New user activation diagnosis and optimization

What to prepare

New user activation diagnosis and optimization Focusing on a clear definition of activation (a new user completes a core behavior within N days after registration), perform data quality inspection, bottleneck location, time trend and group diagnosis, cause evidence grading and experimental design on standardized funnel data, and output an executable activation optimization decision brief. Suitable for businesses such as SaaS, App, e-commerce, content products, communities and API platforms. The final output is a complete Markdown diagnostic report, covering seven sections: data quality, funnel evidence, time diagnosis, group diagnosis, cause hypothesis, experimental design, and decision briefing. Upload data format (CSV): long table structure, columns = period, segment_type, segment_value, stage_order, stage_name, user_count - period: observation period (such as 2026-01 / 2026-W27) - segment_type: all or segmentation dimension (such as channel / platform) - segment_value: fill in all when all; fill in specific value when dividing into groups (such as official website / iOS) - stage_order: funnel step sequence number (starts from 1, must be monotonic) - stage_name: Funnel step name (such as completed registration / first effective behavior) - user_count: the number of users in this cycle-group-step Contains at least 2 periods; contains 1 row of segment_type=all, segment_value=all for the overall summary (allowed to be equal to the sum of each segment); if segment diagnosis is required, please add the corresponding row.

Activation definition and time windowRequired

Explain what behavior represents the user's acquisition of core value, and how long it takes after registration to complete the activation.

Product and user backgroundRequired

Explain the problem the product solves, the target users, and why users need to complete the activation behavior.

Activate funnel dataRequired

Upload activation funnel data summarized by period, step, and optional segmentation. CSV is recommended.

Known business changesOptional

Supplement product revisions, activities, failures, or changes in coverage during the observation period. None to leave blank.

User feedback or problem recordsOptional

You can upload interviews, questionnaires, customer service tickets, or error summaries to verify cause hypotheses. None to leave blank.

Generation instructions

Each row is submitted as a separate task. Required fields are marked with *.

• Each row represents a new user activation diagnostic task for a product during an observation period.
• The activation definition must describe both the core value behavior and the time window.
• It is recommended to use long table CSV for funnel data, which contains at least period, stage_order, stage_name, and user_count.
• It is recommended to include more than 2 cycles; if group diagnosis is required, please increase segment_type and segment_value.
• This version analyzes aggregated funnel data, does not process millions of rows of original buried logs, and does not automatically execute SQL.