Jessica asks, “How do you feel about citizen data scientists?” I love the theory, the concept, and to be sure, there are plenty of people who are data scientists that lend their expertise to causes and movements outside of their day jobs. But the question is, is a citizen data scientist someone who is a…
Summary In today's episode, I break down what it really means to be a citizen data scientist, exploring both interpretations and the hidden risks that come with applying data science skills to causes outside your professional expertise. Here's what this means for you. You gain a practical framework for knowing when your data science contributions…
Jessica asks, “When it comes to marketing data science, I’ve got very good business knowledge, but lack of the technical side. any advice?” The first question you have to ask is whether you need the hands-on skills or just knowledge of what’s possible. The second question is what skills you already have. Remember that in…
Summary In today's episode, I walk through how to build technical and statistical skills for marketing data science even when you start with strong business knowledge. Here's what this means for you. You'll discover that learning the math first, then the tools, prevents wasted effort and helps you make smarter technology choices. You'll also learn…
Phil asks, “How do you determine a large enough sample size for things like our survey? I always thought 10% sample would be enough, but you seemed to think that’s not true?” It depends on the size of the overall population. The smaller the population, the larger the sample you need. It also depends on…
Summary In today's episode, I break down how to determine the right sample size for surveys and why a flat 10% rule rarely works in practice. Here's what this means for you. You'll stop wasting money on surveys that produce unreliable results and start making decisions backed by statistically sound data. You'll also learn these…
Jessica asks, “How would you differentiate hypothesis formation and searching for relevant variables WITHOUT “data snooping”?” Data snooping, or more commonly known as curve fitting or data dredging, is when you build a hypothesis to fit the data. The way to avoid this is by using evidence not included in the dataset you used to…
Jessica asks, “Which concepts or tools to be developed will inform the future of marketing data science?” The biggest changes will be on the technology side of marketing data science. Many tasks, like data cleaning and imputation, will benefit from what’s happening in AI. Transfer learning Massive pre-trained models for things like images, text, and…
Summary In today's episode, I break down four technological innovations that will automate the most time-consuming parts of marketing data science. Here's what this means for you. You reclaim hours spent on data preparation and cleaning so your team can focus on strategy, insights, and tactics that actually move the needle. You'll also learn these…
Balabhaskar asks, “How can we use marketing data science to get more insights from the same old data or the few data points available because of privacy laws?” Blending of new data with old data, especially credible third party data is one solution. The second solution is feature engineering. Both are vital parts of exploratory…
