Conversion analysis

1.1 Concept

The conversion funnel model is a method for analyzing the conversion effect of a user through a series of steps when using a certain business.

The essence of conversion analysis is to promote the circulation of the company's core business and maximize the conversion rate of each marketing funnel.

Under ideal circumstances, users will follow the path of product design to reach the final target event, but the actual situation is that the path of user behavior is diverse. By configuring key business paths through burying events, it is possible to analyze the conversion and loss of various business scenarios, not only to find out the location of potential product problems, but also to locate lost users in each link, and then targeted marketing to promote conversion.

1.2 Problems solved

For example, search for products -> browse products -> place an order -> transaction payment. What is the conversion rate of each process?

The two promotion channels bring different users, which channel has the highest registration conversion rate?

Which customer service orders have the best conversion conditions?

1.3 Expected effect

1.4 Implementation ideas

Calculation formula: Conversion rate = number of users in the next stage/number of users in the previous stage

For example, payment conversion rate = number of payments/number of orders

Use the self-service dataset to count the number of users at different stages and calculate it on the dashboard.

2. Example

Sample data:

367.E-commerce transformation analysis.xlsx

Upload data to FineBI.

2.1 Create a self-service dataset

Enter the corresponding business package under Data Preparation, click "Add Table> Self-Service Dataset", add "E-commerce transfromation analysis" and select all fields, as shown in the following figure:

2.2 Counting the number of users at different behavior stages

Click "+ > Group Summary", drag the "Behavior stage" into the grouping and summary columns respectively, and select "Records count" in the summary column, as shown in the figure below:

2.3 Sort

Add the "Sort" function to sort the "Behavior stage1" in descending order, as shown in the figure below:

Add the "New column" function to rank the "Behavior stage1" in descending order, name it "Sort", and click "OK", as shown in the following figure:

Add the "New column" function and name it "Rank merge column", enter the formula: Sort -1 to merge left and right in the future, click "OK", name the self-service dataset "Conversion dataset-Preparation" and save, as shown in the figure below:

2.4 Merge left and right

Create a new self-service dataset, and select the self-service dataset created in section 2.3, and check other fields except "Sort", as shown in the following figure:

Add "Left and right merge", select the self-service dataset created in Section 2.3, check the merge fields as "Sort" and "Behavior stage1", and click "OK", as shown in the figure below:

Choose the merge method as "Union combine", and the merge basis is "Rank merge column" and "Sort", as shown in the figure below:

Name the self-service dataset "Conversion funnel data" and save it.

2.5 Create a calculated field

Create a dashboard, click "OK", select "Conversion funnel data", and click "OK", as shown in the figure below:

Click "+" to add a calculation indicator, name the field "Conversion rate", enter the formula: Behavior stage1 / Behavior stage11, and click "OK", as shown in the figure below:

2.6 Create a funnel chart

Select the chart type as the funnel chart, drag the "Behavior stage" dimension field into the "color", you can also customize the color of the behavior stage, and set the filter condition to "not empty", click "OK", as shown below Shown:

Drag "Behavior stage 1" into the size column and change the name to "Number of people", drag "Behavior stage", "Conversion rate", and "Behavior stage1" into the tag bar, and change the name of "behavior stage" to " "Final behavior stage", set the color and font, etc. Drag the "Behavior stage" into fine-grained, and arrange them in descending order of "Number of people", as shown in the figure below:

2.7 Effect display

See section 1.3 of this article for details.

3. Conclusion analysis

• The first is the conversion of users from browsing product behaviors to adding shopping cart behaviors. Through the funnel chart, you can quickly see that the conversion rate is 51.22%, reflecting that the platform's product introductions and picture descriptions are strong for users. Appeal;

• Then the conversion rate from adding a shopping cart to placing an order, it can be seen that the conversion rate is as high as 99.66%;

• However, the conversion rate of payment is only 50.34%, which is a conversion node worthy of reflection. Based on data analysis, it is guessed that the payment channels of the platform's shops are not perfect. It is necessary to increase the fast payment channels such as Alipay and WeChat, and reduce the platform because it does not provide user habituation. The probability of users abandoning the purchase behavior due to the payment channels.