Detailed explanation of core indicators and terminology of user growth

This chapter will first help you systematically sort out the core and most commonly used indicators and terms in the field of product and market growth. Let’s consider the question of why we want to build an app?

Part One: Core Framework for Growth - AARRR

The AARRR "Pirate Model" is the most classic framework in the growth field. It depicts the five key stages of the user life cycle, forming a progressive funnel.

Acquisition: Users come into contact with your product for the first time through various channels. Refers to: How do users find you?
Activation: The user completes a key behavior in the product. Refers to: Did the user get a good first experience?
Retention: After the first experience, users are willing to use your product repeatedly in the future. Refers to: Will users come back again?
Referral: Users are satisfied with the product and spontaneously recommend it to others, forming word-of-mouth communication. Refers to: Are users willing to recommend it to others?
Revenue: Products earn business returns by providing value to users. It means: How do you make money from users?

Part 2: Customer Acquisition Indicators (Acquisition)

Customer acquisition is the first step in growth and measures our efficiency and cost in attracting new users from different channels.

CPA (Cost Per Acquisition) / CAC (Customer Acquisition Cost) - User acquisition cost
Definition: The total cost required to acquire an effective new user (such as download, registration, first purchase, etc.). CAC generally refers to the cost of acquiring a “paying customer” and is more narrowly defined.
Calculation formula: Total marketing and sales costs / Total number of new users acquired during the same period
Interpretation: This is an indicator of customer acquisition efficiency. A healthy business model must ensure LTV > CAC.
CPC (Cost Per Click) - Cost per click
Definition: In paid advertising, the advertiser pays every time a user clicks on an ad.
Calculation formula: Total advertising spend / Total number of clicks
Interpretation: CPC is an indicator that measures the cost of advertising traffic and is commonly used in search engine marketing (SEM) and information flow advertising. Optimizing CPC is a direct way to reduce customer acquisition costs.
CPM (Cost Per Mille) - Cost per thousand impressions
Definition: The price you pay per thousand times your ad is shown.
Calculation formula: (Total advertising spend / Total number of impressions) * 1000
Interpretation: CPM is mainly used to measure the exposure cost of advertising. It is often used for brand promotion and advertising campaigns to increase awareness. The goal is to cover as many people as possible.
CTR (Click-Through Rate) - Click-Through Rate
Definition: The number of times an ad was clicked as a percentage of its total impressions.
Calculation formula: (Total number of clicks / Total number of impressions) * 100%
Interpretation: CTR directly reflects the attractiveness of advertising materials (copywriting, images, videos) and target audience positioning. A high CTR means that the ad content is highly relevant to the user's interests.
CR (Conversion Rate) - Conversion rate
- Definition: The percentage of users who complete a specific target behavior among the total visiting users.
- Calculation formula: (number of users who completed the target behavior / total number of users) * 100%
- Interpretation: Conversion rate is the key to measuring the efficiency of each link of the funnel. For example, the "visit-to-registration conversion rate" from clicking on the ad to the landing page, the "registration-to-payment conversion rate" from registration to the first purchase, etc.

Part 3: Activation & Engagement

Success is not considered successful when users come, the key is to make them active and feel the value.

DAU / WAU / MAU (daily/weekly/monthly active users)
Definition: The total number of unique users who have logged in or used the product at least once within a specific time period (a day, a week, a month).
Interpretation: This is the most direct indicator to measure the size of product users and the basic market. Their trend changes reflect the vitality and market position of the product.
Stickiness Ratio = DAU/MAU
Definition: The ratio of daily active users to monthly active users.
Calculation formula: DAU / MAU * 100%
Interpretation: This is a key indicator of user loyalty and product dependence. The higher the ratio, the more frequently the user uses the product and the stronger the habit. Generally speaking, above 20% is considered good, and above 50% is excellent performance (such as social media applications).
Session Duration & Pages Per Session
Definition: The former refers to the average duration of a single visit by a user, and the latter refers to the average number of pages viewed by a user per visit.
Interpretation: These two indicators measure user engagement from two dimensions: time and depth. Longer duration and more pages usually mean users are more interested in the content or features. However, it needs to be combined with product type analysis. For example, tool products may pursue high efficiency, and session length is a good thing.
Bounce Rate
- Definition: The number of visits in which a user visits the first page of a website or app and leaves without any subsequent actions (such as clicking on a link or jumping to a page) as a proportion of the total number of visits.
- Interpretation: A high bounce rate is usually a negative sign and may mean: page content does not match user expectations, loads too slowly, has poor UI/UX design, or unclear navigation.

