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Targeted Advertising in Big Data

Targeted Advertising in Big Data

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This curriculum spans the technical and operational complexity of a multi-workshop program, covering the full lifecycle of data-driven advertising from infrastructure and identity resolution to cross-platform campaign orchestration, comparable to the scope of an internal capability build within a large enterprise’s digital marketing function.

Module 1: Defining Advertising Objectives within Big Data Ecosystems

  • Selecting KPIs such as CTR, conversion rate, or ROAS based on campaign goals and aligning them with data collection capabilities
  • Determining whether to prioritize reach, frequency, or conversion in campaign design given data latency constraints
  • Mapping business objectives to measurable user behaviors in data pipelines (e.g., defining what constitutes a "conversion" in event tracking)
  • Choosing between last-touch, multi-touch, or algorithmic attribution models based on data completeness and stakeholder requirements
  • Establishing thresholds for statistical significance in A/B testing to avoid premature conclusions from noisy data
  • Deciding whether to build custom audience segments or rely on platform-native targeting options based on data granularity needs
  • Integrating offline sales data with online behavioral data for holistic campaign measurement
  • Balancing real-time bidding goals with long-term brand awareness objectives in campaign architecture

Module 2: Data Infrastructure for Advertising Workflows

  • Selecting between batch and streaming ingestion for user event data based on campaign response time requirements
  • Designing event schemas that support both real-time bidding and offline analytics without duplication
  • Implementing data partitioning strategies in data lakes to optimize query performance for audience segmentation
  • Choosing between cloud data warehouses (e.g., BigQuery, Redshift) and data lakes for storing advertising logs
  • Configuring data retention policies for raw event data versus aggregated metrics in compliance with privacy regulations
  • Building data lineage tracking to audit changes in audience definitions and targeting logic
  • Integrating server-side tracking with client-side SDKs to reduce reliance on third-party cookies
  • Managing schema evolution in event data to maintain backward compatibility in reporting pipelines

Module 3: Identity Resolution and Cross-Device Targeting

  • Implementing probabilistic vs. deterministic matching strategies for user identity resolution based on data availability
  • Designing fallback mechanisms for identity graphs when logged-in user data is unavailable
  • Integrating first-party identifiers (e.g., email hashes) with third-party device graphs while managing data leakage risks
  • Handling identity conflicts when a single device is used by multiple users
  • Choosing thresholds for match confidence scores in identity resolution to balance reach and accuracy
  • Managing the degradation of cross-device targeting due to privacy restrictions on IDFA, AAID, and web tracking
  • Building reconciliation processes between CRM data and advertising platform user lists
  • Designing opt-out propagation across identity resolution systems to comply with privacy requests

Module 4: Audience Segmentation and Lookalike Modeling

  • Defining behavioral cohorts based on recency, frequency, and monetary value thresholds from transaction logs
  • Implementing time-decay functions in audience scoring to prioritize recent engagement
  • Selecting features for lookalike modeling based on predictive lift and data availability across platforms
  • Validating lookalike model performance using holdout test groups before full deployment
  • Managing segment refresh frequency to balance data freshness with processing costs
  • Handling edge cases where seed audiences are too small or unrepresentative for modeling
  • Enforcing data access controls to prevent unauthorized use of sensitive audience segments
  • Documenting segment logic for auditability and stakeholder alignment

Module 5: Real-Time Bidding and Programmatic Integration

  • Designing bid request filtering logic to reduce unnecessary auction participation and control costs
  • Implementing real-time feature extraction from bid requests using stream processing frameworks
  • Integrating machine learning models into bidding decision engines with latency constraints under 100ms
  • Configuring bid shading algorithms to optimize cost-per-win in first-price auction environments
  • Managing frequency capping at the bidder level to prevent ad fatigue
  • Handling timeout and fallback strategies when real-time data enrichment fails during bidding
  • Monitoring impression win rates and adjusting bid landscapes based on competition patterns
  • Logging full bid request and response data for post-auction analysis and debugging

Module 6: Privacy, Compliance, and Data Governance

  • Implementing data minimization practices in advertising data pipelines to reduce regulatory exposure
  • Mapping data flows to identify where PII is processed and applying masking or tokenization
  • Configuring consent management platforms to enforce user opt-outs across advertising systems
  • Conducting DPIAs for high-risk processing activities such as behavioral profiling
  • Establishing data retention schedules for advertising logs in alignment with GDPR and CCPA
  • Designing audit trails for data access and usage in advertising platforms
  • Handling data subject access requests (DSARs) involving advertising identifiers and behavioral profiles
  • Implementing vendor risk assessments for third-party ad tech partners with data access

Module 7: Measurement, Attribution, and Incrementality Testing

  • Building counterfactual models to estimate baseline conversion rates for incrementality analysis
  • Designing geo-based lift studies with matched control and treatment regions
  • Integrating multi-touch attribution outputs with budget allocation systems
  • Handling cross-channel interaction effects in attribution modeling (e.g., search following display exposure)
  • Reconciling discrepancies between platform-reported metrics and internal tracking systems
  • Implementing survival analysis to account for conversion delay patterns in attribution windows
  • Selecting between rule-based and algorithmic attribution models based on data quality and interpretability needs
  • Validating attribution model assumptions using synthetic data or holdout campaigns

Module 8: Optimization and Machine Learning in Ad Delivery

  • Defining reward functions for reinforcement learning models in bid optimization (e.g., CPA vs. ROAS)
  • Managing feature drift in ML models due to changing user behavior or market conditions
  • Implementing online learning pipelines to update models with daily performance feedback
  • Designing A/B/n tests to compare ML-driven bidding strategies against rule-based baselines
  • Setting up model monitoring for prediction latency, data skew, and performance degradation
  • Handling cold-start problems for new creatives or audiences with limited historical data
  • Allocating exploration vs. exploitation budget in multi-armed bandit approaches to creative testing
  • Documenting model features, training data, and performance metrics for regulatory review

Module 9: Cross-Platform Orchestration and Campaign Management

  • Designing unified campaign calendars that coordinate messaging across social, search, and display channels
  • Implementing budget pacing algorithms to distribute spend evenly across campaign duration
  • Building automated suppression rules to prevent retargeting users who already converted
  • Integrating creative versioning with dynamic creative optimization (DCO) systems
  • Managing API rate limits and error handling when syncing campaigns across multiple ad platforms
  • Establishing escalation protocols for campaign anomalies such as sudden drop in impression volume
  • Creating standardized reporting templates that normalize metrics across platforms
  • Orchestrating coordinated shutdown of campaigns across platforms during brand safety incidents
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