Building Influencer Marketing Anayltics for Top Apparel Brands

Freestak is an influencer marketing platform that helps brands discover, manage, and measure partnerships with endurance sports creators. The platform enables businesses to evaluate campaign performance, compare influencer engagement, and make data-driven marketing decisions through centralized analytics. 

As the platform expanded its campaign portfolio, delivering timely and reliable performance insights became increasingly important. Creating a consistent analytics foundation helped marketing teams evaluate creators more effectively while supporting faster campaign optimization and reporting.

Campaign performance data was distributed across Instagram and Facebook, requiring manual collection from multiple sources before meaningful analysis could begin. Incomplete content information and inconsistent reporting formats made it difficult to compare campaigns and measure influencer performance with confidence. 

Key Issues 

• Collecting engagement metrics from Instagram and Facebook at scale  
• Consolidating campaign data into a consistent reporting structure  
• Recovering missing captions that reduced content context  
• Managing media assets alongside campaign analytics  
• Delivering analytics-ready datasets for downstream dashboards  
• Reducing the manual effort required for recurring campaign reporting  

Without an automated reporting pipeline, campaign analysis was dependent on manual exports and repetitive data preparation. This led to delayed optimization decisions and reduced confidence in performance reporting. 

An automated analytics workflow was developed to collect, enrich, and organize campaign performance data from Instagram and Facebook. The architecture converted fragmented social metrics into modeled datasets that supported creator comparison, campaign evaluation, and dashboard reporting.

Social Media Data Collection

Python workflows retrieved campaign metrics, post details, stories, and engagement data directly from the Meta Graph API. This replaced recurring manual exports with a repeatable collection process across connected influencer accounts.

AI-Powered Content Enrichment

OpenAI multimodal models analyzed media and thumbnails when native captions were unavailable. OCR-based extraction recovered useful text and reduced gaps in the context available for campaign analysis.

Structured Cloud Storage

Engagement metrics, media metadata, and content information were normalized into consistent JSON records before being synchronized to Amazon S3. Influencer-level storage paths created predictable inputs for downstream processing and supported validation, backfills, and reprocessing.

Analytics Transformation & Modeling

dbt transformed the S3-sourced campaign data into tested analytical models in Snowflake. Standardized warehouse tables created a reliable reporting layer for comparing influencers, evaluating content performance, and measuring campaign outcomes.

Dashboard-Ready Reporting

Modeled Snowflake datasets supplied the reporting and dashboard layer with consistent performance metrics. This gave campaign teams a clearer view of engagement, creator activity, and marketing results across brand partnerships.

Key Deliverables

• Integrated Instagram and Facebook analytics through the Meta Graph API
• Automated campaign metric collection across influencer accounts
• Added AI-powered caption recovery for content with missing text
• Standardized social metrics and media metadata into reusable data contracts
• Synchronized campaign records and media assets to Amazon S3
• Developed dbt transformations for analytics modeling
• Loaded reporting-ready models into Snowflake
• Prepared consistent datasets for campaign dashboards and performance analysis

Faster Campaign Reporting

Recurring data collection reduced dependence on manual exports and made social performance information available through a repeatable reporting workflow.

More Complete Content Context

AI-assisted caption recovery filled gaps in posts and stories with missing text, giving campaign teams more useful information for evaluating creator content.

Consistent Performance Measurement

dbt models standardized engagement and media data in Snowflake, allowing influencer and campaign performance to be compared through consistent business metrics.

Enterprise Brand Onboarding

Reliable reporting infrastructure helped Freestak onboard and support international brands including Under Armour, Nike, and New Balance.

Scalable Analytics Delivery

Structured S3 inputs and modeled Snowflake datasets created a repeatable foundation for supporting additional influencer accounts, campaigns, and dashboard requirements.