Category: Data Science
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You Ask, I Answer: Tools or Concepts in Marketing Data Science?
Jessica asks, “Which should we focus on learning most in marketing data science, concepts or tools?” Without a doubt, concepts. You learn frying, not a specific model of frying pan. You learn painting, not a particular paint brush. You learn to play any piano, not just one kind of piano. Can’t see anything? Watch it…
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You Ask, I Answer: The ROI of Data Quality?
Oz asks, “I have a question about what you mean about data quality can’t be sold and it’s seen as overhead? I suspect we’re talking about 2 different things but I’m curious about what you’re describing.” In the data analytics and data science process, data quality is absolutely foundational – without it, nothing else matters.…
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You Ask, I Answer: Marketing Data Science Hypothesis Formation?
Jessica asks, “I struggle with forming hypotheses. Do I need more data to get better?” Data probably isn’t the problem. A well-defined question you want the answer to is probably the problem. Consider what a valid hypothesis is, within the domain of marketing data science: a testable, verifiably true or false statement about a single…
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You Ask, I Answer: Marketing Data Science Hypothesis Creation?
Jessica asks, “How will a data scientist create my model or hypothesis if they don’t know my business?” This is an excellent question. The short answer is: they can’t, not reliably. Not something you’d want to bet your business on. Data science is the combination of four things: business skills/domain knowledge, scientific skills, technical skills,…
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You Ask, I Answer: Choosing Marketing Data Science Variables?
Jessica asks, “As a Data Scientist for marketing, how do you decide which variables are important?” Generally speaking, feature selection or variable/predictor importance is the technique you’d use to make that determination – with the understanding that what you’ll likely get is correlative in nature. You then have to use the scientific method to prove…
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You Ask, I Answer: Citizen Data Scientists?
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…
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You Ask, I Answer: Marketing Data Science Technical Skills?
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…
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You Ask, I Answer: Determining Sample Sizes for Surveys?
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…
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You Ask, I Answer: Hypothesis Formation Without Data Snooping in Marketing Data Science?
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…
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You Ask, I Answer: The Future of Marketing Data Science?
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…