Part 4: User Retention Indicators (Retention)

Retention is the cornerstone of growth. A product that cannot retain users will have no chance of growth.

Churn Rate
Definition: The percentage of users who lost during a specific period to the total number of users at the beginning of the period. Churn can refer to "user churn" (no longer active) or "revenue churn" (unsubscriptions).
Calculation formula: (number of users lost during a specific period / total number of users at the beginning of the period) * 100%
Interpretation: Churn rate is a "reverse indicator" of product health. High churn rate will greatly erode growth results, and reducing churn rate is the core task to achieve sustainable growth.
Retention Rate
Definition: After a specific period, the percentage of users who are still active accounts for the total number of new users.
Calculation formula: (number of active users on day N (week/month) / total number of new users on day 0 (week/month)) * 100%
Interpretation: Retention rate is the opposite of churn rate and directly measures the long-term attractiveness of a product to users. Common ones include next-day retention, 7-day retention and 30-day retention, which together form the retention curve of the product.
Cohort Analysis
- Definition: Divide users with the same characteristics (such as registration time, customer acquisition channels) into a "cohort", and then track and compare the behavior patterns of different groups over a period of time (such as retention rate, payment rate).
- Interpretation: This is a powerful tool for understanding the evolution of user behavior and evaluating the effectiveness of product iterations or marketing campaigns. By comparing the retention curves of registered users in different months, you can clearly see whether product improvements have led to an increase in retention rates.

Part 5: Recommended indicators (Referral)

Happy users are the best marketing channel, and referrals are the key to viral growth.

K-Factor
Definition: The core indicator to measure the virality of a product, indicating how many new users each existing user can bring on average.
Calculation formula: K = i * c, where i is the average number of invitations sent by each user, and c is the conversion rate of each invitation.
Interpretation:
- K > 1: The product is in a state of viral growth, and user growth will drive itself like a snowball.
- K < 1: Product growth still needs to rely on external channels and cannot achieve spontaneous growth.
- Improving K-factor requires optimizing the invitation process and increasing the conversion rate of new users.
NPS (Net Promoter Score) - Net Promoter Score
- Definition: A measure of user loyalty and willingness to recommend. Calculated by one question: "How likely are you to recommend our product/service to a friend or colleague? (0-10 points)"
- Calculation method:
  - Recommended by (Promoters): 9-10 points, loyal fans.
  - Passives: 7-8 points, satisfied but not enthusiastic.
  - Detractors: 0-6 points, dissatisfied users.
  - NPS = (% Promoters - % Detractors)
- Interpretation: NPS scores range from -100 to +100. It is not only a number, but also a feedback system that drives improvement, helping companies identify and solve user pain points, and convert passive users and detractors into promoters.

Part 6: Business revenue indicators (Revenue)

Revenue is the blood of business survival and development and measures the ability of a product to convert user value into business value.

ARPU (Average Revenue Per User) - Average revenue per user
Definition: The average revenue earned per active user during a specific period.
Calculation formula: Total revenue / Total number of active users
Interpretation: ARPU measures the general monetization ability of the entire user base, regardless of whether the user pays or not. Increasing ARPU means the product’s overall value capture capability for all users is increasing.
ARPPU (Average Revenue Per Paying User) - Average revenue per paying user
Definition: The average revenue earned from each paying user during a specific period.
Calculation formula: Total revenue / Total number of paying users
Interpretation: ARPPU focuses on the value of paying users and is a key indicator to measure the willingness and ability of core users to pay.
LTV / CLV (Customer Lifetime Value) - User Lifetime Value
Definition: Predict the total revenue a user will contribute to the business over the lifetime of their relationship with the product.
Calculation formula: A simplified formula is LTV = average revenue per user per month * (1 / monthly churn rate).
Interpretation: LTV is a predictive metric that answers the question "How much is a user worth in the long run?". LTV > CAC is the golden rule that all healthy business models must abide by. It ensures that investment in customer acquisition will ultimately bring positive returns.
MRR / ARR (monthly/annual recurring revenue)
- Definition: MRR (Monthly Recurring Revenue) and ARR (Annual Recurring Revenue) refer to monthly and annual predictable and sustainable revenue respectively. ARR is usually equal to MRR * 12.

Part 7: Data differences in indicators between Web vs. App

Theory needs to be combined with scenarios. We take an "AI role chat" product for C-side as an example, and use specific data to analyze the core differences between its Web side and App side in various aspects of growth.

All figures are approximate only and are intended to illustrate relative differences between platforms.

