Key Highlights
- In this success story, the media and entertainment client faced challenges in identifying the right social media influencers, verifying authenticity, and ensuring alignment with brand goals. The growing influencer ecosystem made it difficult to measure impact, relevance, and cost-effectiveness.
- OptiSol partnered with the client to build a machine learning–driven Social IQ platform that automatically aggregates, scores, and ranks influencers’ content in real time.
- Our solution pre-processed influencer tweets developed semantic indexing with Universal Sentence Encoder and ranked them across 13 social intelligence dimensions to deliver objective evaluations.
- The platform empowered businesses to discover authentic, relevant, and impactful influencers, while reducing manual effort, campaign costs, and improving brand success through data-driven selection.
Problem Statement
01
Identification: With an overwhelming number of influencers, selecting the right ones aligned to brand image and audience was a major challenge.
02
Authenticity: It was difficult to validate the credibility and suitability of influencers as trusted brand representatives.
03
Relevance: Ensuring an influencer’s content and followers matched business target audiences proved inconsistent and subjective.
04
Measuring Impact: Brand struggled to measure campaign effectiveness, ROI, and audience engagement reliably.
05
Cost: Evaluating and running influencer campaigns required high costs and significant manual effort.
Solution Overview
01
Tweets were pre-processed, and retweets were contextualized to ensure an accurate ranking.
02
A data aggregation pipeline was built to collect tweets from influencers who opted in for scoring via the Social IQ platform.
03
A semantic indexing model using Universal Sentence Encoder (USE) categorized influencer content into 13 dimensions of social intelligence.
04
Machine learning models scored and ranked the processed tweets, generating objective influencer ratings.
05
Scores and rankings were visualized in a robust, real-time dashboard, empowering businesses with actionable insights for influencer selection.
Business Impact
Process Flow: The Visual Story
About The Project
OptiSol collaborated with a client in the Media and Entertainment industry to modernize the influencer selection process using machine learning. The Social IQ platform automated tweet aggregation, semantic indexing, and scoring, enabling objective, scalable, and real-time evaluation of influencers. By aligning influencer performance with 13 social intelligence dimensions, the solution provided businesses with accurate, actionable insights for brand partnerships. The result was improved cost-efficiency, stronger campaign relevance, and enhanced brand impact in the competitive influencer marketing space.
Technology Stack:
FAQs:
How did OptiSol tackle influencer identification?
By creating an ML-driven scoring pipeline, OptiSol enabled the brand to filter influencers based on relevance, authenticity, and audience alignment.
What approach ensured tweet data accuracy?
Tweets were pre-processed and contextualized using NLP techniques, ensuring high-quality inputs for ranking and evaluation.
How did OptiSol address content categorization?
Using Universal Sentence Encoder, tweets were indexed across 13 social intelligence dimensions, enabling structured influencer evaluation.
How did OptiSol ensure objectivity in evaluation?
Machine learning models replaced subjective human judgments with unbiased, data-driven scoring metrics.
What solution was built for performance monitoring?
A real-time dashboard was developed to display influencer rankings, campaign performance, and engagement metrics for decision-makers.
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