A practical guide to user growth

User growth is no longer a new concept. Many companies now set up growth hacker positions and even form growth teams. However, there are many problems in the actual operation process. This article provides some suggestions on how to promote user growth and is recommended for those who are interested in user growth.

When it comes to growth, AARRR, aha moments, magic numbers, and North Star indicators are often mentioned. However, when it comes to actual operations, problems often arise, and they eventually turn into operational activities, marketing tools, and user subsidies.

This growth theory sounds quite scientific and advanced, but why is it that when it is actually implemented, there is no particularly obvious difference from the means used in the past?

In terms of indicator growth, the activities we carried out in the past were able to bring good results; in terms of data-driven, we have also done a lot of product data analysis and activity review. So what kind and to what extent can user growth theory bring us? I don’t know if you have ever had this question, but I have had it before.

After actually carrying out a series of growth tasks, I feel that sometimes what we lack may not be methodology, but perhaps the application of specific tools and methods.

The essence of user growth may not be knowledge, but skills. Otherwise, after reading a few books and taking relevant courses, everyone can become a user growth expert. Based on this conclusion, perhaps what we need is to learn how to use tools and processes and to practice continuously. Therefore, we need to build our own growth toolbox and take out the right tools at the right stage.

The purpose of writing this article is to share my toolbox and introduce the necessary knowledge points, so that it can be used right away. Without further ado, let’s get straight to the point.

1. Before starting growth

If you want to do your work well, you must first sharpen your tools. Before we start growing, we also need to do a series of preparatory work.

Includes: clarifying the core value of the product, target users, aha moments, magic numbers, North Star indicators, growth models (formulas), core growth indicators, and user journeys.

The significance and value of these concepts have been discussed too much, so I will not go into details. Let’s just talk about how to do it. Product core value and target users: If I need to explain this, I suggest you don’t make this product.

1. Aha Moment

It refers to the moment when users truly discover the core value of the product. At this moment, users realize how indispensable the product is to them. It sounds simple enough.

Although in many cases, we can clearly define the aha moment, for example, for a taxi app, the aha moment is that after you place an order with your mobile phone, a car will come to pick you up. The faster the driver comes, the better the experience is usually. But sometimes, the aha moment may not be exactly what we designed, and users may discover new ways to use the product on their own. Therefore, it is still necessary to verify the aha moment. There are 3 steps you can follow to identify your product’s aha moment.

1. List possible key behaviors: Based on the core value of the product we defined, try to list the key behaviors that influence users to judge the value of the product.
2. Data Analysis: Yes, we have to look at the data so soon. We can group users according to business indicators and observe the differences in behavioral data among groups. For example (and it is also recommended) to group users based on next-day/7-day/30-day… retention. It would be even better if you can find out the natural usage cycle of the product. By observing the differences in the behaviors of retained users and churned users, we can usually find some key behaviors.
3. Qualitative analysis: Interview or conduct questionnaires with users based on the key behaviors found in the previous step to confirm the users’ real aha moments. For example, conduct a survey on the indispensability of a product (see the template at the end of the article for details).

2. Magic Number

The magic number is a further quantification of the aha moment, referring to (who) completing (what behavior) (how many times) (in how long). In fact, if we can find the aha moment, we can define the magic number. This number is not necessarily objective and will partly refer to our business goals.

For example, an e-commerce app determines that users who remain and make transactions within a week are ideal best customers. It will then look for the magic number based on this standard, and may be able to uncover the characteristics of the user's data on product visits and add-to-cart volumes within the app. Therefore, if you want to do this more scientifically, you can use AI algorithms to cluster users and let the machine help find appropriate business indicators. Then, based on whether the business indicators are met, models such as decision trees or random forests are built for the behavioral data to identify the key behaviors that have the greatest impact on the business. For a specific single behavior indicator, you can use, for example, SVM to do segmentation and find the critical point.

