We live in a world of acronyms—AI, ML, LLMs, NLG, ROFL (do people still use that one?). How do we parse through AI jargon when choosing a behavioral analytics tool? Let's first debunk some buzzwords to navigate through the noise and improve your customer experience with data based on a true AI foundation.
As a generation, we’ve watched enough dystopian TV to know what Artificial Intelligence is—it’s an umbrella term for different techniques to make machines more human-like.
I still haven’t warmed up to the whole "robots taking over the world" thing, but there are certainly fewer “scary” ways to think about AI and ML.
Deep Learning (DL)
Deep Learning stacks multiple neural-network layers to handle complex problems, like labeling every element in a mobile heat map or detecting subtle rage-click paths. It’s still machine learning under the hood, just with more horsepower for unstructured data such as video or audio.
Generative AI (genAI)
Rather than performing tasks, Generative AI uses data models to create new “computer manipulated” content that resembles human-generated content. Ever heard of deep fakes? An example would be a photo of Santa on the moon (we all know he is hard at work at the North Pole!) generated using GANs (Generative Adversarial Networks).
Large Language Model (LLM)
LLMs are text-focused deep-learning models trained on billions of words. They excel at summarizing feedback, drafting release notes, or answering “why are users dropping at step three?” questions in plain English, which si handy when you need quick takeaways from large raw datasets.
Think ChatGPT’s GPT-4o, Anthropic’s Claude 3, Google’s Gemini 1.5, or Meta’s Llama 3, LLMs that can digest mountains of text and surface clear answers in seconds.
Machine Learning (ML)
Machine Learning is a branch of AI that uses algorithms and statistical models to perform tasks. Think of ChatGPT—it works by engaging with an ML-powered model that uses its training data to generate responses and provide assistance based on what you type into it (like when you ask it, “How to cook rice?”). Too bad I didn’t have this in college.
By understanding the components of AI, you can better evaluate if a behavioral analytics tool is truly built on machine learning and genAI. True AI-backed products will save you time on tedious tasks, improve data capture, and summarize key insights. When considering how to use AI-powered behavioral analytics to enhance your customer experience, first consider these facets.
Autocapture
True autocapture is powered by AI. AI-powered autocapture refers to technology that automatically captures and records user interactions and behaviors on any digital platform without manual intervention. This process utilizes artificial intelligence algorithms to collect and analyze a wide range of AI-ready data, like clicks, scrolls, mouse movements, and other user activities. This makes the data human-readable without the need for tagging events and elements or hypothesizing data sets.
Within the platform, AI powers conversion analysis, journey mapping, and different types of heat mapping in behavioral analytics to highlight the most impactful areas of the user experience. All of this genAI data allows for real-time analysis and supports all your teams in identifying trends, personalizing customer interactions, and predicting customer needs.
AI can also predict customer behavior patterns and trends by analyzing historical data. This helps businesses anticipate customer needs, provide proactive customer service, and optimize offerings to meet customer expectations.
By understanding how a behavioral analytics tool is backed by AI, you can trust that the data equates to a truly personalized experience and a more engaging and relevant customer experience.
