Advances in artificial intelligence are set to dramatically impact market research in the short term. Specifically, narrow AI will enable the automation of individual research tasks. During this phase of Intelligence Augmentation AI will act to augment the capabilities of researches; enabling one person to achieve what previously would have taken an entire team.
The next phase of AI’s impact will happen as AI becomes increasingly generalized; extending automation out to functions which typically are not in the scope of a market researcher – such as content creation. The application of reinforcement learning based approaches to simultaneously solve market research & design challenges will signal the transition into this phase— and potentially the end of market research as we know it.
Reinforcement learning (RL) involves an intelligent agent which takes actions and learns from the outcome of those actions in order to become increasingly better at achieving a goal.
Formulating a problem into one which can be tackled by an RL Agent (RLA) requires defining three things:
1) What is the RL’s goal?
2) What actions can the RL take?
3) What does it observe in order to learn how its actions impact its goal?
Already, AI researchers have developed RLAs capable of playing video games which require complex strategy and planning at a superhuman level. In this case the RLA’s goal is to achieve the highest score possible, the actions it can take are virtually ‘pushing’ the game controller’s buttons and it observes the video game screen in order to learn how its actions impact the state of the game and ultimately the score.
To see how this approach can map to something at the intersection of market research and design, lets consider how an ad is created, tested, & then launched. Specifically, lets consider a simple ad like the ones you see in Google search results, and define the purpose of the ad to drive a person to click it and then sign up for some service.
In this case, we can define the RLA’s goal as maximizing the number of people who click the ad and sign up (given a set of constraints, like a budget). The actions it can take are to generate the content of the ad and make ad buys against various demographics. During this process it observes the click through rates & sign up rates of the various ads & targeting profiles.
At first, one can imagine it learning to generate ads that get a high rate of click through because it has learned that a certain set of words get people to click & that a person clicking is correlated with them signing up (which is the goal). With more time it may learn that while a certain set of ads generate strong click through rates, the rate of signups after clicking through varies greatly. As it then hones in on the ads that achieve both high click through & sign up rates, it may lean how those rates vary across demographics and optimize its targeting accordingly.
With this example we can see how an RLA could learn to produce ads that not only drive click throughs, but specifically those which are likely convert to a sign up, and then learn who to best target those ads at. From a financial perspective, the RLA would learn to continuously reduce the cost per sign up — a clearly quantifiable ROI .
While this seems ideal from an economic perspective, morally it does leave many open questions about how such an ROI optimizing RLA might impact society (more on that here).