Data, Analytics & AI

Ask 10 different experts what artificial intelligence is, and you’ll get 10 different answers. A good definition comes from Demis Hassabis, CEO of DeepMind, an AI company that Google bought.Hassabis calls artificial intelligence the “science of making machines smart.” Today, we can teach machines to be like humans. We can give them the ability to see, hear, speak, write, and move.Your smartphone has tons of AI-powered capabilities. These include facial recognition that unlocks your phone with your face (AI that sees). They also include voice assistants (AI that hears and speaks in natural language). And, don’t forget, predictive text (AI that writes in natural language).Other types of artificial intelligence systems even give machines the ability to move using computer vision (AI that visually interprets the world), like you see in self-driving cars.Your favorite services, like Amazon and Netflix, use AI to offer product recommendations.And email clients like Gmail even use artificial intelligence to automatically write parts of emails for you.In fact, you probably use artificial intelligence every day, no matter where you work or what you do.”Machine learning” powers the most impressive capabilities in an AI platform. Machine learning is a type of AI that identifies patterns based on large sets of structured and unstructured data. The machine uses these patterns to make predictions. Then, it uses more and more data to improve those predictions over time.

The result?

Technology powered by machine learning models gets better over time, often without human involvement.And “deep learning” is the most advanced type of machine learning, where neural nets are structured to mimic the human brain.This is all very different from traditional software.A typical non-AI system, like your accounting software, relies on human inputs to work. The system is hard-coded with rules by people. Then, it follows those rules exactly to help you do your taxes. The system only improves if human programmers improve it.But tools powered by a machine learning algorithm (or many of them) can improve on their own. This improvement comes from a machine assessing its own performance and new data.For instance, an AI tool exists that writes email subject lines for you using natural language generation (NLG) and natural language processing (NLP). The tool’s AI model uses training from humans (samples of a company’s marketing copy) to learn and improve. But then the tool drafts its own email subject lines. Split-testing occurs, then the machine learns on its own what to improve based on the results. Over time, the machine gets better and better with little human involvement. This unlocks possibly unlimited performance potential.Now, imagine this power applied to any piece of marketing technology that uses data analytics. AI can actually make everything, from ads to analytics to content, more intelligent.

How Is AI Used in Analytics?

Before we get into AI use cases in analytics, let’s talk quickly about some confusion you might have when it comes to different terms related to AI and advanced analytics solutions.

First, you’ll often hear people talk about predictive analytics, or analytics where a machine is using historical data to forecast the future. AI powers many sophisticated predictive analytics solutions, so when you see the term, you might be looking at AI.

The key here lies in the nature of the solution’s predictive model. To be real AI, the predictive model needs to actually learn and improve from its predictions. So, ask about how the machine learns when looking at predictive analytics solutions.

You may also hear the term prescriptive analytics. Prescriptive analytics means a machine not only predicts based on data, but also prescribes what to do next. It recommends actions. As long as the machine is learning from its predictions and recommendations, it’s likely using AI.

However, when you hear a platform referred to as using “descriptive analytics,” it doesn’t necessarily mean it’s using AI. It might be. But it’s not always the case. Descriptive analytics just means the platform looks at historical data. If it doesn’t go further than that, it’s not using AI.

Here are just a few of the top use cases we’ve found for AI analytics today.

1. Find new actionable insights from your analytics.
Artificial intelligence excels at finding insights and patterns in large datasets that humans just can’t see. It also does this at scale and at speed.

Today, AI technology exists that will answer questions you ask about your website data analytics. (Think “Which channel had the highest conversion rate?”) An artificial intelligence analytics tool can also recommend actions based on opportunities its seeing in your analytics.

Some tools to check out here include:

Google Analytics’ Analytics Intelligence
Adobe Analytics
2. Use analytics to predict outcomes.
Systems exist that use AI analytics to help you predict outcomes and successful courses of action.

AI-powered systems can analyze data from hundreds of sources and offer predictions about what works and what doesn’t. It can also can deep dive into data analytics about your customers and offer predictions about consumer preferences, product development, and marketing channels.

Crayon
Helixa
3. Unify analytics and customer data.
Artificial intelligence is also used to unify data across platforms. That includes using the speed and scale of AI to pull together all your customer data into a single, unified view. Artificial intelligence is also capable of unifying data across different sources, even hard-to-track ones like call data.