Musing on AI, Consumer Data and Privacy

Uber ride data provides valuable insights into consumer behavior, urban economic health, emerging trends, and much more. Utilizing AnthologyAI's Transportation dataset, we unpack the substantial impact of location, seasonality, and economic factors on Uber fares.

AnthologyAI transforms economic insights with an alternative CPI methodology, offering businesses an accurate digital view of consumer activities. We illustrate this with a case study on milk, a staple commodity that affects a broad consumer base, and compare our approach to traditional benchmarks.

Introducing AnthologyAI. Our CEO, John Roa, unveils a new brand AnthologyAI, the single most efficient, accurate, and actionable source of consumer intelligence ever built. We monitor global consumer trends 24/7 without compromising privacy through PII, identity resolution, or other invasive methods. Read his note below.

Coffee is your morning pick-me-up and daily ritual. But what does it say about you or your lifestyle? We've analyzed over 75,000 Walmart coffee transactions to discover what your coffee choices say about you. When evaluating Walmart's top-purchased coffee brands (Starbucks, Dunkin’, Folgers, Pete’s Coffee, and more), we looked for patterns that intertwine consumer behavior, brand preference, and broader societal trends. So, what did we find?

AnthologyAI (parent company of the Caden consumer app) looked into our knowledge graph of 67 million Netflix watch actions for consumers across the US to uncover what’s trending. Spoiler alert: It’s a mix of the strange, dark, and satirical. 

Neudata, a leading data-scouting FinTech firm dedicated to finding alternative data for leading global organizations, recently held a Shark Tank competition at its New York Data Summit. Four companies competed for the top spot, with Caden emerging victorious. The competition was judged by Beth Pollack from Decision Science Advisors, Adam Brown from Morgan Stanley, and Jason DeRise from Liberty Mutual Investments.

Within the data economy, an aggregation of complex cryptographic concepts have been lumped together into a catchy acronym that follows the blueprint of the latter: PETs, or Privacy Enhancing Technologies.

Sometimes, the names we give technologies reveal much more about our intentions and desires than they do about the ostensible features they provide. As an intellectual exercise, let’s zoom in on one of the buzziest such names in marketing today—the “Clean Room.”

In our previous blog, we laid out how we’re at a point of precipitous “signal loss” of good-quality data, the struggles of improving this quality, and the dangers of relying on poor-quality data. With more than 50% of marketers relying on (AI) for data-driven personalization at scale, having reliable good quality data is of utmost importance. However, to put it bluntly, in the words of Michael Stonebraker, “without clean data, or clean enough data, your data science is worthless.” And this will persist until we, as data practitioners, fix our existing “data supply chain” to ensure better quality data throughout.

The lie we’ve told ourselves as data practitioners - and the one we continue to tell ourselves - is that the headwinds we face from regulation and the demand for informed consent are suppressants and not accelerants.