BrandShadow: Social Media Reputation Monitoring and Analysis Platform

Led product strategy and delivery for BrandShadow, a PaaS solution that aggregated online brand mentions, applied linguistic and influence analysis, and provided analysts with dashboards and reporting tools for client presentations. Owned the business unit, managed cross functional delivery, and delivered capabilities that enabled customers to understand sentiment, trends, and influential voices across large volumes of online data.

Context and objectives

• Product domain: Social media reputation monitoring, linguistic analysis, influencer scoring, trend reporting
• Customer base: Marketing agencies, brand managers, enterprise marketing teams
• Business drivers: Provide actionable insights from large scale online conversations; support client reporting; differentiate through analytics depth
• Constraints: High volume data ingestion, third party data sources, need for intuitive dashboards and exportable reports
• Success criteria: Deliver accurate sentiment and influence analysis, provide analyst ready dashboards, support client presentations, and maintain reliable data ingestion

Role and scope

• Title: Product Manager / Business Unit Owner
• Scope: Owned product strategy, roadmap, backlog, partner integrations, and cross functional delivery
• Responsibilities: Product management, product marketing, partner sourcing, client presentations, reporting, and business operations

Problem statement

Brands struggled to understand the volume, tone, and influence of online conversations across social platforms and blogs. Existing tools lacked actionable insights, required manual analysis, or failed to provide exportable reporting suitable for client presentations.

Approach and strategy

• Discovery: Interviewed analysts, reviewed client reporting workflows, and analyzed gaps in existing monitoring tools
• Prioritization: Focused on high value analytics (sentiment, influence scoring, trend detection) and exportable reporting
• Roadmap: Sequenced ingestion improvements, analytics enhancements, and dashboard UX updates to support analyst workflows

Execution and key activities

• Defined product requirements for data ingestion, linguistic analysis, influence scoring, and dashboard reporting
• Sourced and integrated third party partners for data collection and analytics augmentation
• Designed dashboards enabling analysts to filter, segment, and extract insights for client presentations
• Delivered reporting capabilities including charts, sentiment summaries, influencer lists, and trend visualizations
• Presented insights directly to clients and supported agency teams in preparing deliverables
• Managed business operations including pricing, partner relationships, and reporting cadence

Technical architecture and analytics

• Data ingestion: Aggregated mentions from social networks, blogs, and news sources
• Analytics:
• Linguistic sentiment analysis
• Influence scoring based on network reach and engagement
• Trend detection across time windows
• Dashboards: Interactive charts, filters, sentiment summaries, influencer lists
• Reporting: Exportable charts and summaries for client presentations

Deliverables and artifacts

• Product requirements and backlog
• Dashboard wireframes and UX flows
• Partner integration specifications
• Reporting templates and client ready visualizations
• Business unit reporting and performance metrics

Results

• Delivered a functioning PaaS analytics platform supporting agency reporting workflows
• Enabled analysts to extract insights and generate client ready reports more efficiently
• Improved visibility into brand sentiment, influential voices, and emerging trends
• Strengthened agency relationships through more compelling, data driven presentations

Challenges and mitigations

• Data variability: Implemented normalization and filtering to improve analysis accuracy
• Partner reliability: Evaluated multiple providers and selected stable, scalable sources
• Analyst workflow complexity: Designed dashboards around real reporting use cases
• High volume data: Prioritized performance optimizations and efficient indexing

Lessons Learned

• Analyst first design ensures adoption and value
• Third party integrations require strong SLAs and monitoring
• Exportable reporting is essential for agency workflows
• Next steps: deeper sentiment models, real time alerts, expanded influencer analytics