Do you have a minute to answer a personal question?

Which way do you prefer your roll of toilet paper to hang? Should the paper roll out from the front of the roll, or should it roll from the back?

You might wonder what that has to do with banking, but I bet you thought a few seconds about your preference. That's the genius behind a New York start-up called Hunch, which bills itself as a way to personalize the Internet by making smart recommendations about what you might like based on a series of questions.

Similar to Amazon's successful recommendation engine, or Pandora's Music Genome Project, Hunch leverages a unique user-created social graph to encourage discovery on the Web. Not surprisingly, providing personal data might mean being invited to purchase a product from a Hunch partner, viewing targeted advertising similar to Facebook or Google, or being presented particular Web content (sort of like a smarter StumbleUpon). Hunch attempts to personalize recommendations on thousands of topics, and is slowly being integrated into third-party sites and applications.

This is where Hunch's business model gets interesting, and where the banking industry might glean lessons as it continues to build its own customer profiles and social graph.

Getting started on Hunch is simple. After registering through your Facebook or Twitter profile, your adventure begins with your first batch of questions. After answering the first few, you realize how fresh their approach to information gathering actually is. Hunch makes the process of sharing personal preferences very engaging because of the types of questions being asked, and in the way questions are presented. While many questions come with interesting visual cues, their variety and tone are very entertaining. You might be driven to provide more personal data than you expected because it doesn't feel all that personal. When is the last time you could say that about a customer survey or an online application?

You may question the psychology of some of the queries, such as your preference of receiving a red or blue toothbrush when you have completed a dental visit, or whether you like the window or aisle seat while flying. What does this have to do with my preference for books? Or how can this help me pick the name of my first child? Plenty, it turns out, as the company has over 20 billion "taste profiles" that it uses to model preferences to a myriad of solutions.

While I found the diverse list of recommendation categories interesting and accurate, Hunch masks its monetization through inquiries that aren't easily associated with an obvious product or service. Banks need to learn from these examples to generate customer experiences that are as engaging as they are valuable in gathering intelligent customer data. It's only when we learn enough about our customer's innermost preferences that we can accurately deliver solutions that are designed to meet their true needs.

Bradley Leimer manages the online service group for Mechanics Bank in Richmond, Calif. His background in marketing and technology in the financial services sector includes brand development, social media and user-experience research. You can follow him on Twitter and find him on LinkedIn.