The following chart shows the magic numbers (also known as aha moments) of several well-known products for reference:

3. North Star Indicator

Also known as the only important indicator, the name should need no explanation. This indicator is an indicator that can truly and directly reflect the quality of the product, rather than the superficial indicators (or vanity indicators) that we usually pay attention to, such as the total number of registered users.

Taking Honor of Kings as an example, the total number of registered users should be the vanity indicator, and a further indicator is DAU. However, as the game life cycle progresses, DAU will tend to stabilize. Of course, this is only for long-lived games like Honor of Kings. The DAU of general games tends to decline. At the same time, as a mobile game with a DAU of over 100 million, on the one hand this is a very scary number, but on the other hand it may have reached its ceiling in terms of the number of active users. Therefore, it is likely that the focus will be on game depth, so the number of games per day or the number of users who play more than X games per day would be a more appropriate North Star metric.

It is important to emphasize that the North Star Metric is not static. Different North Star Metrics and growth levers may be defined depending on the product’s life cycle and operating stage.

For example, different indicator data will be paid attention to in the new customer acquisition stage and the monetization stage. For more detailed suggestions on defining the North Star indicator, please refer to the template at the end of the article.

4. Growth Model (Formula)

The “formula” in brackets represents the essence of the growth model. I prefer to express it in formula form, which is more direct and clear. Generally speaking, one side of the formula is the North Star indicator, and the other side is the core growth indicators that constitute or influence that indicator. There are two common ways to construct formulas. If you break down the North Star indicator vertically, you will get a formula that is mainly constructed by addition and multiplication; or if you push it horizontally, you will get a conversion funnel. However, these two methods do not conflict with each other and are often used in parallel.

As shown in the figure below, this is a simple growth formula with the number of active users as the North Star indicator. At the first level, the number of active users is broken down into new and existing ones. At the second level, a conversion funnel for new users and retention of old users are defined respectively.

5. User Journey

This thing drawn separately is the user's operation process in the product. The more detailed the better. The greater value of the user journey is that in the subsequent different growth stages, indicators will be analyzed and strategies will be formulated based on different nodes of the user journey. Before that, it’s best to prepare this user journey map, and it’s even better to simultaneously draw the user’s emotional change curve on it.

Next, I will follow the classic user growth routine and divide it into four stages: customer acquisition, activation, retention and monetization, and introduce some know-how in each stage.

2. Growth tasks at different stages

1. Customer acquisition

User acquisition is the origin of growth. In order to acquire users, we must find the right channels. The criteria for judging whether a channel is excellent, in addition to the absolute number of users the channel can bring, also need to focus on the long-term LTV. Only by ensuring LTV (user lifetime value) and CAC (customer acquisition cost) can a product have the possibility of profitability.

The above should be a natural deduction logic.

Therefore, the most important thing in the customer acquisition stage is to choose the right customer acquisition channel. We can complete the channel screening in the following 5 steps:

1. Determine product features
2. Identify user groups
3. List possible alternative channels
4. Screening initial customer acquisition channels
5. Channel Optimization

The template at the end of the article provides detailed instructions for each step.

In the process of channel optimization, we need to constantly conduct new channel experiments. Channels can be listed according to the table below, and channel costs, target user matching, degree of control, time investment, output time and coverage scale can be scored from 1 to 10, with 1 being the worst and 10 being the best. Finally, we sorted by average score and prioritized testing channels with higher scores.

Channel Experiment Evaluation Form

Data monitoring is required for the experimental channel. The following table provides a data monitoring template:

Channel data performance

When it comes to customer acquisition, we have to pay attention to a special and effective channel: the viral channel.

The essence of viral channels is to acquire customers through person-to-person recommendation, similar to viral transmission (I believe everyone is now very clear about this concept, and we now deeply understand the terrible degree of viral transmission). Terms such as word-of-mouth communication and social fission all speak to similar principles.

The effectiveness of this customer acquisition method is so great that any product should consider whether it needs to design a viral loop. The premise of designing a viral loop is that the product can provide real value to users, that is, the existence of the aha moment, which does not need much explanation.

