Product operation: new user retention and conversion strategy!

Data shows that most application products, if they do not have an independent conversion and retention strategy system designed for new users, are likely to lose 75% of their new users within three days.

If there is no refined retention strategy for new users, it will not only affect the retention of new users, but also restrict all aspects of product growth:

1. It is not possible to clearly understand whether a certain channel is a high-quality channel for a product, and whether there is room for optimization;
2. It may also affect the overall investment in the new model, as there is a lack of the most powerful data to support and prove the budget allocation ratio in each direction of the new model;
3. It may also affect the overall retention of the product.

Therefore, new users need a set of exclusive and independent conversion and retention strategy systems.

For new user retention monitoring, we have many data indicators, such as: the next-day retention rate of new users, the seven-day retention rate, and the next-month retention rate of new users. We may also need to monitor the payment conversion rate of new users (mainly membership system and e-commerce platform products).

However, do not use the monitoring indicator (retention rate) as the goal of the new user retention strategy, otherwise it is easy to fall into the KPI misunderstanding.

Suppose we set the seven-day retention rate of new users as our goal. In order to improve this target value, all strategies are not as effective as one strategy, which is to push new users. Keep sending push notifications to new users to ensure that they log in actively within seven days or one month, and you may even end up completing the target value by sending text messages.

The result of doing this will definitely improve the target value, but it will ultimately harm the overall retention of the product. You will find that the number of users that can be covered by push is getting smaller and smaller, and the contribution to the daily active users of the product is also getting smaller and smaller.

Therefore, when retaining new users, you cannot directly use monitoring indicators as your goal, but instead need to find behavioral data indicators that are positively correlated with the monitoring indicators. Different products will have different positively correlated behaviors. In most cases, there will be multiple positively correlated behaviors. We need to conduct continuous testing and rapid review before we can make judgments. If we analyze purely from a theoretical and methodological perspective, and analyze the product's existing active user behavior data, we will often fall into a "pit".

Next, let’s talk about the pitfalls we’ve encountered in improving new user retention, and effective improvement ideas that are suitable for most products.

Part 1. Pitfalls in Improving New User Retention

The A (Activation) in the AARRR model in "Growth Hacker" refers to new user activation. By guiding users to complete the key behavior of the AHA moment, they can realize the value of the product and thus convert new users into loyal users.

According to this idea, the following points are broken down:

1. Find the AHA moment that your product creates for its users.
2. Work backwards from the AHA moment to the key behaviors.
3. Validate the derivation to see if repeated key behaviors allow users to experience the product value.

If the key behavior is found through the AHA moment and the data verifies that repeated key behavior has a positive growth relationship with retention, then this key behavior is the behavior that we need to push new users to continuously achieve, which is the direction to improve new user retention.

Based on this theoretical analysis, we developed a strategy for retaining new users for Taobao’s Weitao product.

So, we encountered the first big pitfall in retaining new users.

Pitfall 1: Using key behaviors as guidance goals may be an ineffective strategy

Weitao is a product function of Taobao Mobile, which aims to establish attention relationships between users, stores and influencers, with products and treasures as content. The form of the product is similar to WeChat’s Moments. Only with more attention can we see more and richer content, thus realizing the consumption conversion of product content.

【Derivation of key behaviors】：

The value that Weitao products bring to users is that by following them, they can obtain products that match their tastes, tonality, prices, and other characteristics from the content shared by the people they follow. This is a supplement to the search function of e-commerce products and is conducive to the exposure of products in more dimensions.

The moment when a user obtains a satisfactory product is his/her AHA moment. Only when users follow more stores and influencers can they get more satisfactory products, so the "follow" behavior becomes a key behavior before users get the AHA moment to experience the product value.

【Data verification of key behaviors】:

Through the activity data of the entire product, we also found that the more active the users, the more attention they receive. It seems that this is a data indicator that has the function of a North Star indicator. Therefore, we set "follow" as the guiding behavior for new users, and increasing the number of follows as the retention strategy for new users, and the number of follows of a single new user becomes the goal of the strategy.

