Summary In today's episode, I walk through how to scrape fanfiction data from Archive of Our Own and use IBM Watson AutoAI to predict what drives story popularity. Here's what this means for you. You'll see how exploratory data analysis turns messy scraped data into a testable content marketing hypothesis. You'll also learn these concepts:…
Day 2 of THINK 2020 was much more meat and potatoes, from use cases for AI to process automation. Rob Thomas, SVP Cloud and Data, showed a fun stat that early adopters of AI reaped a 165% increase in revenue and profitability, which was nice affirmation. But the big concept, the big takeaway, was on…
Summary In today's episode, I break down the highlights of day two of IBM's Think 2020 digital experience, focusing on a fascinating concept called neurosymbolic AI. Here's what this means for you. Neurosymbolic AI combines the strengths of expert systems and neural networks to build more trustworthy AI, including chatbots that handle nuanced conversations while…
We look back at day 1 of the IBM THINK Digital Experience. Completely different from the in-person experience, but neither better nor worse. Highlights: – AI for IT – complexity of systems – Rob Thomas on a more layperson-friendly Watson Studio AutoAI – Tackling of more complex issues with AI – Data supply chain and…
Summary In today's episode, I recap the highlights from day one of the IBM Think 2020 digital experience, covering AIOps, hybrid cloud strategy, and what marketing technologists can borrow from enterprise IT. Here's what this means for you. You gain a practical lens for protecting your marketing stack by treating your data supply chain with…
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…
Arjuna asks, “Could you please suggest an approach to forecast multiple targets (e.g., is there a way to select multiple columns in AutoAI). In our use case, we need to develop time series forecasts for multiple products. If we correctly understood AutoAI, it will allow us to select one column at a time to generate…
Summary In today's episode, I unpack a listener's question about forecasting multiple targets and using multi-objective optimization inside IBM Watson Studio Auto AI. Here's what this means for you. You'll see why Auto AI hits hard limits on time series and multi-objective work, and you'll get a practical workaround that turns those limitations into results.…
CC asks, “What’s the most valuable analysis overall for a small business to perform?” If we define a small business as an enterprise with less than $1 million in sales and fewer than 10 employees, then from an overall business perspective it’s got to be RFM analysis. Nothing else comes close in terms of helping…
Summary In today's episode, I break down RFM analysis and explain why it is the single most valuable data analysis a small business can perform. Here's what this means for you. You gain a simple, repeatable method for identifying your most valuable customers and the ones with hidden revenue potential, even with nothing more than…
