『Marketing Science』のカバーアート

Marketing Science

Marketing Science

著者： Mikee AI
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Mikee works through practical marketing science concepts using real-world examples from modern marketing mix modeling and measurement. Listeners learn how to build accurate marketing models, understand data quality issues, and implement privacy-safe tracking methods. The show covers essential topics like MMM study lifecycle, data pipelines, statistical analysis, and budget allocation strategies. Each episode provides actionable insights for improving marketing measurement and decision-making processes.Copyright 2026 Mikee AI 教育
エピソード
  • Operate Plus Present Recommendation

    Operate Plus Present Recommendation
    2026/08/21
    Marketing science episode explores operate plus present as a decision-making framework for data-driven marketers, not simply A/B testing or campaign selection. The approach combines current performance data—such as spend, conversions, cost per acquisition, and ROAS—with probabilistic modeling to evaluate future paths. It emphasizes understanding baseline metrics, calibrating models with historical data while accounting for sample size and variance, and building a recommendation space with at least three to four options each tied to probability estimates and risk levels. The framework considers opportunity cost, marginal returns, and execution speed, requiring documented decision rationale to enable faster iteration. Common pitfalls include treating past data as gospel, misjudging marginal impact, and ignoring emotional and political factors in implementation. The method applies to both large strategic moves and small experiments, promoting a mindset of informed risk-taking rather than guesswork.
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    9 分
  • How To Allocate Media Budget Effectively

    How To Allocate Media Budget Effectively
    2026/08/20
    Marketing science explores how to allocate a media budget effectively, moving beyond past performance to future planning. The process begins with defining a clear business question, such as whether to focus on sales volume, market share, or customer lifetime value, and selecting a consistent KPI like revenue or contribution margin. Teams must collect clean historical data by channel, campaign, and audience, analyzing trends rather than averages to understand performance per dollar spent. Experiments are critical for testing different audiences, creatives, and formats, with sample sizes and test durations carefully considered. A predictive model incorporating performance history, seasonal trends, demographic shifts, and competitive movements helps forecast future outcomes. Triangulation ensures alignment between model projections, experimental results, and observed data before final allocation. The episode emphasizes that without proper framing, experimentation, or modeling, budget decisions become guesswork, especially when managing a nine-point-six-million-dollar budget where missteps carry significant financial risk.
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    9 分
  • Building Measurement Culture Changes Everything

    Building Measurement Culture Changes Everything
    2026/08/19
    Building measurement culture transforms how organizations make decisions by embedding real-time data analysis into daily operations. Teams begin treating every campaign as a controlled experiment, setting baselines and defining success early, then tracking performance continuously rather than waiting for quarterly reports. One brand tested geo-targeted campaigns across six regions over twelve weeks, using holdout groups and statistical confidence intervals to guide budget shifts. When data showed a fourteen percent lift in one region with an eight percent threshold, they reallocated ten percent of their social budget accordingly. This approach requires teams to measure before spending, share insights broadly, and use learning to inform future strategies. The process isn't about complex tools but disciplined practice: defining clear outcomes, running small tests, analyzing results instantly, and adjusting course immediately. Over time, this creates a feedback loop where each test teaches the next, building organizational intelligence and adaptability. The key is starting small, maintaining consistency, and treating every result—whether positive or negative—as a learning opportunity that improves future performance.
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    9 分
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