This curriculum spans the design and operationalization of a multi-workshop–scale analytics program, equipping teams to build and maintain a data-driven social media function comparable to an internal capability developed through a multi-phase advisory engagement.
Defining Strategic Objectives and KPIs for Social Media Performance
- Selecting KPIs that align with business outcomes, such as lead conversion rate versus engagement rate, based on organizational priorities
- Determining whether to prioritize reach, sentiment, or share of voice depending on brand maturity and competitive landscape
- Establishing baseline metrics before campaign launch to enable accurate measurement of incremental impact
- Aligning social media KPIs with cross-functional goals in marketing, customer service, and product development
- Deciding on the frequency and format of performance reporting to executive stakeholders
- Choosing between absolute metrics (e.g., total likes) and relative metrics (e.g., engagement per follower) for trend analysis
- Integrating qualitative brand perception goals with quantifiable social metrics in objective setting
Data Collection Architecture and Platform Integration
- Selecting APIs (e.g., Meta Graph, X/Twitter API v2, LinkedIn) based on data depth, rate limits, and compliance requirements
- Designing ETL pipelines to consolidate data from multiple platforms into a unified data warehouse schema
- Implementing OAuth protocols to securely authenticate access to brand social accounts at scale
- Deciding between real-time streaming and batch processing based on use case urgency and infrastructure cost
- Managing data retention policies to comply with regional privacy regulations (e.g., GDPR, CCPA)
- Handling missing or inconsistent data fields across platforms, such as varying definitions of "impressions"
- Validating data integrity through automated reconciliation checks between platform dashboards and internal databases
Competitive Benchmarking and Market Positioning Analysis
- Identifying relevant competitors for benchmarking, including direct rivals and aspirational brands in adjacent categories
- Normalizing engagement metrics by audience size to enable fair cross-brand comparisons
- Tracking share of voice within industry-specific hashtags and trending topics over time
- Mapping competitor content cadence and format distribution to inform brand content strategy
- Assessing sentiment differentials between brand and competitor mentions during product launches
- Using geo-tagged data to evaluate regional performance gaps compared to local market leaders
- Deciding whether to include earned media (e.g., influencer posts) in competitive share of voice calculations
Sentiment Analysis and Brand Perception Modeling
- Selecting between rule-based, lexicon-driven, and machine learning models for sentiment classification based on domain specificity
- Customizing sentiment lexicons to reflect industry jargon and brand-specific slang (e.g., "sick" as positive in youth markets)
- Handling sarcasm and context-dependent language in short-form content using contextual embeddings
- Validating model accuracy through manual annotation sampling and inter-rater reliability checks
- Segmenting sentiment by audience cohort (e.g., customers vs. critics) to identify perception disparities
- Integrating sentiment trends with customer support data to detect emerging service issues
- Updating training datasets quarterly to reflect evolving language use and cultural references
Content Performance Attribution and Optimization
- Designing A/B tests for creative elements (e.g., visuals, CTAs, posting times) with statistically valid sample sizes
- Attributing downstream conversions to specific content formats using multi-touch modeling
- Quantifying the halo effect of viral organic content on paid campaign performance
- Identifying high-performing content clusters using topic modeling and engagement correlation
- Adjusting content strategy based on diminishing returns in engagement for overused formats
- Measuring carryover effects of campaign content beyond its active posting window
- Isolating the impact of external events (e.g., news cycles) on content performance for accurate attribution
Influencer and Advocacy Network Analytics
Real-Time Monitoring and Crisis Detection Systems
- Setting dynamic thresholds for anomaly detection in volume and sentiment to trigger alerts
- Integrating social listening feeds with incident response workflows in customer operations
- Validating potential crisis signals against duplicate reporting and bot activity
- Defining escalation protocols based on severity, velocity, and audience reach of negative spikes
- Archiving real-time data streams for post-crisis root cause analysis and audit purposes
- Coordinating with legal and PR teams on data retention and disclosure policies during incidents
- Conducting red-team exercises to test detection sensitivity and response latency
Longitudinal Brand Health Tracking and Dashboard Design
- Designing dashboard hierarchies that balance strategic overview with drill-down capability for root cause analysis
- Choosing visualization types (e.g., control charts, heatmaps) based on data distribution and stakeholder needs
- Scheduling automated refresh cycles to ensure data currency without overloading database resources
- Implementing role-based access controls to restrict sensitive data (e.g., competitor benchmarks) to authorized users
- Versioning dashboard logic to track changes in metric definitions over time
- Embedding statistical significance indicators in trend visualizations to prevent overreaction to noise
- Archiving historical dashboard states to support retrospective performance reviews
Cross-Channel Integration and Attribution Modeling
- Mapping social media touchpoints to customer journey stages using timestamped interaction data
- Allocating credit across channels using data-driven models (e.g., Shapley value) versus last-click rules
- Reconciling discrepancies in user identification across walled gardens (e.g., Meta, TikTok)
- Estimating incrementality of social campaigns using geo-lift or holdout group designs
- Integrating social engagement data with CRM systems to personalize downstream communications
- Assessing channel interdependencies, such as social driving search volume for branded terms
- Reporting on assisted conversions where social appears in multi-touch paths but not as last click