Revolutionizing Market Research: AI Trends and Innovations 2024

TL;DR

Dialogue AI's new autonomous market research platform drastically cuts project timelines from weeks to days by automating study design, participant recruitment, and AI-moderated interviews. This innovation leverages reinforcement learning and offers deeper integrations and refined pricing, positioning it as a powerful tool for companies seeking rapid, actionable market insights.

Dialogue AI's Autonomous Market Research Platform

Dialogue AI is developing an autonomous market research platform designed to accelerate the speed of insights. The platform aims to reduce project timelines from 8–12 weeks to a day or two by automating study design, participant recruitment, AI-moderated interviews, and real-time synthesis. Lightspeed Venture Partners led their $6M Seed round.

Dialogue Co-founders

Image courtesy of Lightspeed Venture Partners

Dialogue's value proposition has attracted companies like Square, Wayfair, Nextdoor, and Suno. Use cases include ad copy testing, usability testing, concept testing, and post-purchase feedback. The platform supports AI-generated study design, automated participant recruitment, AI-moderated multilingual interviews across text, audio, and video, and real-time synthesis with auto-generated deliverables.

Key Differentiators of Dialogue AI

Dialogue AI highlights several key differentiators:

Live adaptive AI-moderated interviews: The platform understands tone and sentiment. More information here.
Refined pricing: Dialogue AI aims to lower costs relative to incumbents. Check pricing here.
Deeper integrations: Integration into business systems provides richer longitudinal context for respondents. Learn more about integrations.
Broader set of study formats: Includes real-time video and mobile research. Explore study formats.

The Role of Reinforcement Learning in Market Research

Reinforcement learning (RL) involves an agent that learns from the outcomes of its actions to achieve a goal. An RL agent (RLA) requires defining:

The RL’s goal.
The actions the RL can take.
What it observes to learn how its actions impact its goal.

RLAs have been developed to play video games at a superhuman level. The RLA’s goal is to achieve the highest score, the actions are pushing the game controller’s buttons, and it observes the video game screen.

Applying Reinforcement Learning to Ad Creation

Consider an ad creation scenario where the goal is to maximize the number of people who click the ad and sign up for a service. The RLA’s goal is maximizing sign-ups (within a budget). The actions are generating ad content and making ad buys. The RLA observes click-through rates and sign-up rates.

The RLA learns to generate ads that get a high rate of click through and then hones in on the ads that achieve both high click through & sign up rates, optimizing its targeting accordingly. The RLA would learn to continuously reduce the cost per sign up.

Fair Response and Online Sample Quality

Matt Gershner and John J. Talerico launched Fair Response to enhance online sample quality. According to Emmer Webb, the company aims to go back to the basics, focusing on the supply side of panel investment. The goal is to build a high-quality supply of research participants.

Guidance Group: Combining Traditional Research with AI

Raj Manoka and Jonathan Chavez launched Guidance Group, combining traditional research with AI and machine learning. The company aims to deliver rapid, actionable insights. They are fusing established methods with cutting edge technology.

NVIDIA's Acquisition of Gretel and Synthetic Data

NVIDIA acquired Gretel, a synthetic data firm, for AI training data. Synthetic data can alleviate privacy concerns. It is an appealing option for healthcare providers, banks, and government agencies.

Qualtrics AI Experience Agents

Qualtrics unveiled their AI Experience Agents at the Qualtrics X4 Summit. These agents leverage generative AI and automation to provide autonomous support. The AI agents aim to close the customer feedback loop fully.

SurveyMonkey Connect and NoCode Action Library

SurveyMonkey launched SurveyMonkey Connect and a NoCode Action Library for integration. An HR manager can feed employee feedback.

LLMs as Human Participant Replacements in Market Research

Research published in the INFORMS journal Marketing Science suggests large language models (LLMs) can replace human participants in some market research. The study found that LLMs achieve a 75%-85% agreement rate with human data sets. LLMs can accelerate market research programs while reducing costs and preserving accuracy.