Oz asks, “There’s still the ethical questions that my marketing friends avoid: should Google even have all that data? How ethical is it to use that data? What about pending legislation that would hopefully criminalize lots of common data acquisition methods?” Two points I think are super important: minimum viable data, and behavior rather than…
Monina asks, “What steps make up a data science lifecycle? Where do you begin?” The short answer to this question is to define the problem and hypothesis, prepare your data, explore your data, test your hypothesis, build a model, validate the model, and then deploy and observe. Each stage is composed of multiple sub-steps. Watch…
Summary In today's episode, I walk through every stage of the data science lifecycle and explain why each step matters. Here's what this means for you. You get a clear roadmap for turning raw questions into tested, deployable models you can defend. You'll also learn these concepts: why defining the problem and stating a provable…
Monina asks, “What tools are useful to help marketers dig deep into their organization’s data?” The answer to this question depends on the level of skill a marketer has in data science, specifically the technical and statistical skillsets. I’d put the available tools in categories of beginner, intermediate, and advanced. Beginner tools help marketers extract…
Monina asks, “What kind of analytics can data science uncover?” This is an interestingly worded question. Analytics are fundamentally about explaining what happened, the what in your data. Very often, as stakeholders we also want to know what’s relevant (data overload) and then why (insights). That’s where data science can help, especially with understanding what’s…
Summary In today's episode, I explore what data science can uncover beyond basic analytics and how it transforms raw data into actionable insights. Here's what this means for you. You gain the ability to cut through data overload and focus on what actually drives your business results. You'll also learn these concepts: how four key…
Here’s the sign from this week’s Almost Timely Newsletter, which you can download as a PDF and print out for your office. Click on the image or this link to download a 384 KB PDF file.
Summary In today's episode, I share a New Year's message about analyzing your 2019 marketing data to drive real business decisions in 2020. Here's what this means for you. You'll break free from analysis paralysis by focusing only on what to do more of and what to stop doing. You'll also learn these concepts: why…
Summary In today's episode, I show where PDF tracking data appears in Google Analytics after you set it up through Google Tag Manager. Here's what this means for you. You gain a clear map of which reports to check so you can verify your tracking and measure conversions with confidence. You'll also learn these concepts:…
Sampurnakumar asks, “What level of data quality do you need for AI to be successful? Does it require the best data and best data usage to solve problems?” One of the problems with AI, conceptually, is that it’s seen as this mysterious entity that we don’t fully understand. Any qualified AI practitioner should immediately debunk…
