How to use funnel analysis correctly to improve conversion?

How to use funnel analysis correctly to improve conversion?
Simple funnel tools can only tell you at which step the user is lost. You don’t know what behavioral paths there are, what percentage of users follow each path, why users churn , or how to improve them.

So how can we improve conversion rates through funnel tools?

GrowingIO Skill Card Issue 3, how to use real funnel tools to improve conversion rates. 1. Sort out business needs and determine the core conversion path;

2. Conduct in-depth analysis of the causes of customer loss from multiple dimensions and optimize iterations.

01
Identify conversion paths
The first step in unpacking the conversion black box is to identify the user’s conversion path within the product. The reality is that users may have many conversion paths within the product, so it is necessary to determine the core conversion path based on business needs. There are generally two types of user conversion paths:
1. Clear and single conversion path Some conversion paths are relatively clear and single, such as registration flow, payment process of e-commerce products, etc. For this conversion path, you only need to select the corresponding conversion steps in the funnel tool.

Supports 10 conversion steps

When you hover your mouse over an event, you can also see the event definition and data from the past 7 days.
For different users, the conversion goals are also different. You can choose specific target users according to your needs.
2. Other paths Not all user conversion paths are so clear and single. The user's behavior trajectory within the product is often beyond the expectations of the product/ operation . In real visits, users often have many loops and frequent interactive operations (for example, frequent clicks).

In this situation, you need a powerful "smart path" function to filter out invalid loops and frequent clicks, leaving only key nodes. Simply select the final conversion goal to display all conversion paths and their corresponding proportions.

This function truly restores the key nodes of users in the product, changing the era driven by experience and intuition of first presetting results - setting points - collecting statistical data - analyzing data ; to the data-driven era of viewing data and paths according to analysis needs - analyzing data - and further optimizing products.
You can select the conversion path you want to further analyze or the one with the highest percentage, save it as a funnel, and analyze it directly.
02
Analysis of the causes of loss
Users may churn at every point in the conversion process, but what are the reasons for the churn? Can we use a powerful funnel tool to perform drill-down analysis to find problems and optimization directions? The answer is yes

1. Dimensional comparative analysis There are many reasons that affect conversion, which may be the adaptability of different browsers , the impact of operational activities in various regions, or the different quality of users coming from different channels . We need to conduct comparative analysis of dimensions to find the reasons for user loss in order to find the direction of optimization.

A truly powerful funnel supports multi-dimensional drill-down comparisons:

You can click on all the options under different dimensions to make different comparisons
To give a real example, when an e-commerce website used the GrowingIO funnel to measure transaction conversions, it was found that the number of users on the App was higher than that on the website, but the conversion rate was lower than that on the web page.

From the specific steps, it can be seen that the conversion rate from user submission to payment is significantly lower than that on the web page. Users who submitted orders had a strong desire to buy, but they chose to return to the previous step instead of paying. By comparing the information structure of the payment pages on the website and the App, we found that the payment page on the App lacks detailed descriptions of the ordered goods, recipient address, contact information, etc., causing many users to return to the previous step for confirmation. This also makes users hesitate, resulting in a decrease in conversion rate. Therefore, the product manager referred to the information structure of the website, supplemented the detailed information, and recalled the lost users during the payment process.

The effect of optimizing the payment link can be monitored from the trend chart of the funnel. The conversion rate from order submission on the App to payment has increased significantly, even slightly higher than the website conversion rate, and the overall conversion rate has also been raised. At the same time, select the users to be recalled in the funnel as the target users, and observe the changes in conversion rate after the recall to evaluate the effectiveness of this recall activity.
Conversion problems like these are difficult to discover based on intuition alone; they require product or operations personnel to have a high degree of data sensitivity and sophisticated business skills. This is also a manifestation of the advanced stage of conversion analysis. After discovering the problem, product optimization is carried out, and then the optimization effect is monitored back in the funnel. The product grows steadily through continuous iterations.
2. In addition to the segmentation comparison in different dimensions, user group comparison analysis can also perform more advanced operations.

Comparative analysis of users from different groups can be done not only based on dimensions, but also based on different user behaviors . For example, users who have clicked on function A twice within 30 days, users who have posted comments within 7 days, etc., can create a comparison funnel based on this dimension and drill down to more detailed areas.

This feature can be used to do A/B testing very well:

For example, an e-commerce platform tried to wake up recently inactive users and divided the users into two groups, one of which was given a discount coupon and the other a discount coupon. Want to know which coupon converts better?

At this time, you can create two user groups: "Users who received coupons for purchases above a certain amount" (2010 people) and "Users who received coupons for instant discounts" (1080 people). When comparing funnel users, select these two user groups and adjust the time range to a period of time after the users received the coupons. Then, you can compare the conversion rates of the two different user groups.

In this era of competing tools and efficiency, you deserve better and smarter data analysis tools to help improve conversions.

The author of this article @GrowingIO compiled and published by (Qinggua Media). Please indicate the author information and source when reprinting!

Product promotion services: APP promotion services Advertising

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