Kolfind.com

The influencer marketing industry had a massive gap in the micro-influencer segment: Market Dynamics: - 90% of influencer marketing budgets go to 1% of influencers - Micro-influencers (1K-100K followers) have 7x higher engagement - SMBs couldn't afford traditional influencer marketing agencies - Manual influencer research took 40+ hours per campaign Brand Pain Points: - No efficient way to find niche micro-influencers - Fake followers and engagement pods everywhere - Difficult to verify audience demographics - No standardized outreach process - Campaign ROI tracking was nearly impossible

We built an AI-powered discovery engine that automates the entire influencer finding process: Core Components: - Web scraping infrastructure for social media data - ML models for fake follower detection - NLP for content categorization - Audience demographic prediction - Automated outreach system - Campaign performance tracking

Influencer Discovery Algorithm We built a sophisticated influencer scoring system that analyzes multiple data points:

# Influencer scoring algorithm
class InfluencerScorer:
    def calculate_score(self, profile):
        # Engagement rate calculation
        engagement_rate = (
            profile['likes'] + profile['comments'] * 2
        ) / profile['followers']
        
        # Authenticity check
        authenticity_score = self.check_authenticity(
            followers_growth=profile['growth_rate'],
            engagement_pattern=profile['engagement_pattern'],
            comment_quality=profile['comment_sentiment']
        )
        
        # Niche relevance
        niche_score = self.calculate_niche_match(
            profile_keywords=profile['bio_keywords'],
            post_hashtags=profile['hashtags'],
            target_niche=self.target_niche
        )
        
        # Composite score
        return {
            'overall': (engagement_rate * 0.4 + 
                       authenticity_score * 0.3 + 
                       niche_score * 0.3),
            'breakdown': {
                'engagement': engagement_rate,
                'authenticity': authenticity_score,
                'relevance': niche_score
            }
        }

Data Processing Pipeline: - Instagram scraping with rate limiting - Natural language processing for bio analysis - Computer vision for content categorization - Real-time data updates every 24 hours Search Optimization: - Elasticsearch for full-text search - Pre-computed similarity scores - Faceted search with 20+ filters - Response time under 200ms for complex queries

Phase 1: Problem Validation (Days 1-3) Customer Discovery Process: 1. Initial Hypothesis: SMBs want affordable influencer marketing 2. Interview Setup: 50 marketing managers from SMBs 3. Key Questions Asked: - "How do you currently find influencers?" - "What's your biggest frustration with influencer marketing?" - "How much time/money do you spend on influencer discovery?" - "What would an ideal solution look like?" Findings: - 82% spent 20+ hours per campaign on research - 73% had been burned by fake influencers - 91% wanted micro-influencers but couldn't find them efficiently - Average willingness to pay: $200-500/month Phase 2: Solution Validation (Days 4-7) MVP Test: 1. Built: Simple landing page with value proposition 2. Promise: "Find 50 perfect micro-influencers in 5 minutes" 3. CTA: "Get Early Access - $49/month" 4. Traffic: $200 Facebook ads targeting SMB marketers Results: - 312 visitors - 47 email signups (15% conversion) - 12 pre-orders ($588 revenue) - Validated: Problem exists and people will pay Phase 3: Product Validation (Days 8-14) Concierge MVP: 1. Manually fulfilled orders for first 12 customers 2. Process: - Customer submits brand details - We manually research influencers (8 hours per customer) - Deliver spreadsheet with 50 influencers 3. Learnings: - Customers loved the results - Process was painful but revealed patterns - Identified automatable parts Key Insights: - Engagement rate more important than follower count - Niche relevance crucial for conversion - Brands wanted contact templates - Direct Instagram DMs had 3x response rate vs email Phase 4: Scaling Validation (Days 15-21) Automated MVP: 1. Built basic automation for discovered patterns 2. Reduced research time from 8 hours to 30 minutes 3. Tested with 25 new customers at $99/month 4. Results: - 20 customers retained after month 1 - NPS score: 72 - 5 customer referrals Phase 5: Market Sizing (Days 22-28) TAM Calculation: - 5M SMBs in the US - 20% do marketing (1M) - 10% interested in influencer marketing (100K) - 5% can afford our solution (5K) - $200 average price - TAM: $12M annually Competitive Analysis: - Existing solutions focused on enterprise ($5K+/month) - No direct competitors in SMB segment - Adjacent tools (social media management) didn't solve discovery GTM Strategy Validation: 1. Channel Tests: - Facebook Ads: CAC $45, LTV $800 ✅ - Google Ads: CAC $120, LTV $800 ✅ - Content Marketing: Slow but promising - Partnerships: Agency referrals showed potential 2. Pricing Tests: - $49/month: 8% conversion, high churn - $99/month: 6% conversion, low churn ✅ - $199/month: 2% conversion, very low churn Decision Framework: ✅ Green Lights: - Clear problem with quantifiable pain - Willingness to pay validated - TAM sufficient for venture scale - Acquisition channels identified - Product can be built in 4 weeks ⚠️ Yellow Lights: - Instagram API restrictions - Potential platform changes - Competitive moat questions 🚫 Red Lights: - None identified Final Validation Metrics: - Problem-Solution Fit: ✅ (NPS 72) - Product-Market Fit: ✅ (40% would be disappointed) - Business Model Fit: ✅ (LTV:CAC = 17:1) - Founder-Market Fit: ✅ (Marketing expertise) Go/No-Go Decision: GO ✅