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Listen "EP 11 — Generac's Neil Bhandar On Speaking Data Fluently Across Marketing, HR & Supply Chain"
Episode Synopsis
At Generac, Neil Bhandar expans his 20+ year career into IT. Previously at P&G, JPMorgan, and other companies, he operated within business functions. He's a business executive who works in data, not the other way around. This explains how he sees the same sawtooth pattern in inventory replenishment and credit card balances, and launched Tide Coldwater to 4% market share gains in under four weeks.
At P&G during the second Bush administration, oil jumped from $45 to $105 per barrel. His team positioned an enzyme-activated detergent as consumer cost savings, combined it with down-counting to convert Tide from a retail loss leader into a profitable product, then targeted only retailers with high category close rates and golden households. Four weeks later: 4% market share gain for a $7 billion brand.
At JPMorgan, he used organizational network analysis to find which business units weren't communicating. When he tested whether "happy employees make happy customers," he found no data that validated it. His book The Cost of Curiosity addresses analytics' broken economics where each follow-up question costs as much as the first. His biggest AI concern isn't hallucination but the sea of sameness when competitors use identical foundation models.
Topics discussed:
Three-pronged retail strategy delivering 4% market share gains in four weeks for $7 billion brand
Down-counting technique converting retail loss leader products into profitable SKUs with built-in retailer margin
Targeting golden households doing multiple loads per day through high close-rate retail channels
Applying sawtooth geometric patterns across inventory optimization and credit card balance modeling domains
Using organizational network analysis to surface invisible communication gaps at 250,000-person institutional scale
Testing whether "happy employees make happy customers" assumption holds across full employee survey dataset
Moving between supply chain, marketing, financial services, and HR by modulating technical accent per audience
Sea of sameness risk when competitors deploy identical pre-trained foundation models without differentiation
Resonance effects from multiple autonomous AI agents built on same models making independent decisions
Reducing marginal cost of answering analytical questions to approach zero through Alexa-like interaction model
At P&G during the second Bush administration, oil jumped from $45 to $105 per barrel. His team positioned an enzyme-activated detergent as consumer cost savings, combined it with down-counting to convert Tide from a retail loss leader into a profitable product, then targeted only retailers with high category close rates and golden households. Four weeks later: 4% market share gain for a $7 billion brand.
At JPMorgan, he used organizational network analysis to find which business units weren't communicating. When he tested whether "happy employees make happy customers," he found no data that validated it. His book The Cost of Curiosity addresses analytics' broken economics where each follow-up question costs as much as the first. His biggest AI concern isn't hallucination but the sea of sameness when competitors use identical foundation models.
Topics discussed:
Three-pronged retail strategy delivering 4% market share gains in four weeks for $7 billion brand
Down-counting technique converting retail loss leader products into profitable SKUs with built-in retailer margin
Targeting golden households doing multiple loads per day through high close-rate retail channels
Applying sawtooth geometric patterns across inventory optimization and credit card balance modeling domains
Using organizational network analysis to surface invisible communication gaps at 250,000-person institutional scale
Testing whether "happy employees make happy customers" assumption holds across full employee survey dataset
Moving between supply chain, marketing, financial services, and HR by modulating technical accent per audience
Sea of sameness risk when competitors deploy identical pre-trained foundation models without differentiation
Resonance effects from multiple autonomous AI agents built on same models making independent decisions
Reducing marginal cost of answering analytical questions to approach zero through Alexa-like interaction model
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