Leveraging Big Data and Predictive Analytics in Marketing Strategies
Marketers have never had more data about their customers, yet much of it sits unused while campaigns still run largely on intuition. This chapter asks whether predictive analytics, applied to big data, can turn that raw information into marketing decisions that retain customers, sharpen campaigns, and serve a wider social purpose at the same time.
Taking a conceptual and practice-based approach grounded in secondary data, industry insight, and theory drawn from machine learning, customer behaviour, and marketing analytics, the chapter uses visual models and structured tables to map how predictive techniques work in practice. It shows analytics being used to spot high-value customers, anticipate churn, tailor campaigns, and price products dynamically, moving marketing teams from reactive responses towards proactive, anticipatory strategy.
Its distinctive argument is that data-driven marketing need not be purely commercial. When predictive models are paired with sustainability-oriented metrics, they help align revenue goals with social responsibility, improving return on investment while also building transparency, more ethical engagement, and lasting value co-created with consumers.
Big data and predictive analytics let marketers shift from reacting to anticipating, using customer data to spot high-value buyers, forecast churn, personalise campaigns, and price dynamically. The chapter's distinctive claim is that, paired with sustainability metrics, these tools align profit with responsibility, lifting return on investment while supporting transparency and long-term value co-creation.
- Predictive analytics moves marketing from reactive to proactive, letting teams pinpoint high-value customers, anticipate likely churn, tailor campaigns, and set prices dynamically from patterns already present in customer and behavioural data.
- The chapter takes a conceptual and practice-based approach, drawing on secondary data, industry insight, and theory from marketing analytics, machine learning, and customer behaviour, with visual models and tables illustrating how the techniques apply.
- Its central argument is that data-driven marketing need not be purely commercial: pairing predictive models with sustainability metrics helps align revenue objectives with social responsibility rather than trading one against the other.
- Beyond improving return on investment, the chapter contends that responsibly applied analytics fosters transparency, ethical customer engagement, and long-term value co-creation, reframing big data as a tool for durable relationships rather than mere conversion.
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