

3. Transform funnel

Mainly according to AARRR model, by observing the user’s entry to exit transformation process. Analyze the key problems of loss analysis in transformation process

4. Retention curve

With the growth of user volume, we continue to find the transformation and growth path with the best cost performance for different types of users, and then apply guidance and incentives. Analyze accumulated retention numbers

5. User group/user portrait

Comb user labels based on SIKT model. Based on the user scenario, achieved indicators, user actions, labels to build user profile data of user region, age, preferences. For example, in the user research stage, the product manager understands the commonness and differences of users through questionnaires and customer interviews, and aggregates different virtual users. Convenient user segmentation, precision marketing

▲ SIKT model under user portrait

6. Heat map

The thermal map is presented in the form of a thermal map by recording users’ clicking and browsing behaviors on live apps on websites. A thermal map is a visual display of users’ behaviors. Thermal map helps product design to deeply analyze users’ access to content and functions, operation habits and behavioral preferences through visualization. Understand user product access preferences

7. A/B testing

Real ABTest our purpose is to focus on two

The first is to decide which is better:

For example, two different product interaction design representations for the same function. Whether A or B is better, if resources allow, we need to make an experimental judgment.

The second is quantification of revenue data:

For example, whether the order transfer and direct cash income involved in the function are determined by AB test.

The above data operation method is broken down into four dimensions based on the four features of my new book, Simple Design.

  • Just need: Core data acquisition

  • Easy to understand: Data reading comprehension is low, but reflects business results

  • Efficient: Low data collection threshold and simple data generation rules

  • Low cost: low data acquisition cost and easy data operation

With time to write, my third book, Simple Design, is coming out soon. Simple Design This book is based on the case of start-up companies, introducing the thinking skills of Internet product design and company operation

Tough, easy to understand, efficient, low cost

Simple design methods have been proven repeatedly in my entrepreneurial process, and can be the cornerstone of product design, product development, and company operations.