As a successful Minnesota entrepreneur, Scott Litman — along with longtime business partner Dan Mallin — has built five companies and founded MN Cup, the largest statewide startup competition in the entire country.
In their most recent venture, Equals 3, Scott and Dan created Lucy, an AI-powered companion for marketers. They seek to “democratize data” and help more people at big companies have clearer, simpler access to the data they own.
At their Minneapolis headquarters, Scott chatted with me about the adoption of artificial intelligence, the key to successful business partnerships and what businesses are still getting wrong about marketing automation.
Nels: Tell me how you’d describe Equals 3 and Lucy to someone who’s not as well-versed in artificial intelligence.
Scott: I’ve had a long career in marketing services and ad-tech and I’ve worked with a lot of different companies. A common challenge that businesses face, particularly in sales and marketing, is they have so much data that they can’t use it.
Think about a big retailer like a Target or a Best Buy or a big packaged goods company like a General Mills. They have data about who’s been on their websites. They have data about the search marketing terms that people use to find them. They have data about people who talk about them in social. They have data either in their CRM, which are their customer databases, or in their loyalty databases.
And these things are all different systems that house marketing data. But that’s just one type of data.
Then they also have subscriptions. They buy research from places like Forrester and eMarketer. They buy media data from places like Nielsen. They buy audience data from places like Axiom. And the list of who they can license from goes on and on and on.
And then they’ve also got files. Imagine a company like a 3M, a General Mills, a Best Buy. They probably have millions of PowerPoints, PDFs and Word documents that are sitting in file systems that will never be opened again.
So they’ve paid for all this but it’s more information than any one person, or even a team of people, can be physically aware of. And so our premise was that, in a world of machine learning, we could read every document, every row and column of every database and spreadsheet, and we could make that information available to people when they need it, not unlike the way they ask for the weather or movie listings or sports scores from Alexa or Siri. I could ask our Lucy questions but it’s about the data that marketers own or license. Click to read or listen to the full interview.