Furthermore, the virality of any product is determined by 3 factors:

1. Payload: how many people each person transmits to each time. For example, sending to friends in WeChat is a one-to-one dissemination, while sending to a group or circle of friends will be a large-scale dissemination. (No wonder the Happy Landlords require sharing to WeChat groups for free Happy Beans~)
2. Conversion rate: This one needs no explanation.
3. Frequency: This is also easy to understand, that is, the frequency of users initiating dissemination. However, these three points are relatively general judgment criteria. If you really want to evaluate the effect of viral dissemination within the product in detail, you must also build a real dissemination conversion funnel.

Don’t worry, the template has already prepared a typical viral conversion funnel for you:

Viral Conversion Funnel

2. Activation

Simply acquiring customers only means that someone has visited your product. To truly turn the visiting traffic into your users, you must activate them.

Before activating users, the most important thing is to define the activation standards. If the wrong activation standards are used, it will inevitably lead to wrong activation strategies, which will ultimately affect the healthy development of the product. Fortunately, this standard is not difficult to define, and it is usually the aha moment and magic number we find.

For example, a third-party payment product may define its activated users as users who have completed their first bank card binding and payment (well, this first time is actually problematic. If you only focus on the user’s first payment behavior, the operation strategy will be tilted towards the first order, which means it will be targeted by the wool party and may ignore the continuous operation after the first order). The "secret" to increasing the activation rate is to let users experience the aha moment as soon as possible.

This is when the user journey we drew earlier comes in handy. Mark the aha moment, find all the nodes leading to the aha moment, and then start optimizing.

Either shorten the distance to the aha moment, or ensure that users have enough enthusiasm at each node before reaching the aha moment. Another effective tool is to activate the conversion funnel. Don't just look at the total funnel. It is recommended to group users by channel (other applicable grouping methods are of course also acceptable). This way, you can understand the differences between users in different channels, which can be used for targeted optimization based on channel characteristics and can also be used to screen channel quality. The template also provides a typical activation conversion funnel for reference.

Activate conversion funnel

3. Retention

As with activation, you must first define the retention criteria, including the definition of specific events and the definition of retention duration. We mentioned earlier the user's natural usage cycle, which means the natural frequency and cycle of a user's continued use of a product.

The natural usage cycles of different products are different. For games, we may open them and play them every day, so we will pay attention to the retention rate on the next day.

But for an e-commerce app, it is impossible for us to shop every day. If data analysis shows that 80% of users have an average interval of about 7 days between two purchases, then the natural usage cycle of this product may be 7 days. We will focus on retention on a weekly basis.

However, some financial apps may have a longer natural usage cycle, so it is normal to pay attention to their 30-day retention and 60-day retention. As for the segmentation, drawing and analyzing of retention curves, we have already introduced it in detail in Data Product Guide (IV) - User Behavior Analysis Platform, so we will not repeat it here.

Here we only introduce two tools that will be used in retention operation strategies.

1) Participate in the loop

Or it is called addiction model, reinforcement circuit, etc. The core is to set up a cyclic mechanism in the product to continuously strengthen user usage habits. It includes 4 steps:

1. Trigger: Product design that stimulates users to take action. The design standard should be to remind users that there is an opportunity that is obviously valuable to them.
2. Action: The user is successfully triggered and takes action
3. Reward: The product gives users instant gratification
4. Investment: Returns motivate users, increase their stickiness to the product, and encourage them to invest more personal resources

Addiction Model

We need to design enough natural participation loops in our products to guide users to continue using the products and form dependence.

2) User Engagement Ladder

This is a simple user segmentation model that divides users into passive users, core users and super users according to their level of participation in the product.

• Passive users: Those who do not use the product in an optimal way. For example, a Weibo user who has never registered on Weibo may occasionally pay attention to information on Weibo through sharing by friends.
• Core users: Mainstream active users who use the product in the right way and at a normal frequency. For example, users who have a fixed habit of browsing Weibo.
• Super users: highly engaged users with high usage frequency and depth. For example, the big Vs who are active on Weibo every day. The proportions of these three types of users are approximately 90%, 9% and 1% respectively, so it is also called the 1-9-90 rule.

