Internet Marketing, Advertising, and Branding

1. CVR Cut-off 🔗︎

Definition:
CVR Cut-off refers to the practice of truncating or removing data with excessively low or high conversion rates (CVR) during advertising performance analysis to improve the reliability and accuracy of the analysis.

Purpose:

• Setting Upper and Lower Limits: For example, truncating data with a conversion rate below 0.5% or above 20%.
• Avoiding Interference from Extreme Values: Improving the precision of data analysis by excluding outliers.
• Optimizing Ad Performance Evaluation: Better evaluate advertising effectiveness and optimize ad delivery strategies.

2. All-Day Pacing 🔗︎

Definition:
All-Day Pacing is a common strategy in online advertising aimed at distributing the ad budget as evenly as possible throughout the ad delivery period, ensuring the ad receives reasonable exposure and click-through rates at all times of the day.

Features:

• Dynamic Adjustment: Automatically adjusts ad delivery frequency and timing based on conditions set by the advertiser, such as bids, budget, and target audience.
• Real-time Monitoring and Optimization: Combines real-time data monitoring to dynamically adjust delivery strategies and maximize ad ROI.
• Avoiding Budget Waste: Ensures the ad budget is not concentrated in specific periods, thus avoiding missing opportunities in other important time slots.

3. Attribution Adjustment 🔗︎

Content to be added:
Attribution adjustment is a crucial part of ad delivery, involving how to reasonably attribute conversion credit to different ad channels or touchpoints. It is typically used to solve the contribution allocation problem when users are reached through multiple channels, helping advertisers more accurately assess the actual effectiveness of each channel.

4. Eliminating Inefficient Channel Traffic Sources 🔗︎

Content to be added:
Eliminating inefficient channel traffic sources refers to identifying and removing traffic sources with low conversion rates, high costs, or poor performance through data analysis. This optimizes the allocation of ad resources, concentrating the budget on high-efficiency channels to improve the overall ROI of ad delivery.

Ad Settings and Display Strategy

1. Research Competitors’ Ad Settings 🔗︎

• Objective: Analyze the ad settings of competing apps of the same type.
• Focus Points:
  • Ad display positions (e.g., homepage, detail pages, bottom navigation bar, etc.).
  • Ad types (e.g., banner ads, interstitial ads, rewarded video ads, etc.).
  • Ad content (e.g., e-commerce promotions, utility app ads, brand campaigns, etc.).

2. User Persona and Payment Value Assessment 🔗︎

• User Age Groups:
  • Identify the primary age range of your user base (e.g., teenagers, young adults, middle-aged, etc.).
• Payment Value Assessment:
  • Analyze users’ spending power and willingness to pay.
  • Select appropriate ad types based on user characteristics to increase ad revenue.

3. Ad Type Selection Recommendations 🔗︎

• Recommended Ad Types:
  • CPA (Cost Per Action): Billed after a user completes a specific action (e.g., registration, download).
  • CPS (Cost Per Sale): Billed after a user makes an actual purchase through an ad.
  • CPM (Cost Per Mille): Billed per a certain number of ad impressions (e.g., one thousand).
• Ad Types to Avoid:
  • Gambling ads, and spammy mobile game ads.

Advantages of In-Page Mini-Programs and the Value of In-Feed Ads

1. Advantages of In-Page Mini-Programs 🔗︎

• Seamless Purchase Experience:
  By embedding a mini-program in an ad page, users can make purchases directly without jumping to other platforms.
  • Reduced Costs: Reduces user churn from redirects, increases conversion rates, and thus significantly lowers customer acquisition costs.
  • Improved User Experience: Simplifies the shopping process and enhances user satisfaction.

2. Core Value of In-Feed Ads 🔗︎

• Adapts to Mobile Internet Browsing Habits:
  In-feed ads naturally fit the browsing patterns of mobile internet users, integrating into their daily content consumption scenarios.

• Precise Recommendations:
  Based on user characteristics and interests, in-feed ads can achieve more intelligent recommendations.

  • Increased Reach: Through data analysis, ads are shown to the most relevant user groups.
  • Increased Conversion Rate: Precisely matches user needs, increasing the likelihood of ad clicks and purchases.

3. Relationship Between Technology, User Growth, and Commercialization 🔗︎

• Technology-Driven:
  Advanced technology, such as big data analysis and artificial intelligence algorithms, is the foundation for achieving precise recommendations and optimizing user experience.

• User Growth:
  Attract more target users and expand the user base through efficient ad delivery strategies and optimization methods.

• Commercialization:
  On the basis of user growth, achieve maximum business value through ad monetization, e-commerce conversion, and other methods.

1. Brand and Market Core Concepts 🔗︎

• Brand = Traffic

  • The core value of a brand lies in attracting traffic.

• Two Major Directions of Marketing Work:

  1. Go Out: Expand into external markets to increase brand exposure.
  2. Bring In: Attract target users to drive conversions.

2. Data and User Research 🔗︎

• Competitor Analysis:

  • The core task of data and user research is to “know yourself and your enemy” by analyzing competitors to understand market dynamics, as well as their strengths and weaknesses.

• Video Editing Team:

  • As an important communication medium, video content needs the support of a professional video editing team to improve content quality and dissemination effectiveness.

3. Current Problems 🔗︎

3.1 Key Metrics Not Met 🔗︎

• Failure to achieve set goals may stem from inadequate strategy execution or insufficient resource allocation.

