Analysis / Data Mining / NLP
Research question: Can Natural Language Processing (NLP) be used to identify review sentiment and customer satisfaction trends?
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Marketing research, analysis, intelligence and insight
I am a consulting marketing data science professional living and working in the United Kingdom. I operate at the intersection of strategic marketing, analytics, data, and code, and the goal of my work is to uncover and effectively communicate insights to fuel your strategic direction. I work at the strategic level with in-house marketing and R&D teams and regardless of the sector, I work best with teams who are passionate about driving strategic marketing growth. Please read my Marketing Data Science Manifesto.
Latest Marketing Theory
Applied Marketing Data Science Projects
Analysis
Research question: Does the price elasticity of demand for digital asset purchases differ between two groups that are distinct in terms of location and economic status?
Analysis
Research question: To what (measurable) degree does eWOM recommendation have a moderating effect on a parents’ intention to enquire or register at an independent senior school?
Forecasting
Research question: What are the lifetime values, churn and retention rates of different segments of online subscribers?
Analysis / Regression Modelling
Research question: Is there a correlation or causal relationship between school pupil withdrawals and school transport pickup points, or the distance travelled?
The Value of Marketing Data Science
Marketing data science, research, insight, and analytics reside at the centre of all well-informed business and customer decisions when insight into what makes a service, business initiative, or policy work is often the hidden factor that defines the difference between success and failure. In essence, the value of applied marketing data science lies in its ability to deliver objective, data-driven insights into marketing problems. Through rigorous scientific methods, it provides high-quality data, information, and insight for planning and decision-making in complex, fast-moving, and often turbulent environments.
The Marketing Data Science Domain
Marketing Data Science is an interdisciplinary field that involves applying scientific, statistical, and computational techniques entwined with a deep knowledge of marketing to extract insights from data and understand, measure, and optimise marketing decisions and strategies.Traditional marketing approaches rely on intuition and experience to make decisions. Instead, marketing data science uses data and scientific methods to test hypotheses and make evidence-based recommendations. Using mathematical models, statistical analysis, and other analytical tools, the marketing data science approach will attempt to gain insights into customer behaviour, market trends, and the effectiveness of marketing campaigns.
By combining insights from strategic marketing - the process responsible for identifying, anticipating and satisfying customer requirements, computer science (computation and automation), and statistical mathematics - including the analysis of quantitative data, marketing data science can inform and support effective marketing strategies that will be grounded in data and evidence.
Marketing Data Science Methodology
Marketing data science methodology involves hypothesis and research question development, data mining, collection, cleaning and pre-processing, extrapolation, analytical and machine learning model development, hypothesis testing and reporting. A new hypothesis is then developed, and the cycle continues.The scientific approach focuses on selecting meaningful hypotheses to test, which develop into carefully crafted research questions, creating well-designed experiments, and minimising any factors that could skew the results. Additionally, it promotes the importance of iterating experiments systematically, considering how the results can be applied to other contexts and when it is appropriate to reuse past findings or revalidate them. It also emphasises the need for intellectual honesty when analysing outcomes, understanding the limitations of the evidence, and knowing when to stop pursuing a theory.
By adopting a scientific approach, marketers can better understand consumer behaviour, preferences, and attitudes and therefore make data-driven decisions that lead to more effective marketing strategies and better outcomes for their organisations.
Marketing Data Science Application
With a broad remit, the data-driven application of marketing data science leverages statistical analysis and other scientific methods to enhance, support and inform a multitude of marketing areas.Marketing data science can be applied to market research, understanding consumer behaviour, product, service and pricing development, market segmentation, marketing mix modelling, attribution modelling, pattern and trend identification, predictive analytics, propensity modelling, decision support and strategy development.
By leveraging advanced statistical functions such as linear and logistic regression, conjoint analysis, clustering, time series analysis and multidimensional scaling, marketers can help organisations make more data-driven decisions, enhance customer understanding, increase efficiency, improve targeting, and increase ROI. The outcomes of marketing data science can be used in conjunction with storytelling frameworks to create compelling data visualisations that can present complex data in an engaging way that resonates with audiences.
Understanding Consumer Behaviour
With applications in advertising and market research to product design and customer service, marketing data science allows you to understand why consumers buy certain products, how they perceive brands, and how they make choices among a myriad of different options. Motivation, perception, learning, memory, and attitudes can all be analysed, along with the relationships between different economic variables, in order to make predictions about future economic outcomes. By understanding consumer behaviour patterns, marketers can better develop effective marketing strategies that resonate with consumers.