At the same time, according to other successful cases for reference, such as Twitter and Weibo, these products also use attention as a strategic goal when improving new user retention (Twitter: when a user follows about 7 users, the product has relatively stable activity, and the possibility of user loss will decrease).

Therefore, following these successful cases, we developed a “one-click follow” function for new users when they log in - that is, through a certain recommendation logic, we put the recommended accounts into a list, and guide users to follow the accounts in the list with one click.

However, one quarter later, although the proportion of new users who followed the app after logging in increased from 9% to 20%, what puzzled us was that without any changes in the channel, the retention rate of new users in the next month decreased instead of increasing.

We can't help but wonder, why can Twitter and Weibo be successful with the same approach? Did we misjudge the key behavior? Why does the positive relationship between active users and followers not mean there is also a positive relationship between new user retention and followers?

Through re-analysis, we found the following problems:

1) Following is an active behavior of active users

Before product-guided attention is carried out, attention data is generated by users actively, which means that all attention is conscious, active and expected behavior of users.

Behind this behavior is the user's desire to receive dynamic information from those they follow, so they will be called back by product messages from these dynamic information. This is why the more active the user, the more followers they have, and the higher the retention rate.

2) Data comes from loyal users of the product

The results reflected in the data from mature users of the product cannot be used to make reverse inferences. If we look for commonalities among the active users who come every day, we can indeed see that most of the active users have a following range of 5-8. However, this does not mean that we can reversely infer that if we want users to become active users every day, we must get them to follow at least 5 users. This is illogical.

The logic behind loyal users’ attention behavior is: browse products (purchase) – recognize stores (experts) – follow – revisit – browse again (purchase).

The prerequisite for following is to recognize the product recommendations of the store (expert).

Therefore, we adjusted our strategy:

• Adjust the new user retention strategy of guiding attention to guide new users to browse content.
• First, based on a preliminary judgment of the new user’s information, matching content is provided to encourage the user to continue browsing, and then the recommendation algorithm is optimized in real time based on the data during the browsing process.
• During the browsing process of new users, we constantly and cleverly guide users to pay attention to the content producers.
• The goal of retaining new users has been adjusted from the number of user followers to the conversion rate of new users’ browsing to following.

Finally, after adjustments and continuous optimization of strategies, in the second quarter, the next-month retention rate of new users of the Weitao product function increased by 12%, and the overall attention increased by 30%.

Pitfall #2: Some products cannot immediately give new users an AHA moment, let alone product value.

We all agree on a theory that after new users experience the value of a product, they will become retained users. Therefore, in our new user retention strategy, we will try our best to get users to complete certain actions as soon as possible so that they can experience the value of the product as soon as possible.

For example: For Xianyu, the product value is to help users complete transactions of second-hand goods. In order to allow users to experience as quickly as possible that "their second-hand goods are actually sold", it is necessary to guide users to publish goods.

The data shows that the retention rate of users who sell items online exceeds the overall retention rate, and the next-month retention rate of new users who post items far exceeds the next-month retention rate of overall new users.

Therefore, "posting behavior" was once Xianyu's only strategy to retain new users.

In this direction, we have done many different types of guidance, such as:

1) New users may not know what to sell

solve:

• Through the data of purchased products, users can be told what they can sell.
• What users on the platform want to buy the most, what is the most popular and sells the fastest, will make more money.

2) New users may find publishing troublesome

Solution: Image recognition, the system automatically generates copy.

3) New users lack motivation to publish products

Solution: Cash is used to encourage users to post, and users can earn xx yuan by posting.

These guidance strategies do not only appear in the form of poppers or focus pictures, but also appear in every step of the new user's product browsing, just like the road signs that can be seen everywhere on the roadside, appearing repeatedly in various forms on the user's behavior path.

However, it ultimately did not lead to an increase in new user retention, and the new user penetration rate of publishing behavior could not be improved. This was the second pitfall I encountered in new user retention.

Where exactly is the problem? Is this the wrong direction? Or is the data deceiving us again?

Through review and analysis, we adjusted our strategy and conducted an A/B test. Finally, the retention rate of new users has increased significantly compared to before.

The reason is that it is impossible for new users to perceive the value of the product at the first time.