Scene setting

Product: AI character chat application (similar to Character.AI).
Core Value: Users can have immersive conversations with AI characters with different settings.
Business Model: Freemium model offering a monthly subscription to unlock premium features (like faster response times, unlimited chats, etc.).
Region: Europe and America.

1. Acquisition - traffic cost and user intent

Indicators	Web	App	Data Interpretation
Unit Cost	CPC: $0.8	CPI: $2.5	In European and American markets, traffic costs are higher. The "installation" cost of App is naturally higher than the "click" cost of Web.
Registration Conversion Rate	Click->Register CVR: 4%	Install->Register CVR: 60%	Web-side user intention is shallow and the conversion rate is low. "Installation" is a high-commitment behavior and the user's intention is clear, so the registration conversion rate is extremely high.
Final Customer Acquisition Cost	CPA (Registration): $20.00	CPA (Registration): $4.17	Although the initial cost is high, the App side relies on its high-intention users, and the final cost (CPA) of acquiring a registered user is much lower than that of the Web side, and the customer acquisition efficiency advantage is huge.

2. Activation - the value of immersive experience

Core Activation Event: The user and AI character send more than 10 messages.

Indicators	Web	App	Data Interpretation
First impression	Bounce rate: 65%	Activation rate (installation->first time opening): 90%	The quality of web traffic is uneven, and a large number of users have only tried it. After app users complete the installation, most of them will open the app.
Core Behavior Conversion	Register->Send 10 messages CVR: 20%	Register->Send 10 messages CVR: 45%	The App provides a distraction-free, full-screen immersive environment, allowing users to focus more on experiencing the core gameplay, so the activation conversion rate is much higher than a browser environment full of distractions.

3. Retention - Reachability and usage habits

Indicators	Web	App	Data Interpretation
Next-day retention rate	8%	35%	App can proactively and efficiently recall users through push notifications (such as "Your AI partner has sent you a message!"). The web side relies on weak contact methods such as user active memory or email, and the early retention gap is huge.
30-day retention rate	2%	12%	In the long run, the App’s “sense of presence” on the mobile desktop and its convenient opening method make it easier to integrate into users’ fragmented time, thereby cultivating stronger user habits and loyalty.
churn signal	The user has not logged in for 30 consecutive days	Uninstall rate: 40% (within 30 days)	The "uninstallation" of an app is a clear and traceable strong churn signal. The definition of "churn" on the web is vague and difficult to accurately measure, which gives apps an advantage when analyzing the reasons for user churn.

4. Revenue - payment scenarios and willingness

Indicators	Web	App	Data Interpretation
Paid conversion rate	Active user->Subscription CVR: 0.5%	Active user->Subscription CVR: 1.5%	The App is seamlessly integrated with App Store payment (Apple/Google Pay), and the payment process is extremely smooth, which greatly reduces the user's decision-making and operating costs, so the payment conversion rate is higher. The more core point is that the subscription renewal rate of IAP is much higher than that of the Web.
ARPPU	$9.99/month	$9.99/month	Assume that the subscription prices at both ends are the same.
Platform commission	~5% (such as Stripe)	15%-30% (Platform tax)	Although the payment conversion rate on the App side is high, the channel cost (platform tax) is also extremely high, which will directly affect the final net income. Web payment channel rates are much lower.
Net ARPPU	~$9.5	~$7.0 - $8.5	After deducting the platform commission, the net revenue obtained by the App from each paying user has dropped significantly. This is a trade-off that must be considered when choosing a primary battlefield for commercialization.
LTV (12 months)	$1.14	$10.20	Final conclusion: Even after taking into account the high platform tax, the App side still far exceeds the Web side in terms of LTV (user lifetime value) due to its huge advantages in retention and paid conversion. For C-side entertainment products, App is the core for commercialization.

Therefore, my conclusion is that for most C-side products:

Web client: more suitable as a window for brand display and preliminary traffic drainage. It can quickly verify market demand and guide high-intent users to App downloads, but it should not be used as a hosting and monetization platform for core users.
App side: It is the main battlefield for core user operations and commercialization. Its immersive experience, high retention and convenient payment are the keys to building a product moat and achieving large-scale revenue.

This is also the answer to the question at the beginning of the article, and this is why I want to write about App-related development first in the follow-up.

Part 8: Summary

Together, the above indicators form a comprehensive growth analysis framework. It is important to emphasize that no single indicator can reflect the whole picture. In real projects, these indicators should be combined to build a complete "indicator dashboard" to gain insight into user behavior, evaluate the effectiveness of strategies, and drive the continuous and healthy development of the business.