Conversion Analysis

Definition

The conversion funnel model analyzes the steps required to reach an outcome by outlining each stage of your users’ mindset.

The essence is to facilitate the circulation of enterprise core business and maximize the conversion rate of each marketing funnel.

Ideally, users will follow the path designed in the product to achieve the ultimate goal. But the reality is that the users' behavior paths are diverse. By configuring key business paths through event tracking, you can analyze conversion and churn rates in various business scenarios. Not only can you locate the potential problems of products, but also know in which link you have lost users, facilitating you to make targeted marketing strategies to promote conversion.

Application

For example, how much is the conversion rate for each process: searching for products -> browsing products -> placing orders for products -> making payment?

Which channel has a higher registration conversion rate?

Which customer service personnel has the highest conversion rate for placing orders?

Expected Effect

For details on the dashboard, see Conversion Analysis.

Implementation Ideas

Calculation formula: Conversion rate = Number of users in the later stage / Number of users in the earlier 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 carry out the calculation on the dashboard.

Sample data:Conversion Analysis.xlsx

Upload the example data to FineBI.

Creating a Self-service Dataset

Create an analysis subject with the built-in dataset "E-commerce_conversion_analysis" in FineBI, as shown in the following figure:

Counting Users in Different Behavior Stages

Choose + > Group Summary, drag the behavioral stage into the group and total bars, and select Count in the drop-down list of Total bar as shown in the figure below:

Sorting

Add Summary Column and rank Behavioral stage1 in descending order, as shown below:

In this way, you rank all user behaviors in sequence.

Add Formula Column, name it Sorting Merge Column, enter the formula Sorting-1 for subsequent join operations, click OK, name the self-service dataset table Conversion Rate Dataset - Preparation, and save it, as shown in the following figure: 

Joining

Create a self-service dataset and select the self-service dataset created in "Sorting". Click Field Settings and check the fields except for Sorting, as shown in the following figure:

Add Join, select the self-service dataset created in section "Sorting", check Sorting and Behavioral stage1, click OK, as shown in the figure below:

Select the merge mode as Full Join, and set the merge basis as Sorting Merge Column and Sorting, as shown in the following figure:

Name the self-service dataset as Conversion Funnel Data and save it.

Now you have got the conversion data at different stages and can obtain conversion rates at different stages by just dividing Behavioral Stage 1 by Conversion Rate Dataset - Preparation - Behavior Stage 1 on the dashboard.

Creating a Calculation Indicator

Click Component, click to add a calculation indicator, select Conversion Funnel Data, and enter the formula Behavioral stage 1 / Conversion Dataset - Preparation - Stage 1.

Name the indicator Conversion Rate, and click OK, as shown in the figure below:

Creating a Funnel Chart

Choose the chart type as funnel chart, drag Behavioral stage into Color (you can also customize the color), and set the filter condition to Is not empty, click OK, as shown in the following image:

Drag Behavioral stage 1 into Size, and rename it Number. Drag Behavioral stage, Conversion Rate, and Behavioral Stage 1 into Label. Rename Behavioral stage as Final behavior stage and set the color and font. Drag Behavioral stage into Fine-grained and sort in descending order by Number, as shown in the following figure:

Effect Display

For details, see "Expected Effect" in this article.

Through the funnel chart, it can be seen that the conversion rate of users from the stage of browsing products to the stage of adding products into carts is 51.22%, reflecting that the product introduction, pictures, and product description are attractive to users.

The conversion rate from users who add products to carts to users who place orders is 99.66%, which is high.

However, the conversion rate at the payment stage is only 50.34%, which is worth reflecting on. It is speculated that the payment channels of this store on this platform are not perfect, and maybe it is necessary to add convenient payment channels such as Alipay and WeChat to reduce the probability of users giving up purchasing due to imperfect payment settings.