Summary In today's episode, I explain how to identify the right KPIs for measuring content engagement and walk through the process of sourcing data to track them. Here's what this means for you. You learn to run a regression analysis that reveals which engagement metrics actually predict your conversions rather than wasting effort on vanity…
Jessica asks, “When it comes to training data for marketing AI models, do you think vendors will anonymize/share data sources in the future? Will it be required?” It depends on the vendor and the model. The raw data for public models, even de-identified, probably will not be publicly available, but should be made available to…
Jessica asks, “Who’s liable for violations in marketing AI models, the company that hires the vendor, or the vendor? Anything in service agreements to look for?” Who’s liable when someone uses a tool improperly or illegally? Companies have some responsibility, as we see with product warnings, but the ultimate responsibility – and who gets sued/arrested…
Tracy asks, “What are some questions you should ask vendors to better understand what data they use in their algorithms to make sure it’s not biased?” It’s not just questions we need to ask. Consider checking for bias to be like any other audit or due diligence. We will want to investigate the 6 main…
Gianna asks, “What’s the difference between fair and unfair bias? What’s the fine line?” Fair and unfair comes down to two simple things: laws and values. Statistical bias is when your sample deviates from the population you’re sampling from. Bias isn’t inherently bad unless it crosses one of those two lines. Can’t see anything? Watch…