3.2 Lack of Product Innovation 🔗︎

• The product is highly homogenized and lacks differentiated competitiveness.

3.3 No Breakthrough in Growth 🔗︎

• User growth has stagnated, and new growth points have not been found.

3.4 Slow Response 🔗︎

• Low efficiency in decision-making and execution, unable to respond quickly to market changes.

3.5 Lack of Resources 🔗︎

• Insufficient resources (manpower, capital, technology, etc.) limit business development.

4. Strategy and Organizational Problems 🔗︎

4.1 Strategy is not clear enough 🔗︎

• Lack of a clear strategic direction leads to inconsistent team goals.

4.2 Structure is not reasonable enough 🔗︎

• An unreasonable organizational structure may lead to unclear responsibilities and difficulties in collaboration.

4.3 Capabilities are not in place 🔗︎

• Insufficient capabilities in key positions affect overall execution and business progress.

5. Channel and Technical Barriers 🔗︎

• Channel Barriers:

  • Insufficient acquisition and utilization of channel resources limit market expansion.

• Technical Barriers:

  • Lack of technological innovation capabilities makes it difficult to achieve product breakthroughs.

6. High Customer Unit Price and Content + Service Model 🔗︎

• High Customer Unit Price Strategy:

  • Target users with high spending power by providing high-value products and services.

• Content + Service Model:

  • Combine high-quality content with precise services to enhance user experience and loyalty.

7. User Growth and Commercial Monetization 🔗︎

• User Growth:

  • Use precise data guidance to identify which users are more likely to convert.
  • Develop targeted growth strategies to improve user acquisition efficiency.

• Commercial Monetization:

  • Convert user growth into actual revenue, optimize the business model, and enhance profitability.

User Growth Strategy

1. Define the user lifecycle and user growth path, and discover effective methods through key moments or magic numbers.
2. Intervene and stimulate users when user churn reaches a certain threshold.
3. Self-service analysis + cross-comparison of metrics + user personas + A/B testing -> formulate product and operational strategies.

Key Concepts
Attribution Analysis: Identify all processes that contribute to the final conversion.

Analysis Methods:

1. Data Statistics
2. EBM (Explainable Boosting Machine)
3. Network Analysis
4. Standardized Preferences
5. Single-Sample Methods

Traffic Fraud

Terminology 🔗︎

Takerate 🔗︎

• Definition: The proportion of profit that a merchant invests in marketing activities.
• Purpose: Used to assess the merchant’s emphasis on marketing and their resource allocation.
• Formula: [ Takerate = \frac{\text{Marketing Investment Amount}}{\text{Total Profit}} ]

ROI (Return on Investment) 🔗︎

• Definition: The return on investment, representing the percentage of revenue generated per unit of investment.
• Purpose: To measure the economic effectiveness of an advertising or marketing campaign.
• Formula: [ ROI = \frac{\text{Revenue} - \text{Cost}}{\text{Cost}} \times 100% ]
• Example: If you invest 100 yuan in advertising and generate 300 yuan in revenue, then: [ ROI = \frac{300 - 100}{100} \times 100% = 200% ]

Page Conversion Funnel 🔗︎

d to o (Detail to Order) 🔗︎

• Definition: The conversion from the product detail page to the order page.
• Purpose: To measure the efficiency of user behavior conversion from browsing a product to deciding to purchase.

o to p (Order to Payment) 🔗︎

• Definition: The conversion from the order page to the payment page.
• Purpose: To assess the user’s willingness to complete the payment and the smoothness of the payment process.

CPC (Cost Per Click) 🔗︎

• Definition: Pay-per-click, the fee an advertiser pays for each click on their ad.
• Purpose: Commonly used for search engine advertising (like Google Ads) or social media advertising.
• Formula: [ CPC = \frac{\text{Total Advertising Cost}}{\text{Number of Clicks}} ]
• Optimization Goal: To reduce the cost per click while improving the quality of clicks.

CVR (Conversion Rate) 🔗︎

• Definition: The percentage of users who complete a specific goal (e.g., registration, activation, payment).
• Purpose: To measure the conversion effectiveness of an ad or a page.
• Formula: [ CVR = \frac{\text{Number of Conversions}}{\text{Number of Clicks}} \times 100% ]
• Example: If there are 500 clicks, and 50 of them convert into paying users, then: [ CVR = \frac{50}{500} \times 100% = 10% ]

CTR (Click-Through Rate) 🔗︎

• Definition: The frequency with which an ad or link is clicked.
• Purpose: To evaluate the attractiveness of an ad or content.
• Formula: [ CTR = \frac{\text{Number of Clicks}}{\text{Number of Impressions}} \times 100% ]
• Example: If an ad is shown 10,000 times and is clicked 200 times, then: [ CTR = \frac{200}{10,000} \times 100% = 2% ]

POC (Proof of Concept) 🔗︎

• Definition: An incomplete implementation of an idea or hypothesis to demonstrate its feasibility.
• Purpose: In data analysis and product development, it is used to verify the actual effectiveness of new features, strategies, or technologies.
• Features:
  • Usually a small-scale experiment or prototype.
  • The goal is to quickly validate a hypothesis and reduce the risks of large-scale implementation.
• Application Scenarios:
  • Testing the effectiveness of a new advertising strategy.
  • Verifying the user experience of a new feature.