User operations in Internet finance: How to promote high orders and high conversions?

I. Four Behavioral Characteristics of Internet Finance Users

Internet financial platform users have four major behavioral characteristics:

1. Low traffic conversion rate

The following figure shows the overall purchase conversion funnel of new customers on the website of an Internet financial company in the past 30 days. Its conversion rate is only 0.38%:

This is not an isolated case. In fact, for most Internet financial companies, the conversion rate of web purchases is basically below 1%, and the APP purchase rate is around 5%, which is much lower than the purchase rate of e-commerce or other online transactions.

2. Although the conversion rate is low, the average order value is high

Generally speaking, the average customer spending in the e-commerce industry is between tens and hundreds, while for Internet finance clients, the average customer spending ranges from thousands to tens of thousands, and even up to hundreds of thousands in some special areas. A high average order value means that users’ purchasing decisions will be more complicated and the purchasing cycle will be longer.

3. User purchasing behavior is highly cyclical

The next purchase time of e-commerce customers is uncertain, but on Internet financial platforms, the users who actually make purchases are those who have financial management needs. After the funds mature and the products are redeemed, they will definitely make the next purchase, but it may not happen on your platform.

The following figure is a typical interactive trend chart of Internet finance users:

It can be seen that every once in a while, this user will have a concentrated and large amount of interactive behavior. After the user completes the purchase, the user's interactive behavior becomes rare again. He may occasionally check the product's yield, but the overall interaction index will not be too high until his next purchase. The cycle of this user's financial management needs is about one month.

4. Purchasing behavior is highly characteristic

This mainly includes two characteristics:

(1) Users’ purchasing preferences are relatively easy to identify. The number and categories of financial products are small, so users’ purchasing needs or preferences can be easily identified from their behavioral data.

(2) Three stages in the user purchasing process are particularly easy to identify.

1. During the purchase decision stage, users have a large number of interactive events. They will look at products, compare the yields and risks of different products, compare the investment periods of different products, etc.
2. But once he completes the purchase of the product, there will not be a lot of interactive behavior, he may just come back to check the product's rate of return.
3. After the user redeems the product funds, a large number of interactive events occur. In fact, he is in the decision-making period for purchasing the next product.

2. Three major steps in Internet financial user operations

In view of the four characteristics of Internet financial user behavior, there are three relatively important phased tasks in user operations :

1. First, acquire target users who may purchase, and reasonably allocate the advertising budget on the channel to increase the proportion of high-quality user acquisition;

2. Next, we need to identify high-value users – those who really have purchasing needs and are willing to pay and purchase;

3. Adopt targeted operation strategies to improve the conversion rate of high-value users.

3. Get target users

The core of channel work is to do two things well:

• At the macro level, optimize the configuration of the entire channel;
• At the micro level, from the perspective of a single channel, targeted implementation and adjustments should be made based on the channel configuration strategy.

1. Optimize channel configuration

Everyone is familiar with the implementation of specific channels, but many people do not have much exposure to the optimization of the entire channel portfolio configuration. This picture is the overall conversion funnel, which can be compared from different dimensions. For example, we first select the top 10 channels in terms of traffic:

Taking channel one as an example, the overall conversion rate is 0.02%; the total traffic on the site in the past 30 days is 18.9K, and the conversion rate from the first level to the second level of the funnel is 3.36%, which is a total of five levels. We can see that the total number of users who completed transactions through channel one is 4.

Similarly, the data for the top 10 channels is very clear. The traffic brought by different channels, the overall conversion rate of different channels, and the conversion rate of each step of the entire conversion path of different channels can all be seen.

2. Targeted operation channels

There are several special channels here:

1. The characteristics of channel 1 are that the traffic brought by channel 1 is the largest among all 10 channels, but its overall conversion rate is low;
2. Channel 2 and Channel 7, the volume of Channel 2 is large, but the conversion rate is zero. Channel 7 volume is average, and the conversion rate is zero;
3. Channel nine and Channel ten, these two channels have the highest conversion rates among all channels. However, the characteristics of these two channels are that they do not bring a lot of traffic...

Combining the characteristics of typical channels, we can make a quadrant diagram:

The first quadrant (upper right corner) has high-quality channels and brings in large amounts of traffic. Channels three, four, and five meet this characteristic. The channel strategy should be to continue to maintain and increase channel investment.

The quality of the channels in the second quadrant (upper left corner) is relatively high, but the traffic they bring is relatively small. The main channels included here are eighty, ninety, and so on. The corresponding main strategy is to increase channel investment, and in the process of increasing investment, continue to pay attention to changes in channel quality. I

Let’s first look at the fourth quadrant (lower right corner). The channel quality is relatively poor, but it brings in relatively large traffic. It mainly includes Channel 1 and Channel 2. The corresponding channel strategy should make more precise placements in the channel to improve the quality of the entire channel.

The third quadrant (lower left corner) has poor channel quality and brings in small traffic, such as channel six and channel seven. Should we just cut it off? The suggestion here is to be more cautious in strategy. Therefore, in terms of specific channel strategies, we should keep monitoring the performance and then make small adjustments.

According to the results of the above data analysis , after channel optimization, it will bring us more high-quality users.

4. Find high-value users

The premise for investing resources and energy in users who are truly likely to purchase is that we must be able to identify which users are truly valuable. Who are the low-value users?

1. User’s core behavior

In fact, for Internet financial platforms, and even all platforms that include online transactions, users' purchasing intentions can be identified from their behavioral data. Due to the particularity of Internet financial platforms, compared with e-commerce platforms, there are fewer product categories and simpler platform functions, so user behavior data can better reflect the purchasing intention of users on Internet financial platforms.

