Senior Manager Marketing Analytics ...

Join SimpliSafe, a fast-growing, high-tech home security company dedicated to protecting homes. We are seeking a highly analytical and experienced Senior Manager, Marketing Analytics Forecaster to play a pivotal role in developing and refining our forecasts for critical Direct-to-Consumer (DTC) metrics. This role involves close collaboration with various departments, including Marketing, Finance, Operations, and Product, to ensure the accuracy and decision-readiness of our projections. If you are passionate about leveraging data to drive strategic business decisions and thrive in a dynamic environment, this is the opportunity for you.

Responsibilities:

• Own the end-to-end forecasting process, including system selection, cadence, and ongoing improvement.
• Develop and maintain daily and monthly forecasts for key DTC metrics such as traffic/leads, orders, activations/installs, new subscribers, Average Revenue Per User (ARPU), and overall revenue.
• Produce and publish clear, concise, and stakeholder-ready forecast updates on a weekly, monthly, and quarterly basis, meticulously documenting assumptions, versioning changes, and key insights.
• Maintain a comprehensive driver tree that logically connects controllable input factors (e.g., media spend, promotional activities, pricing strategies, channel mix) to desired business outcomes (e.g., sales, subscriber growth, revenue).
• Build and maintain robust, driver-based forecasting models that leadership can trust for strategic planning.
• Develop and refine forecasting models incorporating advanced techniques such as time series analysis and causal drivers, accounting for seasonality, promotional calendars, pricing/offer fluctuations, channel mix dynamics, and operational constraints.
• Layer assumption-driven adjustments onto model outputs when business context shifts (e.g., new promotional mechanics, product launches, macroeconomic changes), providing clear documentation for the rationale behind these adjustments.
• Develop comprehensive scenario plans (base, upside, downside) and sensitivity analyses to assess the impact of variations in media spend, promotional depth, pricing changes, and macroeconomic or competitive shifts.
• Forecast marketing impact and integrate learnings from media measurement, with a strong emphasis on incrementality.
• Translate detailed marketing plans into precise forecast inputs, including spend by channel, expected reach and response rates, lag effects, and saturation/diminishing returns curves.
• Incorporate incrementality learnings from experiments (e.g., A/B tests, geo tests, lift tests) and/or Marketing Mix Modeling (MMM) / Multi-Touch Attribution (MTA) outputs into response curves and forecast drivers, explicitly differentiating correlated performance from expected incremental impact.
• Partner with the analytics team on media measurement and marketing performance analytics. This includes designing and analyzing incrementality tests (A/B, holdouts, geo/lift), collaborating on MMM/MTA initiatives, and translating findings into actionable response curves, Customer Acquisition Cost (CAC)/Return on Ad Spend (ROAS) benchmarks, and planning assumptions.
• Diagnose and explain shifts in the customer funnel (e.g., conversion rates, approval rates, attach rates) and lifecycle dynamics (e.g., retention, churn) in plain language.
• Own the reconciliation of the Direct P&L (Profit and Loss) and performance attribution.
• Produce reporting that ties operating forecasts directly to Direct P&L outcomes (sales, subscription revenue, unit economics), including clear bridges between forecast and actual performance.
• Ensure consistent metric definitions and perform reconciliations across various systems (billing, marketing, finance), maintaining an audit-friendly methodology and a comprehensive change log.
• Provide leadership with a clear and compelling 'forecast vs. actuals' narrative, detailing what moved, what drove those movements, and recommended actions.
• Continuously innovate and implement improvements to enhance forecast accuracy over time.
• Own forecast performance tracking (e.g., Weighted Absolute Percentage Error (WAPE)/Mean Absolute Percentage Error (MAPE), bias) and conduct root-cause error decomposition by channel, promotion, segment, and forecast horizon.
• Implement advanced improvements such as enhanced features/drivers, segmentation/cohort analysis, hierarchical forecasting, improved promo pull-forward treatment, anomaly detection/alerting, and automation.
• Partner with Analytics Engineering to enhance pipeline reliability, speed, and data quality, thereby reducing manual effort and increasing overall trust in the data and forecasts.