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Honeylove, a fashion-tech company, shares how they use Google BigQuery and Gemini to unify data, automate insights, and improve product quality and service efficiency.
•Consolidated fragmented data sources (Shopify, email, ads) into a single BigQuery platform, eliminating manual silos and enabling AI/ML adoption.
•Built BigQuery ML contribution analysis models to identify key drivers of conversion rate, customer satisfaction, website performance, and return rates.
•Automated pre-meeting dashboard reviews with Gemini-generated reports, saving hundreds of hours per year across 10–15 team members.
•Deployed ARIMA univariate forecasting models for SKU-level demand planning, achieving within 5% accuracy vs. 20–30% error from third-party vendors.
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Used Gemini embedding models and BigQuery vector search with RAG to extract actionable insights from customer service tickets, saving ~30 seconds per ticket.
This summary was automatically generated by AI based on the original article and may not be fully accurate.
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