Summarizing all the behaviors of users on the platform, there are actually not many core behaviors, including:

When a user views a product list page, it indicates that he or she has some purchasing intention, and when a user clicks on a product, it indicates that the user wishes to learn more about it. The user finally confirms the payment and completes the purchase. The purchase process is then completed and his financial needs have been met. Each behavior indicates a different degree of user willingness to buy, so it is very important to obtain user behavior data in the product.

2. Find high-value users through quantitative analysis

Since user behavior data is so important, how do we obtain it? We collect all user behavior data in a seamless way, assign different weight coefficients based on our business needs, and further segment users into groups based on the strength of their purchasing intention.

This is an example of a user purchase intention indicator created by one of our clients. The first five behaviors are typical behaviors of users before purchasing:

The weight coefficient of each typical event is different, and the user's willingness to buy is getting stronger and stronger: when the user clicks the investment button or even the submit button, his willingness is obviously stronger than just looking at the product list page, or just looking at the product page or detail page. The more an event can reflect the user's purchasing intention, the greater the weight you should give it. This is a general principle. 0.05 or 0.06 will not have much impact, so don't worry about it.

In this way, we can score the user's purchase intention according to all the behaviors of each user, and finally form an indicator of the user's purchase intention.

This is the situation where we cut off the purchase intention scores of some users from high to low. The first column is the ID of each user, and the second column is the score given to each user according to the purchase intention. Users with high scores are those with the strongest purchasing intention. After obtaining the purchasing intention of all users, we can divide all users into different groups according to the strength of their purchasing intention and carry out targeted operations.

This is to find out the users with scores greater than 5 based on all the behavioral data generated by the users in the past 14 days according to the weight of the purchase intention score. Among the total users, this part of users ranks in the top 20% in terms of purchase intention. We give them a name, called users with strong purchase intention.

Similarly, we have also segmented users with medium purchasing intention, which are users whose purchasing intention ranks between 20-60%; users whose purchasing intention ranks in the bottom 40% are the user segment with the weakest purchasing intention.

After grouping, click on any group and it will be listed in the form of user ID. Because you need the user's ID to impose operational strategies on these users. You can see the number of visits, most recent visit location, and last visit time of each user in the last 30 days.

Next, how do we promote user conversion for these users with strong purchasing intention?

5. Improve the conversion rate of high-value users

1. Users with different product preferences

First, let's look at the purchasing preferences. The categories of goods on Internet financial platforms are relatively few, and the purpose of users' purchases is relatively clear. Generally, there are several categories of goods:

1. Bond-type financial products;
2. Stock-based financial products;
3. Monetary financial products;
4. Index-based financial products;
5. Hybrid financial products.

If we calculate the proportion of users' visit time on products in different categories, we can better understand users' purchasing preferences. For example, in the figure below, we divide the length of time users spend visiting the bond product details page by the total length of time users spend on the site to get an indicator of the percentage of time users spend visiting bond products.

We still use user segmentation tools to separate users whose visit time on bond-type products accounts for more than 40%. These are customers with very strong characteristics, and their purchasing preference is bond-type products. At the same time, we set another indicator, such as the user purchase intention indicator. Previously, we have done it to be greater than 5, which means that the purchase intention ranks in the top 20%.

Through these two conditions, we can find out the users who prefer bond products and have a strong willingness to buy. The relationship between these two indicators is an AND relationship. Similarly, we can group users who are interested in other categories into different user groups based on their purchasing preferences, and then form user groups with different purchasing preferences.

For these users, in fact, in terms of operational strategy, we can carry out operations from three levels:

2. Users in different life cycles

From the perspective of the purchasing stage, first we can divide all users into new customers and old customers. For these two groups, the operational strategies and priorities are very different.

• The new customer group consists of users who have never made a purchase on the platform. We need to perform further operations based on the users’ purchasing intentions.
• For the old customer group, that is, users who have already purchased products on the platform, in addition to paying attention to the user's purchasing intention, the user's fund status (whether the funds have been redeemed) is also a very important parameter.

Did the user purchase the product? Have users who purchased the product redeemed their funds? These two contents are actually the current attributes of a user.

In our grouping work, there is a dimension menu. Through this dimension menu, we can find users with certain attributes:

I have made a grouping here, let’s take a look. In the dimension menu, we set the dimension value of whether the product has been purchased to 1. The value of whether the funds have been redeemed is also set to 1. In fact, we are finding out the old users whose funds have been redeemed. Similarly, in the indicator menu, we also find out the users who have a strong desire to buy, the time is the past 14 days, and the indicator is greater than 5.

In this way, we have created a user group, and all users in this user group must meet the following three characteristics:

• Feature 1: Old customers who have purchased the product.
• Feature 2: Their funds have been redeemed.
• Feature 3: Behavioral data from the past 14 days shows that this user has a strong desire to buy.

Similarly, we organize all users into the following categories, corresponding to different operation strategies:

For example, among new customers, those who currently have the intention to buy are actually new users in the purchase decision-making period. This kind of relatively high-quality financial product should be recommended based on the user's purchasing preferences. And give certain purchase incentives to promote these new customers' first purchase on the platform, which is very important for new customers, and so on.

Compared with e-commerce or other industries, Internet financial platforms combine the characteristics of the industry and users, drive product business and improve user conversion rates from the perspective of user behavior data analysis, which is more important.

Mobile application product promotion service: APP promotion service Qinggua Media advertising

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