Marketing Personalization · Sales Automation
Metro Pacific Investments Corporation, Manila Electric Company, and PLDT wanted a compliant way to unify fragmented cross-industry datasets so they could generate actionable insights, automation, and advanced analytics instead of leaving valuable customer data siloed across their businesses.
Reported impact
Káyana created a large-scale, regulation-aware data foundation that improved customer understanding and turned it into measurable commercial results, including more precise segmentation, stronger online acquisition performance, and higher lead generation while helping affiliated businesses deliver more tailored consumer experiences.
Káyana was established with Bain as a digital and data company that unifies datasets from major Philippine businesses and turns them into AI-driven growth programs. The deployment starts by bringing together data from Metro Pacific Investments Corporation, Manila Electric Company, and PLDT, then applying large-scale enrichment to create a shared dataset. Káyana uses that data estate to build more than 200 customer micro-segments and cross-industry narratives, especially around health, wellness, and digital services, so business teams can better understand consumer needs. Those segments then feed an AI-powered online acquisition campaign and a machine-learning-based lead acquisition engine that optimize targeting and outreach. The result is a production workflow that moves from multi-company data integration to enriched segmentation, then into AI-driven acquisition and lead generation programs that materially improve conversion and customer engagement.
Datasets from Metro Pacific Investments Corporation, Manila Electric Company, and PLDT are unified and enriched inside Káyana, which produces more than 200 customer micro-segments and cross-industry narratives. Those outputs feed an AI-powered acquisition campaign and a machine-learning-based lead engine that drive tailored outreach and improved lead conversion.
"An AI-powered online acquisition campaign delivered 2–3x higher conversion rates"
"a machine-learning-based lead acquisition engine delivered a 2x uplift"
"an AI-enabled digital and data company"
"creating over 200 micro-segments"
"enable data- and AI-informed insights, automation, and advanced analytics across their domains"
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Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
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