SayPro Monthly Data Compilation Process

Prepared by: SayPro Marketing & Monitoring & Evaluation (M&E) Teams
Date: [Insert Date]
Prepared for: Senior Management, Marketing Team, and M&E Team

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1. Purpose of Data Compilation

The purpose of this process is to ensure that all relevant data from marketing campaigns, website performance, and other key sources are systematically collected, organized, and analyzed. This will enable SayPro to track key metrics, assess marketing performance, and drive continuous improvement across all marketing activities.

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2. Data Sources to be Collected

The following are the key data sources that will be compiled and organized for analysis during each monthly cycle:

Marketing Campaign Data

• Campaign Metrics: Data from all active marketing campaigns, including:
  • Impressions
  • Click-through Rate (CTR)
  • Conversion Rate
  • Lead Generation (e.g., form submissions, sign-ups)
  • Cost per Lead/Acquisition (CPL/CPA)
  • Return on Investment (ROI)
  • Ad Spend vs Revenue
  • Engagement Rates (social media likes, shares, comments)
  • A/B Testing Results (if applicable)

Website Performance Data

• Traffic Metrics: Data from web analytics tools (e.g., Google Analytics, internal tracking systems):
  • Sessions and Pageviews
  • Unique Visitors
  • Bounce Rate
  • Average Session Duration
  • Traffic Sources (Organic, Paid, Referral, Social, etc.)
  • Top Landing Pages
  • Exit Pages
  • Conversion Tracking (including form submissions, purchases, and other key conversions)
  • Goal Completions (e.g., sign-ups, downloads)
  • User Flow (tracking the path users take through the site)

Customer Interaction Data

• Social Media Analytics: Metrics from all social media platforms (e.g., Facebook, Instagram, LinkedIn, Twitter):
  • Engagement Metrics (likes, shares, comments)
  • Follower Growth
  • Post Reach and Impressions
  • Sentiment Analysis (positive/negative feedback)
• Customer Support and Feedback:
  • Customer Satisfaction Score (CSAT)
  • Net Promoter Score (NPS)
  • Customer Feedback (from surveys, reviews, or support interactions)

Sales and Conversion Data

• Sales Metrics: Data related to product or service sales generated via marketing activities:
  • Revenue Generated
  • Conversion Rates from Marketing Channels
  • Sales Funnel Analysis (steps in the conversion process, drop-off rates)
  • Customer Lifetime Value (CLV) (if applicable)

Marketing Tools and Platform Metrics

• Email Campaign Data: Metrics for email marketing performance, including:
  • Open Rate
  • Click-through Rate (CTR)
  • Unsubscribe Rate
  • Bounce Rate (email-specific)
  • Spam Complaint Rate
• Paid Media: Data from paid campaigns (e.g., Google Ads, Facebook Ads):
  • Click-through Rate (CTR)
  • Conversion Rate
  • Cost per Click (CPC)
  • Impressions
  • Budget Spent

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3. Data Collection Process

The data collection process should be organized on a regular basis to ensure accuracy and timeliness. The following steps outline the process:

Step 1: Gather Data from Marketing Platforms

• Campaign Management Tools: Collect all performance data from platforms such as Google Ads, Facebook Ads Manager, LinkedIn Campaign Manager, etc.
• Email Marketing Tools: Export performance data from email marketing platforms like Mailchimp, HubSpot, or other ESPs.
• Social Media Analytics Tools: Gather insights from native platforms (e.g., Facebook Insights, Twitter Analytics, Instagram Insights) or third-party tools (e.g., Sprout Social, Hootsuite).

Step 2: Collect Website Performance Data

• Google Analytics: Review and export data related to website traffic, engagement, conversion rates, and user behavior.
• Google Search Console: Gather data on search performance, keyword rankings, and site health.
• Custom Analytics Tools: If applicable, gather any proprietary data tracked via internal tools or custom software.

Step 3: Collect Sales & Conversion Data

• CRM System: Export data related to leads generated, deals closed, revenue, and conversion performance from CRM platforms like Salesforce, HubSpot, etc.
• E-commerce Analytics: For e-commerce businesses, extract sales data from platforms like Shopify, WooCommerce, etc.

Step 4: Customer Interaction Data

• Survey Tools: Collect feedback from surveys sent to customers (e.g., Google Forms, Typeform, SurveyMonkey).
• Customer Support Tools: Extract data from customer support platforms (e.g., Zendesk, Freshdesk) to assess satisfaction, ticket resolution times, and overall customer sentiment.
• Social Listening: Collect customer sentiment analysis data from social listening platforms (e.g., Brandwatch, Hootsuite, Mention).

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4. Data Organization Process

Once the data is collected from various sources, the following steps will be followed to organize and structure it for analysis:

Step 1: Centralized Data Repository

• Create a Master Data Sheet: Use a centralized tool (e.g., Google Sheets, Excel, Airtable) to consolidate data from all sources into a single document. Each source of data should have its own section for easy navigation.
  • Example Sections:
    • Campaign Metrics
    • Website Analytics
    • Customer Interaction Data
    • Sales Data
    • Email Marketing Data
    • Social Media Analytics

Step 2: Categorize and Tag Data

• Tag by Time Period: Ensure all data is categorized by the appropriate reporting period (e.g., monthly, quarterly).
• Tag by Source: Each dataset should be clearly labeled to specify which marketing channel or tool it was derived from (e.g., “Google Ads”, “Instagram”, “Email Campaign”).

Step 3: Data Validation

• Check for Consistency: Ensure that all collected data aligns with expected results, making sure there are no discrepancies or outliers.
• Confirm Source Accuracy: Verify the accuracy of the data provided from each tool or platform by cross-checking with native sources.

Step 4: Calculate Derived Metrics

• Aggregation of Data: Aggregate individual metrics (e.g., total traffic, total conversions) and calculate derived metrics such as:
  • ROI (Return on Investment)
  • Customer Acquisition Cost (CAC)
  • Cost per Lead (CPL)
  • Lead Conversion Rate
• Segmentation: Break down the data based on specific segments like traffic source, campaign type, device (mobile/desktop), location, etc., to provide more granular insights.

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5. Reporting and Analysis

After organizing and validating the data, it will be used for monthly reporting. Key activities in this phase include:

Step 1: Key Metrics Dashboard

• Generate Monthly Dashboard: Create a visual representation of key metrics from the data (e.g., in Power BI, Google Data Studio, or Tableau).
  • Metrics to Display: Sessions, CTR, ROI, Conversion Rate, Engagement Rate, etc.

Step 2: Comparative Analysis

• Compare Performance Against Targets: Benchmark the data against pre-established KPIs and goals for the month.
• Identify Trends: Highlight trends, successes, and areas that require attention.

Step 3: Insights & Recommendations

• Actionable Insights: From the analysis, identify areas where marketing strategies can be improved, such as underperforming campaigns, audience targeting issues, or opportunities for new channels.
• Recommendations: Provide recommendations for optimization based on data trends and observations.

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6. Conclusion & Next Steps

• Compile Final Monthly Report: Prepare a comprehensive report for senior management and stakeholders that includes:
  • Summary of Marketing Performance
  • Website and Conversion Metrics
  • Key Campaign Insights
  • Customer Feedback and Sentiment
  • Suggested Actions for Future Campaigns
• Next Steps: Share the final report with the marketing and M&E teams to ensure strategic adjustments for the upcoming month.

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This SayPro Monthly Data Compilation process will help maintain organized, accurate, and actionable data that guides marketing decisions, optimizes performance, and enhances the overall customer experience.