The product value of Xianyu is to bring second-hand goods transactions to users. Among new users, the proportion of users who have the demand to sell second-hand items does not exceed 30%, and all our new user strategies are aimed at allowing users to publish second-hand items so that they can perceive the value of the product.

Therefore, no matter how comprehensive and effective the new user strategy is, it can only trigger 30% of new users at most. The remaining 70% of new users do not mean that they cannot become sellers. On Xianyu, every user is both a seller and a buyer, and the path for them to become Xianyu sellers is not achievable when they are new users.

This is like the use of Tik Tok. Some people record short videos immediately after downloading it, while others record after using it for a while.

Therefore, if our new user strategy is still just focused on “release”, the remaining 70% of users may face loss.

After analyzing the reasons, the new user strategy was adjusted from "release" to "let new users connect with the product as soon as possible". We no longer "force the issue" and expect new users to feel the value of the product as soon as possible. Instead, we guided new users to browse products, follow expert sellers, or join different fish ponds, etc.

All these guiding behaviors are to encourage new users to leave more information in the product with a "low threshold", so that the product can get to know new users more comprehensively and quickly. As more and more bonds are maintained, the value of the product will eventually be felt naturally.

Part 2. Ideas for effectively improving new user retention

New user retention requires refined operations. The following is a shared idea:

1. Start with the source of new users, what you see is what you get, and make effective conversions as soon as possible

New User Identity Tag:

Suppose we choose to deliver information flow, and the target user group is "women aged 25-35 in Beijing", then the new users coming from these information flow channels will have labels of region, age range and gender. For these new users’ tags, combined with the product’s characteristics, we can provide content or activities that match the tags when the new users open the product for the first time.

【Product features that new users are interested in】:

Suppose we put material on a certain functional point of a product, and users click on the material to come to the product. We need to present the function or activity to these users as soon as possible through deep links.

For example, Douyin discovered that young users in first- and second-tier cities on the platform like to shoot the "finger dance". The "finger dance" video has become a material for acquiring customers outside the site. New users brought in by clicks on this material, the first video content they watch when they open Douyin is the "finger dance". This WYSIWYG format has brought a 20% increase in ROI for products.

[New users come with their own relationship chain]:

New users come to the product through sharing clicks between users. This inherent relationship chain and the user portrait of the sharer are the strategic basis for the product to convert new users.

2. The browsing time of new users without source is the key

For new users who are natural or whose sources cannot be distinguished, the original content provided comes from popularity, word of mouth, etc. Starting from the first browsing of the new user, the "footprint" information of the new user is used to continuously optimize the personalized content presented and increase the browsing time of the new user. The longer the time, the less likely a new user is to abandon your product.

3. Lead new users to connect with your product as soon as possible

The product value of many products cannot be perceived by users through an action at the first time, especially by new users. If these products can encourage new users to browse more when they first log in to the product, which is a low-threshold behavior, it will be more helpful for retaining new users.

Guiding new users to connect with your product does not mean getting them to register an account, leave a phone number, post content, or make a purchase. A more effective way is to provide the expected browsing content or functions, and then guide them to register, follow, post, or perform other actions during the browsing and experience process.

There are many ways to use guidance techniques, all of which require constant testing. For example, the attention icon becomes larger and flashes, and a "hot" icon is added at the function entrance. These simple changes can bring about behavioral improvements. In past cases, adding a "hot" icon at the function entrance can bring up to a 10% improvement in the function experience.

Therefore, the above is only an idea, but the execution of the approach requires constant testing and changes. The operations will naturally be different for different products and user attributes.

Related reading:

1. Product operation and promotion: How to compete for traffic?

2. How can product operations increase the number of new users and retain them?

3. Product operation: 2 major ways to get started to accurately capture private domain traffic!

4. Product Operation | How do stranger social products guide users?

5. How can product operations conduct good competitor research and analysis?

6. Product operation and promotion | 5 underlying ideas for traffic growth!

7. Product operation: application of data system under the growth model!

8. How can product operations conduct good competitor research and analysis?

Author: Ruining Rita

Source: Ruining Rita