AI in consumer insights
Navigating a new world of possibilities
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The story of technology and consumer insights are inextricably interwoven. AI was introduced years ago, and continues to shape the narrative. But as the plot thickens, we’d prefer not to wax poetically on distant “what-ifs,” or ride a string of buzzwords off into the sunset. Instead, we want to actively integrate the story of AI into the appropriate context—one written by research experts and insights seekers looking for understanding coupled with the consistency, security, and safety of their data.
Towards a more
tech-enabled future
AI was introduced into the world of market research decades ago,
but recent advancements promise to revolutionize the industry.
And while we’re excited about the possibilities these new technologies present, our approach requires rigorous testing in controlled environments, iterating on our hypotheses, and only bringing solutions to market that pass our rigorous development process. We’re continuously exploring, and experimentation is an integral part of our DNA. To that end, aytm remains committed to delivering quality products and experiences for insights seekers today while building a tech-enabled future for the curious of tomorrow.
Maintain control of your data
The potential of AI is undeniable, but so are the data privacy concerns. At aytm, we take consent and confidentiality seriously. We pledge to absolute transparency when it comes to how your data is or isn’t being used—empowering you to opt in or opt out of using any AI-powered features across the board.
Your data is never accessible to external parties
We employ AI algorithms in our platform, which we will go over in detail, but none of your data is saved by these algorithms, and our AI does not use client data to learn and improve in any way.
You are not automatically opted in to AI features
If you choose to use our AI-powered features, your data will remain compartmentalized and outputs precisely tailored to the needs, preferences, and prior history of your particular account.
Our AI partners are held
to highest standards
When it comes to choosing vendors and third parties to help deliver our AI solutions, the integrity, consistency, and security of your data is our primary concern. Our partners exceed the highest industry standards.
AI and the aytm platform
We want to go beyond words of assurance to clearly demonstrate the safety precautions built into our platform, so you confidently understand every step of your research journey. We’ve been using AI in our platform for years with certain tools and features that users may choose to deploy. Here are the ways you can use AI on the aytm platform right now.
Image response
This question type opens a qualitative window into consumer experience by letting respondents answer with an image. It uses AI image recognition technology to automatically identify texts and objects within images.
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Heatmaps
This question type helps you see through your consumers eyes by highlighting areas of importance in an image. It uses machine learning and neural networks to recognize and cluster images, words, and paragraphs included in concepts.
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data centrifuge
This is a data preparation and analysis tool that ensures high-quality, optimized data for the most impactful insights. Its algorithms leverage machine vision to search for and identify similar image responses.
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Sentiment analysis
This tool helps you understand how respondents feel about your products or services. It leverages Natural Language Processing (NLP) to classify the tone and emotion of open-ended responses.
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invisimark
This image watermark feature helps ensure the security of our clients’ proprietary content. It uses AI to apply invisible watermarks to content, and leverages SIFT (Scale-invariant feature transform) to detect them.
Learn moreThe different types of AI before they were buzzwords
Discussions at the forefront of any technology always bear the potential to blur lines between subjective marketing and objective reality. So we want to cut through the buzzwords and get to the origin of the terms that get tossed around when talking about AI.
Artificial Intelligence
Coined in the 50s by John McCarthy, this term began as a way to describe machines that can learn and reason like humans. This sets the stage for its use as a blanket term for a wide range of technologies that perform human tasks.
Machine learning
Believe it or not, this term also comes from the 50s and referred to self-teaching computers of the time. Since then it’s become a distinction among AI technologies that train algorithms on data to produce adaptable models.
Deep learning
This term was coined in the 80s and refers to a subset of machine learning. The word “deep” here refers to the fact that these technologies include layers and layers of network architecture that can be supervised, partially supervised, or unsupervised in their learning methods.
Neural Networks
This term has popped up several times over the past 80 years—first in the 40s, then in the 80s. Today, it describes a type of deep learning modeled after the human brain, with layers of forward-feeding nodes that enable computers to learn to execute tasks by analyzing training examples.
Natural language processing
This started in the late 40s with the goal of getting machines to translate languages automatically. Over the years focus has shifted from a rules-based approach to a statistical approach—driven by information extracted from the internet and consumer applications. Today, NLP powers search engines, speech recognition, and more.
Large Language Models
The origin of these models goes back decades, but in the 2010s, things started taking off when the field of LLMs began to intersect with the field of neural networks. LLMs are now trained on large neural networks that pull from massive amounts of unstructured data. They can be used in a wide range of NLP applications, including text generation via Generative Pre-trained Transformer (GPT) models.
Generative pre-trained transformers
Responsible for much of the recent discussion over AI, GPT are a subset of LLMs built on a transformer architecture. They can be used to generate text from a prompt by breaking it into tokens and then predicting the likelihood of each subsequent token.
