SayPro Monthly January SCMR-5 SayPro Monthly Classified User Insights: Analyse user behaviour and engagement metrics by SayPro Classified Office under SayPro Marketing Royalty SCMR
Objective:
Segment users based on demographics and behaviors to target ads more effectively, using insights from SayPro Monthly January SCMR-5 and the SayPro Monthly Classified User Insights report.
Tasks Breakdown:
Week 1: Data Gathering and Initial Analysis
- Access SayPro Monthly January SCMR-5 Report:
- Review the data from the January SCMR-5 report to get an overview of user engagement trends across different platforms (web, mobile).
- Extract insights related to ad performance, user behavior, and engagement metrics for different user categories.
- Gather User Demographic Data:
- Compile demographic information (age, gender, location, etc.) from SayPro’s data sources.
- Ensure that the user demographic data is comprehensive and up to date for accurate segmentation.
- Behavioral Data Collection:
- Identify and extract behavioral metrics (e.g., click-through rate, time spent on site, frequency of interactions) from the Classified Office user insights.
- Collect data on user actions such as clicks, impressions, and conversions for different types of ads (e.g., category-specific or location-based).
- Data Cleansing:
- Cleanse the data by removing duplicates, correcting errors, and ensuring consistency across datasets.
- Normalize data to ensure all user behaviors are comparable across segments.
- Initial Segmentation Setup:
- Begin sorting users into broad categories based on basic demographic criteria (age, gender, geographic location).
- Map user behaviors to these categories to determine high-level engagement patterns.
- Set Up Analytics Tools for Ongoing Tracking:
- Ensure analytics tools (Google Analytics, CRM systems, etc.) are configured to track engagement metrics for each segment.
- Set up dashboards for visualizing user engagement across different segments.
Week 2: Deep Dive into Segmentation and Refinement
- Detailed Segmentation Based on Behavior:
- Use the insights from the previous week to create granular user segments based on detailed behavioral patterns (e.g., frequent visitors, one-time users, active converters).
- Consider incorporating psychographic segmentation (interests, lifestyle preferences) where possible to refine targeting efforts.
- Analyze Interaction Patterns by Category:
- Look into specific user engagement with various ad categories (e.g., cars, jobs, real estate).
- Segment users by category preference and measure their engagement with specific ad types within these categories.
- Refinement of Demographic Segments:
- Refine demographic segments by incorporating behavioral insights. For example, younger users in a specific region might engage with fashion-related ads more than other categories.
- Create detailed profiles of high-value user segments to target.
- Performance Analysis by Segments:
- Review the performance of targeted ads within different segments, measuring key performance indicators (KPIs) such as CTR (click-through rate), ROI (return on investment), and engagement rates.
- Identify underperforming segments and make adjustments to optimize the segmentation.
- Segmentation Model Testing:
- Test the effectiveness of the segmented user profiles by running small-scale targeted campaigns for specific segments.
- Collect feedback on ad performance to further adjust targeting strategies.
- Report on Findings:
- Summarize the segmentation process and the resulting user insights.
- Provide a detailed report that outlines which user segments perform best and recommend actions for optimizing ad targeting moving forward.
Deliverables:
- Segmentation Report detailing:
- User demographics, engagement metrics, and identified behavior patterns.
- Segmented user profiles and their engagement rates.
- Targeting Strategy based on the segmentation model.
- Performance Dashboards for tracking ad performance across different segments.
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