User Engagement Ladder

Our operational goal is to clearly define the three types of user standards in the product and gradually guide users to migrate to the upper level. The higher the user is on the ladder, the stronger the connection with the product, and the longer the retention will be. The template also provides you with recommendations for retention tools that can be used in different retention periods.

Matching user retention with retention tools

4. Monetization

Don’t forget that our ultimate goal of acquiring, activating, and retaining users is to monetize, so we need to increase the revenue brought by each user. In order to increase revenue, we need to draw a monetization funnel. Strictly speaking, this is not a funnel, but there is a payment funnel at each payment point.

So we have to take out the user journey map again. We need to mark all the opportunities to make profits from users on the map, and at the same time find all the links that hinder us from making profits. These places are called pinch points.

The next thing we need to do, of course, is to set payment triggers at the profit opportunity points and work hard to eliminate pinch points, or at least minimize their impact.

In the monetization stage, segmentation analysis is equally important. We can segment users according to the revenue they contribute. On the one hand, we can understand the characteristics and behavioral performance of high-value users, and on the other hand, we can find the connection between each group and revenue. More specific pricing optimization involves the so-called pricing relativity and consumer psychology, which will not be elaborated here.

The template provides some simple, usable principles, which should be familiar to you if you have read "Influence" and "Freakonomicon".

Pricing optimization points

3. About Growth Experiment

User growth cannot be separated from a large number of experiments. Failing fast and learning fast is the norm in the growth process.

A complete growth experiment includes at least the following four steps:

1. Come up with ideas based on the current situation: Assuming that we have done enough analysis, clearly identified the problem, and clarified the growth goals for the next stage, it is time to brainstorm. In order to ensure that the ideas proposed by everyone are not too weird, we can standardize the format appropriately. An idea needs to contain at least the following information: name, description, hypothesis and indicators to be measured. For detailed filling instructions, please refer to the template.
2. Prioritize experimental ideas: Due to actual resource limitations, we can only experiment with ideas with higher priority, so we need a set of criteria for judging priorities. Here you can refer to the "ICE scoring system" developed by Sean Ellis, the author of "Growth Hacker". ICE stands for impact, confidence, and ease. When ideas are submitted or in growth meetings, we score them, with 1 being the worst and 10 being the best, and prioritize them based on the average score.
3. Experimental testing: This seems to be self-explanatory. The key is to ensure the statistical significance of the experimental results and avoid drawing wrong conclusions.
4. Experiment summary: Describe the overall effect of the experiment, the affected features, changes in key indicators, etc. The template provides a simple experiment report template. For the head of the growth team, you should always have a growth planning map in hand to understand the current growth goals, strategies, assumptions and experimental ideas. The simple template is shown below.

Growth Planning Map

4. About Toolbox Templates

It should be noted that the toolbox is largely based on the two books "Growth Hacker" and "Silicon Valley Growth Hacker's Actual Notes". After all, learning from big cows is a good way to make rapid progress, and there is nothing shameful in being a knowledge porter.

This template is divided into several sub-tables according to stages and attributes. Each table will contain approximately four types of information: a template for filling in content that needs to be determined in actual operations, a data analysis model template, a user research template, and a checklist of key points in product design. Down

The figure takes the template of the customer acquisition stage as an example, which includes the definition of new user standards, channel selection steps and guidance, channel experiment evaluation standards, channel data performance, viral transmission conversion funnel and checklist information of the customer acquisition stage.

User Growth Template - Customer Acquisition

Although templates can guide our growth work to a certain extent, market conditions, game rules and growth theory itself are constantly improving. Never expect a tool that can be used once and for all. Internalizing growth theory and forming your own template through continuous training and accumulation is a more valuable growth path in the long run. Of course, my templates will continue to be optimized.

Author: Rowan

Source: Mr. Luo, don't do this

Related reading:

5 general methods to increase user growth!

Low budget user growth model!

How to implement a user growth plan from 0 to 100?

A must-know method for user growth: retention curve