The Persuasion API: Profiling Consumers for Individualized Sales Pitches

Science Rockstars aims to transcend simple recommendation engines and A/B testing techniques to create personal profiles of pitches that persuade individually. Their Persuasion API allows applications to compile profiles of each customer and what sways them. Then, based on the profile, communication can be altered in real time in ways that best suit the customer and increase the conversion rate. The documentation notes that it can be implemented either on the server or client side (the latter includes a Javasctipt Library to facilitate the process).

Eli Pariser of fame wrote up the research behind  their work in Wired, summarizing it as the ability to figure out how you think. He describes how two researchers, Marits Kaptein and Dean Eckles, set up an experimental bookstore online to try out different persuasion techniques:

"Some book buyers felt comforted by the fact that an expert reviewer vouched for their intended product. Others preferred to go with the most popular title or a money-saving deal. Some people succumbed to what Eckles calls “high need for cognition” arguments—smart, subtle points that require some thinking to get (“The Hunger Games is the Inferno of children’s literature”). Still others responded best to being hit over the head with a simple message (“The Hunger Games is a fun, fast read!”). And certain pitches backfire: While some people rush for a deal, others think discounts mean the merchandise is subpar. By eliminating persuasion styles that didn’t work on a particular individual, Kaptein and Eckles were able to increase the effectiveness of a recommendation by 30 to 40 percent."

The kicker is the techniques translate across different types of products: the same types of persuasion tactics that entice you to buy a book will persuade you to buy clothes.

Pariser sees a dark side to this, the personalization of the web to the point where people are fed what they most want to hear, something that began in 2009 when Google decided to personalize search results for each user. Pariser chronicles this in his 2012 book, The Filter Bubble: How the New Personalized Web Is Changing What We Read and How We Think.

But there is an upside as well (in addition to the profitability for corporations using the technology), as Nir Eyal explains in Techcrunch. Namely, the possible reduction of advertising that merely clutters our lives:

"In the age of increasing personalized data and a greater understanding of the tools of persuasion, companies will no longer need to communicate with customers as an amalgam of an “average user” that doesn’t actually exist. Instead, by marrying psychology and customer data, smart companies will give customers more of what they want: someone who speaks their language."

Where to from here? Is this an advance, or a manipulation that could constrain people's growth based on limitations of what we are exposed to, according decisions made by algorithms?

Full disclosure: This column was not customized based on your online behavior, nor do we have a personal profile of you. Yet.

Be sure to read the next API article: DatumBox API Equips Third Party Apps with Machine Learning Capabilities