What is the difference between Telegram Active Filtering Pro and Basic Filtering? A comparative analysis ranging from Daily Active User (DAU) assessment to behavioral tagging methods.

I. Daily Active Users (DAU) Determination Criteria: Single Timestamp vs. Multi-Dimensional Activity Trajectory

• Simply capturing the last_seen time point cannot distinguish between "accidental online activity" and "continuous activity".
• Ignoring login frequency and online duration can easily lead to misjudging a single login account as a daily active user.
• Without dynamic verification, it is impossible to identify "fake active" accounts simulated by scripts.
• The system only outputs "yes/no" daily active users, without any tier classification, which is not conducive to user segmentation.

• Statistics on login frequency over the past 3 and 7 days were compiled, categorizing them into levels such as "multiple logins per day" and "occasional logins".
• Determine account usage depth by combining online time and number of session switching.
• Verify login IP and device stability to exclude abnormal accounts that are actively simulating cloud control activity.
• Output tags such as "high active daily active users," "normal daily active users," and "low daily active users" to adapt to different operational strategies.

II. Behavioral Tag Coverage: Basic Attributes vs. In-depth Behavioral Profiling

• Only basic information such as profile picture, nickname, and group membership are detected.
• Without interactive behavior analysis, it is impossible to determine whether a user sends a message or participates in a group chat.
• Independent tags without cross-validation can easily lead to invalid accounts that have profile pictures but no interaction.
• Without interest tags, marketing content cannot be matched with user needs.

• Coverage of messaging interactions: private chat reply rate, group chat speaking frequency, message length, etc.
• Content-related behaviors include: channel browsing, link clicking, content forwarding, and content saving.
• Overlaying social attributes: number of groups, types of interactive groups, frequency of administrator interaction
• Generate combined tags, such as "high-frequency interaction + cryptocurrency interest + daily active users," to target high-intent users.

III. Abnormal Account Identification: Superficial Filtering vs. Comprehensive Risk Control Verification

• It only judges "unregistered" and "empty number", but cannot identify virtual numbers or SMS verification code platform accounts.
• Without risk control verification, it is unable to identify abnormal accounts that have been banned or frequently reported.
• The inability to distinguish between personal accounts, marketing accounts, and bot accounts can easily trigger platform restrictions.
• A dormant account that was "abandoned but not cancelled" was mistakenly identified as a valid user.

• Built-in database of virtual phone numbers and SMS verification code platforms, automatically identifies non-real accounts.
• Check account risk control status: ban flags, report records, abnormal IP/device switching.
• Excluding bot accounts that are manually operated to ensure user authenticity.
• Based on registration duration and activity decay curves, "zombie accounts" and "abandoned accounts" are eliminated.

IV. Batch Filtering Capability: Inefficient Single-Thread vs. High-Efficiency Intelligent Processing

• Processing small amounts of data at a time, such as tens of thousands of data entries, is time-consuming and inefficient.
• Without a concurrency mechanism, issues such as lag, missed detections, and incorrect results are likely to occur.
• The filtering criteria are fixed, and there is no way to customize multi-dimensional combinations, resulting in poor flexibility.
• The output format is limited and cannot be directly integrated with CRM or SCRM systems.

• Supports batch import of millions of phone numbers, runs fully automatically 24/7, and is 5-10 times faster.
• It supports custom filtering criteria, such as "daily active users + male + e-commerce interaction + non-virtual accounts".
• The structured output includes 20+ fields, such as activity level, behavioral tags, and risk level.
• Supports API integration, enabling a closed-loop process of "screening-stratification-reach".

V. Practical Value and Applicable Scenarios: Extensive Reach vs. Precise and Refined Operations

• Only suitable for initial screening of a small number of numbers during the cold start phase, reducing basic invalid outreach.
• Unable to support precision marketing and user segmentation, resulting in limited conversion rates.
• Long-term use is wasteful of resources, with high customer service and mass messaging costs and a low ROI.
• Without data accumulation, it is impossible to provide a reference for subsequent operations.

• Adaptable to scenarios such as large-scale user cleansing, refined community operations, and targeted advertising.
• Targeting highly active and high-intent users increases the reach success rate by over 60%.
• Reduce ineffective investment, lower platform risk, and improve long-term ROI.
• Accumulate behavioral data to support marketing strategy optimization.

Conclusion