The Mathematics Manifesto: A Path to Predictable Marketing?

Just as Martin Luther in 1517 ignited the Reformation by nailing his 95 Theses to the door of All Saints' Church, we nail our new principles to the digital doors of the marketing world. Inspired by the seminal work of Marketing Mathematics Philip Kotler and building upon it, this humble manifesto calls for a radical transformation of marketing from an art to a science — a discipline governed by data, algorithms, and the pursuit of undeniable mathematical certainty.

Falling short of that, we hope, at the very least, to provide the algorithms or formulas for brand trust, brand growth, and marketing performance that reduce risk and increase the chances of marketing success consistently within predictable parameters.

Acknowledgment of Historical Foundations:

Obviously, the idea of mathematics in marketing isn’t new – every marketer is aware of how to calculate ROAS, ROI, CPCA and more; much of it we owe to Philip Kotler's pioneering integration of marketing and mathematics, who laid the groundwork for what we aspire to enhance. Today, with advancements in AI, machine learning, brand trust psychology and analytics, BHG is poised to take these initial steps in the hope of achieving unprecedented predictive precision in marketing strategies. An ambitious goal if nothing else.

Core Principles:

1. Evidence-Based Decision Making: Example 1: Leveraging predictive analytics, a leading online retailer adjusted its inventory and marketing in real-time, resulting in a 50% reduction in stock-outs and a 35% increase in customer satisfaction scores. Example 2: An automotive brand used customer journey analytics to optimise touchpoints, which increased conversion rates by 45% and customer retention by 25%.
2. Quantitative Modelling and Algorithms: Formula Development: The 'Customer Lifetime Value Prediction Model', which uses historical purchase data and engagement metrics to forecast long-term profitability from customer segments, was introduced. Implementation: A media company applied machine learning to parse viewer data, enhancing content targeting that lifted viewer engagement rates by 60%.
3. Behavioural Economics and Consumer Psychology: Research Insight: Studies integrating principles from the Ehrenberg-Bass Institute demonstrate how widespread brand recognition significantly outweighs loyalty in driving consumer purchase decisions. Application: A beverage brand used these insights to restructure its marketing focus on point-of-sale presence, which expanded its market share by 20%. Formula Development: BHG already has a trust-based algorithm for optimising the perception of brand trust. Post-optimisation trust perception gradings see an average of 27% improvement in perceived trust, helping to improve other marketing metrics like conversion rates.
4. Ethical Data Practices: Policy Advocacy: Promoting stringent global standards for data privacy that not only comply with regulations like GDPR but also exceed them, ensuring consumer trust and brand integrity. Community Engagement: Creating forums for consumer feedback on data usage, which improves transparency and enhances consumer-brand relationships.
5. Unified Marketing Metrics: Standardisation Initiative: Crafting a set of common metrics that allow for cross-industry comparisons of marketing effectiveness, akin to financial metrics like EBITDA. Benchmarking Success: Establishing industry-wide benchmarks based on these metrics to foster a competitive, transparent marketing ecosystem.
6. Strategic Empiricism: Case Study: A fashion retailer implemented an AI-driven forecasting tool that adapted marketing strategies based on real-time fashion trends, increasing their season-to-season sales by 30%. Philosophical Approach: Committing to strategies that are continuously validated through empirical research ensures that marketing practices are not just theoretically sound but proven in practice.
7. Optimal Brand Growth Strategies: Algorithms are now designed to calculate the optimal mix of market penetration and product innovation, ensuring sustainable brand growth. Example: A cosmetics brand leverages sentiment analysis and sales data in real-time to optimise its product lineup and respond swiftly to market demands.
8. Acceleration of Concept Testing: AI-augmented tools are transforming traditional research, speeding up concept testing, and allowing quicker market entry with refined offerings. Example: A food and beverage company employs machine learning to analyse social media feedback, rapidly adjusting product features to meet consumer preferences better.

Building Upon the Shoulders of Giants:

This manifesto draws upon the insights of the Ehrenberg-Bass Institute, Professors Byron Sharp and Mark Ritson, thought leaders like Seth Godin, and trusted author Patrick Lencioni. Their pioneering work in marketing provides the intellectual scaffolding for our expanded approach. We seek only to aggregate and simplify their hard-earned and published research into useful formulas where possible and apply them where practical.

Author: Grant Belcher

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