David Kuehner

Directing the Customer Experience (CE) analytics strategy to drive data-driven decision-making, optimize retention programs, and elevate the B2B customer journey across the enterprise. Key Responsibilities & Focus Areas: Advanced Analytics & Modeling: Leading the development of predictive models, customer segmentation, and lifecycle behavior tracking to support proactive engagement and personalized service strategies. Team Leadership: Mentoring and developing a high-performing team of data analysts focused on uncovering insights from complex data sets and mapping the customer journey. Executive Reporting: Designing robust reporting ecosystems and BI dashboards (Power BI, Tableau) to provide stakeholders with real-time visibility into customer insights, service performance, and agent metrics (FCR, AHT). Cross-Functional Strategy: Partnering with operational and commercial leadership to translate raw data into actionable "test and learn" initiatives that improve overall customer satisfaction and business profitability.

Scope & Impact Lead enterprise operational analytics and reporting in support of the COO organization, including Lending, Deposits, Fraud, and Security. Serve as primary analytics partner to the Chief of Staff for the COO, delivering executive-level insights, scorecards, and performance reporting. Key Responsibilities Direct and mentor an analytics team responsible for data extraction, aggregation, validation, and reporting across multiple enterprise data sources. Design and deliver executive dashboards and scorecards highlighting operational performance, trends, and risk indicators. Identify opportunities to improve reporting efficiency, automation, and data quality across the organization. Architect analytics solutions aligned with regulatory, compliance, and governance standards. Lead roadmap execution for enterprise information delivery, including data warehouse, BI, analytics platforms, and data governance. Partner with senior leadership to translate business needs into scalable analytics capabilities. Foster a culture of continuous improvement, learning, and operational excellence.

Led advanced marketing analytics, consumer insights, and data science initiatives across multiple brands and channels. Served as a strategic analytics partner to marketing and executive leadership. Led and developed a team of 6–7 data scientists and analysts, fostering a high-performing, collaborative culture Owned the marketing analytics roadmap aligned to enterprise growth and efficiency objectives Oversaw end-to-end data science lifecycle including data ingestion, modeling, deployment, and monitoring Directed development of advanced models including Marketing Mix Modeling (MMM), attribution, CLTV, segmentation, propensity, and personalization Led MMM consulting engagements from intake through delivery, including scenario planning, ROI simulations, and media optimization recommendations Translated complex analytical outputs into clear, executive-ready narratives, dashboards, and presentations Acted as primary escalation point for senior stakeholders and external marketing partners Championed analytical rigor, QA standards, and ethical modeling practices Product, MarTech & Digital Analytics Leadership Defined and executed Customer and Marketing Data Product strategy, including KPIs, business cases, and budgets Translated strategy into Agile roadmaps with prioritized, high-value initiatives Led vendor management and contract oversight Evaluated and incubated GenAI, agentic AI, and reinforcement learning use cases for marketing automation and decisioning Owned digital analytics implementation across web and mobile platforms, including tagging, data layers, governance, and measurement plans Partnered with Data Engineering to integrate analytics data into cloud ecosystems

Led product analytics and advanced decision science initiatives supporting pricing, promotions, retention, and service optimization. Owned in-life product performance analytics including sales, profitability, renewals, cancellations, and retention Built KPI reporting by division, region, and channel to inform strategic decision-making Developed and deployed predictive and machine learning models using Python, R, SQL, and Databricks Conducted large-scale exploratory data analysis, hypothesis testing, and causal analysis Designed and executed A/B and multivariate tests to evaluate pricing, offers, and product strategies Managed and mentored a high-performing analytics team Partnered cross-functionally with Product, Marketing, IT, Data Engineering, and senior leadership Championed data governance, data quality, documentation, and reliability standards Served as a trusted analytics advisor and “go-to” resource across the organization

Built and led data science and forecasting teams delivering predictive and prescriptive analytics at enterprise scale. Designed and deployed machine-learning-based time series forecasting engines for demand, sales, new products, anomaly detection, and goal setting Served as a Center of Expertise for forecasting and advanced analytics across the business Led teams of full-time, contract, and intern analysts Acted as hands-on architect and owner of forecasting codebases and analytical frameworks Partnered with BI, Product, Marketing, IT, and Demand Planning to operationalize analytics Developed automated dashboards and performance monitoring tools Communicated complex analytical concepts clearly to executive and non-technical audiences Evangelized data-driven decision-making and forecasting best practices across the enterprise